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<br/> <br/>
- [2024/07] We added support for running Microsoft's **GraphRAG** using local LLM on Intel GPU; see the quickstart guide [here](docs/mddocs/Quickstart/graphrag_quickstart.md). - [2024/07] We added support for running Microsoft's **GraphRAG** using local LLM on Intel GPU; see the quickstart guide [here](docs/mddocs/Quickstart/graphrag_quickstart.md).
- [2024/07] We added extensive support for Large Multimodal Models, including [StableDiffusion](https://github.com/jason-dai/ipex-llm/tree/main/python/llm/example/GPU/HuggingFace/Multimodal/StableDiffusion), [Phi-3-Vision](python/llm/example/GPU/HuggingFace/Multimodal/phi-3-vision), [Qwen-VL](python/llm/example/GPU/HuggingFace/Multimodal/qwen-vl), and [more](python/llm/example/GPU/HuggingFace/Multimodal). - [2024/07] We added extensive support for Large Multimodal Models, including [StableDiffusion](python/llm/example/GPU/HuggingFace/Multimodal/StableDiffusion), [Phi-3-Vision](python/llm/example/GPU/HuggingFace/Multimodal/phi-3-vision), [Qwen-VL](python/llm/example/GPU/HuggingFace/Multimodal/qwen-vl), and [more](python/llm/example/GPU/HuggingFace/Multimodal).
- [2024/07] We added **FP6** support on Intel [GPU](python/llm/example/GPU/HuggingFace/More-Data-Types). - [2024/07] We added **FP6** support on Intel [GPU](python/llm/example/GPU/HuggingFace/More-Data-Types).
- [2024/06] We added experimental **NPU** support for Intel Core Ultra processors; see the examples [here](python/llm/example/NPU/HF-Transformers-AutoModels). - [2024/06] We added experimental **NPU** support for Intel Core Ultra processors; see the examples [here](python/llm/example/NPU/HF-Transformers-AutoModels).
- [2024/06] We added extensive support of **pipeline parallel** [inference](python/llm/example/GPU/Pipeline-Parallel-Inference), which makes it easy to run large-sized LLM using 2 or more Intel GPUs (such as Arc). - [2024/06] We added extensive support of **pipeline parallel** [inference](python/llm/example/GPU/Pipeline-Parallel-Inference), which makes it easy to run large-sized LLM using 2 or more Intel GPUs (such as Arc).
@ -180,15 +180,8 @@ Please see the **Perplexity** result below (tested on Wikitext dataset using the
## `ipex-llm` Quickstart ## `ipex-llm` Quickstart
### Docker
- [GPU Inference in C++](docs/mddocs/DockerGuides/docker_cpp_xpu_quickstart.md): running `llama.cpp`, `ollama`, etc., with `ipex-llm` on Intel GPU
- [GPU Inference in Python](docs/mddocs/DockerGuides/docker_pytorch_inference_gpu.md) : running HuggingFace `transformers`, `LangChain`, `LlamaIndex`, `ModelScope`, etc. with `ipex-llm` on Intel GPU
- [vLLM on GPU](docs/mddocs/DockerGuides/vllm_docker_quickstart.md): running `vLLM` serving with `ipex-llm` on Intel GPU
- [vLLM on CPU](docs/mddocs/DockerGuides/vllm_cpu_docker_quickstart.md): running `vLLM` serving with `ipex-llm` on Intel CPU
- [FastChat on GPU](docs/mddocs/DockerGuides/fastchat_docker_quickstart.md): running `FastChat` serving with `ipex-llm` on Intel GPU
- [VSCode on GPU](docs/mddocs/DockerGuides/docker_run_pytorch_inference_in_vscode.md): running and developing `ipex-llm` applications in Python using VSCode on Intel GPU
### Use ### Use
- [Arc B580](docs/mddocs/Quickstart/bmg_quickstart.md): running `ipex-llm` on Intel Arc **B580** GPU for Ollama, llama.cpp, PyTorch, HuggingFace, etc.
- [NPU](docs/mddocs/Quickstart/npu_quickstart.md): running `ipex-llm` on Intel **NPU** in both Python and C++ - [NPU](docs/mddocs/Quickstart/npu_quickstart.md): running `ipex-llm` on Intel **NPU** in both Python and C++
- [llama.cpp](docs/mddocs/Quickstart/llama_cpp_quickstart.md): running **llama.cpp** (*using C++ interface of `ipex-llm`*) on Intel GPU - [llama.cpp](docs/mddocs/Quickstart/llama_cpp_quickstart.md): running **llama.cpp** (*using C++ interface of `ipex-llm`*) on Intel GPU
- [Ollama](docs/mddocs/Quickstart/ollama_quickstart.md): running **ollama** (*using C++ interface of `ipex-llm`*) on Intel GPU - [Ollama](docs/mddocs/Quickstart/ollama_quickstart.md): running **ollama** (*using C++ interface of `ipex-llm`*) on Intel GPU
@ -200,6 +193,14 @@ Please see the **Perplexity** result below (tested on Wikitext dataset using the
- [Axolotl](docs/mddocs/Quickstart/axolotl_quickstart.md): running `ipex-llm` in **Axolotl** for LLM finetuning - [Axolotl](docs/mddocs/Quickstart/axolotl_quickstart.md): running `ipex-llm` in **Axolotl** for LLM finetuning
- [Benchmarking](docs/mddocs/Quickstart/benchmark_quickstart.md): running (latency and throughput) **benchmarks** for `ipex-llm` on Intel CPU and GPU - [Benchmarking](docs/mddocs/Quickstart/benchmark_quickstart.md): running (latency and throughput) **benchmarks** for `ipex-llm` on Intel CPU and GPU
### Docker
- [GPU Inference in C++](docs/mddocs/DockerGuides/docker_cpp_xpu_quickstart.md): running `llama.cpp`, `ollama`, etc., with `ipex-llm` on Intel GPU
- [GPU Inference in Python](docs/mddocs/DockerGuides/docker_pytorch_inference_gpu.md) : running HuggingFace `transformers`, `LangChain`, `LlamaIndex`, `ModelScope`, etc. with `ipex-llm` on Intel GPU
- [vLLM on GPU](docs/mddocs/DockerGuides/vllm_docker_quickstart.md): running `vLLM` serving with `ipex-llm` on Intel GPU
- [vLLM on CPU](docs/mddocs/DockerGuides/vllm_cpu_docker_quickstart.md): running `vLLM` serving with `ipex-llm` on Intel CPU
- [FastChat on GPU](docs/mddocs/DockerGuides/fastchat_docker_quickstart.md): running `FastChat` serving with `ipex-llm` on Intel GPU
- [VSCode on GPU](docs/mddocs/DockerGuides/docker_run_pytorch_inference_in_vscode.md): running and developing `ipex-llm` applications in Python using VSCode on Intel GPU
### Applications ### Applications
- [GraphRAG](docs/mddocs/Quickstart/graphrag_quickstart.md): running Microsoft's `GraphRAG` using local LLM with `ipex-llm` - [GraphRAG](docs/mddocs/Quickstart/graphrag_quickstart.md): running Microsoft's `GraphRAG` using local LLM with `ipex-llm`
- [RAGFlow](docs/mddocs/Quickstart/ragflow_quickstart.md): running `RAGFlow` (*an open-source RAG engine*) with `ipex-llm` - [RAGFlow](docs/mddocs/Quickstart/ragflow_quickstart.md): running `RAGFlow` (*an open-source RAG engine*) with `ipex-llm`

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<br/> <br/>
- [2024/07] 新增 Microsoft **GraphRAG** 的支持（使用运行在本地 Intel GPU 上的 LLM），详情参考[快速入门指南](docs/mddocs/Quickstart/graphrag_quickstart.md)。 - [2024/07] 新增 Microsoft **GraphRAG** 的支持（使用运行在本地 Intel GPU 上的 LLM），详情参考[快速入门指南](docs/mddocs/Quickstart/graphrag_quickstart.md)。
- [2024/07] 全面增强了对多模态大模型的支持，包括 [StableDiffusion](https://github.com/jason-dai/ipex-llm/tree/main/python/llm/example/GPU/HuggingFace/Multimodal/StableDiffusion), [Phi-3-Vision](python/llm/example/GPU/HuggingFace/Multimodal/phi-3-vision), [Qwen-VL](python/llm/example/GPU/HuggingFace/Multimodal/qwen-vl)，更多详情请点击[这里](python/llm/example/GPU/HuggingFace/Multimodal)。 - [2024/07] 全面增强了对多模态大模型的支持，包括 [StableDiffusion](python/llm/example/GPU/HuggingFace/Multimodal/StableDiffusion), [Phi-3-Vision](python/llm/example/GPU/HuggingFace/Multimodal/phi-3-vision), [Qwen-VL](python/llm/example/GPU/HuggingFace/Multimodal/qwen-vl)，更多详情请点击[这里](python/llm/example/GPU/HuggingFace/Multimodal)。
- [2024/07] 新增 Intel GPU 上 **FP6** 的支持，详情参考[更多数据类型样例](python/llm/example/GPU/HuggingFace/More-Data-Types)。 - [2024/07] 新增 Intel GPU 上 **FP6** 的支持，详情参考[更多数据类型样例](python/llm/example/GPU/HuggingFace/More-Data-Types)。
- [2024/06] 新增对 Intel Core Ultra 处理器中 **NPU** 的实验性支持，详情参考[相关示例](python/llm/example/NPU/HF-Transformers-AutoModels)。 - [2024/06] 新增对 Intel Core Ultra 处理器中 **NPU** 的实验性支持，详情参考[相关示例](python/llm/example/NPU/HF-Transformers-AutoModels)。
- [2024/06] 增加了对[流水线并行推理](python/llm/example/GPU/Pipeline-Parallel-Inference)的全面支持，使得用两块或更多 Intel GPU（如 Arc）上运行 LLM 变得更容易。 - [2024/06] 增加了对[流水线并行推理](python/llm/example/GPU/Pipeline-Parallel-Inference)的全面支持，使得用两块或更多 Intel GPU（如 Arc）上运行 LLM 变得更容易。
@ -180,15 +180,8 @@ See the demo of running [*Text-Generation-WebUI*](https://ipex-llm.readthedocs.i
## `ipex-llm` 快速入门 ## `ipex-llm` 快速入门
### Docker
- [GPU Inference in C++](docs/mddocs/DockerGuides/docker_cpp_xpu_quickstart.md): 在 Intel GPU 上使用 `ipex-llm` 运行 `llama.cpp`, `ollama`等
- [GPU Inference in Python](docs/mddocs/DockerGuides/docker_pytorch_inference_gpu.md) : 在 Intel GPU 上使用 `ipex-llm` 运行 HuggingFace `transformers`, `LangChain`, `LlamaIndex`, `ModelScope`，等
- [vLLM on GPU](docs/mddocs/DockerGuides/vllm_docker_quickstart.md): 在 Intel GPU 上使用 `ipex-llm` 运行 `vLLM` 推理服务
- [vLLM on CPU](docs/mddocs/DockerGuides/vllm_cpu_docker_quickstart.md): 在 Intel CPU 上使用 `ipex-llm` 运行 `vLLM` 推理服务
- [FastChat on GPU](docs/mddocs/DockerGuides/fastchat_docker_quickstart.md): 在 Intel GPU 上使用 `ipex-llm` 运行 `FastChat` 推理服务
- [VSCode on GPU](docs/mddocs/DockerGuides/docker_run_pytorch_inference_in_vscode.md): 在 Intel GPU 上使用 VSCode 开发并运行基于 Python 的 `ipex-llm` 应用
### 使用 ### 使用
- [Arc B580](docs/mddocs/Quickstart/bmg_quickstart.md): 在 Intel Arc **B580** GPU 上运行 `ipex-llm`（包括 Ollama, llama.cpp, PyTorch, HuggingFace 等）
- [NPU](docs/mddocs/Quickstart/npu_quickstart.md): 在 Intel **NPU** 上运行 `ipex-llm`（支持 Python 和 C++） - [NPU](docs/mddocs/Quickstart/npu_quickstart.md): 在 Intel **NPU** 上运行 `ipex-llm`（支持 Python 和 C++）
- [llama.cpp](docs/mddocs/Quickstart/llama_cpp_quickstart.zh-CN.md): 在 Intel GPU 上运行 **llama.cpp** (*使用 `ipex-llm` 的 C++ 接口*) - [llama.cpp](docs/mddocs/Quickstart/llama_cpp_quickstart.zh-CN.md): 在 Intel GPU 上运行 **llama.cpp** (*使用 `ipex-llm` 的 C++ 接口*)
- [Ollama](docs/mddocs/Quickstart/ollama_quickstart.zh-CN.md): 在 Intel GPU 上运行 **ollama** (*使用 `ipex-llm` 的 C++ 接口*) - [Ollama](docs/mddocs/Quickstart/ollama_quickstart.zh-CN.md): 在 Intel GPU 上运行 **ollama** (*使用 `ipex-llm` 的 C++ 接口*)
@ -200,6 +193,14 @@ See the demo of running [*Text-Generation-WebUI*](https://ipex-llm.readthedocs.i
- [Axolotl](docs/mddocs/Quickstart/axolotl_quickstart.md): 使用 **Axolotl** 和 `ipex-llm` 进行 LLM 微调 - [Axolotl](docs/mddocs/Quickstart/axolotl_quickstart.md): 使用 **Axolotl** 和 `ipex-llm` 进行 LLM 微调
- [Benchmarking](docs/mddocs/Quickstart/benchmark_quickstart.md): 在 Intel GPU 和 CPU 上运行**性能基准测试**（延迟和吞吐量） - [Benchmarking](docs/mddocs/Quickstart/benchmark_quickstart.md): 在 Intel GPU 和 CPU 上运行**性能基准测试**（延迟和吞吐量）
### Docker
- [GPU Inference in C++](docs/mddocs/DockerGuides/docker_cpp_xpu_quickstart.md): 在 Intel GPU 上使用 `ipex-llm` 运行 `llama.cpp`, `ollama`等
- [GPU Inference in Python](docs/mddocs/DockerGuides/docker_pytorch_inference_gpu.md) : 在 Intel GPU 上使用 `ipex-llm` 运行 HuggingFace `transformers`, `LangChain`, `LlamaIndex`, `ModelScope`，等
- [vLLM on GPU](docs/mddocs/DockerGuides/vllm_docker_quickstart.md): 在 Intel GPU 上使用 `ipex-llm` 运行 `vLLM` 推理服务
- [vLLM on CPU](docs/mddocs/DockerGuides/vllm_cpu_docker_quickstart.md): 在 Intel CPU 上使用 `ipex-llm` 运行 `vLLM` 推理服务
- [FastChat on GPU](docs/mddocs/DockerGuides/fastchat_docker_quickstart.md): 在 Intel GPU 上使用 `ipex-llm` 运行 `FastChat` 推理服务
- [VSCode on GPU](docs/mddocs/DockerGuides/docker_run_pytorch_inference_in_vscode.md): 在 Intel GPU 上使用 VSCode 开发并运行基于 Python 的 `ipex-llm` 应用
### 应用 ### 应用
- [GraphRAG](docs/mddocs/Quickstart/graphrag_quickstart.md): 基于 `ipex-llm` 使用本地 LLM 运行 Microsoft 的 `GraphRAG` - [GraphRAG](docs/mddocs/Quickstart/graphrag_quickstart.md): 基于 `ipex-llm` 使用本地 LLM 运行 Microsoft 的 `GraphRAG`
- [RAGFlow](docs/mddocs/Quickstart/ragflow_quickstart.md): 基于 `ipex-llm` 运行 `RAGFlow` (*一个开源的 RAG 引擎*) - [RAGFlow](docs/mddocs/Quickstart/ragflow_quickstart.md): 基于 `ipex-llm` 运行 `RAGFlow` (*一个开源的 RAG 引擎*)

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# IPEX-LLM Documentation # 💫 Intel® LLM Library for PyTorch*
<p>
<b>< English</b> | <a href='./README.zh-CN.md'>中文</a> >
</p>
## Table of Contents **`IPEX-LLM`** is an LLM acceleration library for Intel [GPU](Quickstart/install_windows_gpu.md) *(e.g., local PC with iGPU, discrete GPU such as Arc, Flex and Max)*, [NPU](Quickstart/npu_quickstart.md) and CPU [^1].
- [LLM in 5 minutes](./Overview/llm.md) ## Latest Update 🔥
- [Installation](./Overview/install.md) - [2025/01] We added the guide for running `ipex-llm` on Intel Arc [B580](Quickstart/bmg_quickstart.md) GPU
- [CPU](./Overview/install_cpu.md) - [2024/12] We added support for running [Ollama 0.4.6](Quickstart/ollama_quickstart.md) on Intel GPU.
- [GPU](./Overview/install_gpu.md) - [2024/12] We added both ***Python*** and ***C++*** support for Intel Core Ultra [NPU](Quickstart/npu_quickstart.md) (including 100H, 200V and 200K series).
- [Docker Guides](./DockerGuides/) - [2024/11] We added support for running [vLLM 0.6.2](DockerGuides/vllm_docker_quickstart.md) on Intel Arc GPUs.
- [Overview of IPEX-LLM Containers for Intel GPU](./DockerGuides/docker_windows_gpu.md)
- [Python Inference using IPEX-LLM on Intel GPU](./DockerGuides/docker_pytorch_inference_gpu.md) <details><summary>More updates</summary>
- [Run/Develop PyTorch in VSCode with Docker on Intel GPU](./DockerGuides/docker_run_pytorch_inference_in_vscode.md) <br/>
- [Run llama.cpp/Ollama/Open-WebUI on an Intel GPU via Docker](./DockerGuides/docker_cpp_xpu_quickstart.md)
- [FastChat Serving with IPEX-LLM on Intel GPUs via docker](./DockerGuides/fastchat_docker_quickstart.md) - [2024/07] We added support for running Microsoft's **GraphRAG** using local LLM on Intel GPU; see the quickstart guide [here](Quickstart/graphrag_quickstart.md).
- [vLLM Serving with IPEX-LLM on Intel GPUs via Docker](./DockerGuides/vllm_docker_quickstart.md) - [2024/07] We added extensive support for Large Multimodal Models, including [StableDiffusion](../../python/llm/example/GPU/HuggingFace/Multimodal/StableDiffusion), [Phi-3-Vision](../../python/llm/example/GPU/HuggingFace/Multimodal/phi-3-vision), [Qwen-VL](../../python/llm/example/GPU/HuggingFace/Multimodal/qwen-vl), and [more](../../python/llm/example/GPU/HuggingFace/Multimodal).
- [vLLM Serving with IPEX-LLM on Intel CPU via Docker](./DockerGuides/vllm_cpu_docker_quickstart.md) - [2024/07] We added **FP6** support on Intel [GPU](../../python/llm/example/GPU/HuggingFace/More-Data-Types).
- [Quickstart](https://github.com/intel-analytics/ipex-llm/tree/main/docs/mddocs/Quickstart/) - [2024/06] We added experimental **NPU** support for Intel Core Ultra processors; see the examples [here](../../python/llm/example/NPU/HF-Transformers-AutoModels).
- [`bigdl-llm` Migration Guide](./Quickstart/bigdl_llm_migration.md) - [2024/06] We added extensive support of **pipeline parallel** [inference](../../python/llm/example/GPU/Pipeline-Parallel-Inference), which makes it easy to run large-sized LLM using 2 or more Intel GPUs (such as Arc).
- [Install IPEX-LLM on Linux with Intel GPU](./Quickstart/install_linux_gpu.md) - [2024/06] We added support for running **RAGFlow** with `ipex-llm` on Intel [GPU](Quickstart/ragflow_quickstart.md).
- [Install IPEX-LLM on Windows with Intel GPU](./Quickstart/install_windows_gpu.md) - [2024/05] `ipex-llm` now supports **Axolotl** for LLM finetuning on Intel GPU; see the quickstart [here](Quickstart/axolotl_quickstart.md).
- [Run IPEX-LLM on Intel NPU](./Quickstart/npu_quickstart.md) - [2024/05] You can now easily run `ipex-llm` inference, serving and finetuning using the **Docker** [images](#docker).
- [Run Local RAG using Langchain-Chatchat on Intel CPU and GPU](./Quickstart/chatchat_quickstart.md) - [2024/05] You can now install `ipex-llm` on Windows using just "*[one command](Quickstart/install_windows_gpu.md#install-ipex-llm)*".
- [Run Text Generation WebUI on Intel GPU](./Quickstart/webui_quickstart.md) - [2024/04] You can now run **Open WebUI** on Intel GPU using `ipex-llm`; see the quickstart [here](Quickstart/open_webui_with_ollama_quickstart.md).
- [Run Open WebUI with Intel GPU](./Quickstart/open_webui_with_ollama_quickstart.md) - [2024/04] You can now run **Llama 3** on Intel GPU using `llama.cpp` and `ollama` with `ipex-llm`; see the quickstart [here](Quickstart/llama3_llamacpp_ollama_quickstart.md).
- [Run PrivateGPT with IPEX-LLM on Intel GPU](./Quickstart/privateGPT_quickstart.md) - [2024/04] `ipex-llm` now supports **Llama 3** on both Intel [GPU](../../python/llm/example/GPU/HuggingFace/LLM/llama3) and [CPU](../../python/llm/example/CPU/HF-Transformers-AutoModels/Model/llama3).
- [Run Coding Copilot in VSCode with Intel GPU](./Quickstart/continue_quickstart.md) - [2024/04] `ipex-llm` now provides C++ interface, which can be used as an accelerated backend for running [llama.cpp](Quickstart/llama_cpp_quickstart.md) and [ollama](Quickstart/ollama_quickstart.md) on Intel GPU.
- [Run Dify on Intel GPU](./Quickstart/dify_quickstart.md) - [2024/03] `bigdl-llm` has now become `ipex-llm` (see the migration guide [here](Quickstart/bigdl_llm_migration.md)); you may find the original `BigDL` project [here](https://github.com/intel-analytics/bigdl-2.x).
- [Run Performance Benchmarking with IPEX-LLM](./Quickstart/benchmark_quickstart.md) - [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)).
- [Run llama.cpp with IPEX-LLM on Intel GPU](./Quickstart/llama_cpp_quickstart.md) - [2024/02] `ipex-llm` added initial **INT2** support (based on llama.cpp [IQ2](../../python/llm/example/GPU/HuggingFace/Advanced-Quantizations/GGUF-IQ2) mechanism), which makes it possible to run large-sized LLM (e.g., Mixtral-8x7B) on Intel GPU with 16GB VRAM.
- [Run Ollama with IPEX-LLM on Intel GPU](./Quickstart/ollama_quickstart.md) - [2024/02] Users can now use `ipex-llm` through [Text-Generation-WebUI](https://github.com/intel-analytics/text-generation-webui) GUI.
- [Run Llama 3 on Intel GPU using llama.cpp and ollama with IPEX-LLM](./Quickstart/llama3_llamacpp_ollama_quickstart.md) - [2024/02] `ipex-llm` now supports *[Self-Speculative Decoding](Inference/Self_Speculative_Decoding.md)*, 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.
- [Serving using IPEX-LLM and FastChat](./Quickstart/fastchat_quickstart.md) - [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)).
- [Serving using IPEX-LLM and vLLM on Intel GPU](./Quickstart/vLLM_quickstart.md) - [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)).
- [Finetune LLM with Axolotl on Intel GPU](./Quickstart/axolotl_quickstart.md) - [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)*).
- [Run IPEX-LLM serving on Multiple Intel GPUs using DeepSpeed AutoTP and FastApi](./Quickstart/deepspeed_autotp_fastapi_quickstart.md) - [2023/12] `ipex-llm` now supports [Mixtral-8x7B](../../python/llm/example/GPU/HuggingFace/LLM/mixtral) on both Intel [GPU](../../python/llm/example/GPU/HuggingFace/LLM/mixtral) and [CPU](../../python/llm/example/CPU/HF-Transformers-AutoModels/Model/mixtral).
- [Run RAGFlow with IPEX-LLM on Intel GPU](./Quickstart/ragflow_quickstart.md) - [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)*).
- [Run GraphRAG with IPEX-LLM on Intel GPU](./Quickstart/graphrag_quickstart.md) - [2023/12] `ipex-llm` now supports [FP8 and FP4 inference](../../python/llm/example/GPU/HuggingFace/More-Data-Types) on Intel ***GPU***.
- [Key Features](./Overview/KeyFeatures/) - [2023/11] Initial support for directly loading [GGUF](../../python/llm/example/GPU/HuggingFace/Advanced-Quantizations/GGUF), [AWQ](../../python/llm/example/GPU/HuggingFace/Advanced-Quantizations/AWQ) and [GPTQ](../../python/llm/example/GPU/HuggingFace/Advanced-Quantizations/GPTQ) models into `ipex-llm` is available.
- [PyTorch API](./Overview/KeyFeatures/optimize_model.md) - [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).
- [`transformers`-style API](./Overview/KeyFeatures/hugging_face_format.md) - [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).
- [GPU Supports](./Overview/KeyFeatures/gpu_supports.md) - [2023/10] `ipex-llm` now supports [FastChat serving](../../python/llm/src/ipex_llm/llm/serving) on on both Intel CPU and GPU.
- [Inference on GPU](./Overview/KeyFeatures/inference_on_gpu.md) - [2023/09] `ipex-llm` now supports [Intel GPU](../../python/llm/example/GPU) (including iGPU, Arc, Flex and MAX).
- [Finetune (QLoRA)](./Overview/KeyFeatures/finetune.md) - [2023/09] `ipex-llm` [tutorial](https://github.com/intel-analytics/ipex-llm-tutorial) is released.
- [Multi Intel GPUs selection](./Overview/KeyFeatures/multi_gpus_selection.md)
- [Examples](../../python/llm/example/) </details>
- [CPU](../../python/llm/example/CPU/)
- [GPU](../../python/llm/example/GPU/) ## `ipex-llm` Quickstart
- [API Reference](./PythonAPI/)
- [IPEX-LLM PyTorch API](./PythonAPI/optimize.md) ### Use
- [IPEX-LLM `transformers`-style API](./PythonAPI/transformers.md) - [Arc B580](Quickstart/bmg_quickstart.md): running `ipex-llm` on Intel Arc **B580** GPU for Ollama, llama.cpp, PyTorch, HuggingFace, etc.
- [FQA](./Overview/FAQ/faq.md) - [NPU](Quickstart/npu_quickstart.md): running `ipex-llm` on Intel **NPU** in both Python and C++
- [llama.cpp](Quickstart/llama_cpp_quickstart.md): running **llama.cpp** (*using C++ interface of `ipex-llm`*) on Intel GPU
- [Ollama](Quickstart/ollama_quickstart.md): running **ollama** (*using C++ interface of `ipex-llm`*) on Intel GPU
- [PyTorch/HuggingFace](Quickstart/install_windows_gpu.md): running **PyTorch**, **HuggingFace**, **LangChain**, **LlamaIndex**, etc. (*using Python interface of `ipex-llm`*) on Intel GPU for [Windows](Quickstart/install_windows_gpu.md) and [Linux](Quickstart/install_linux_gpu.md)
- [vLLM](Quickstart/vLLM_quickstart.md): running `ipex-llm` in **vLLM** on both Intel [GPU](DockerGuides/vllm_docker_quickstart.md) and [CPU](DockerGuides/vllm_cpu_docker_quickstart.md)
- [FastChat](Quickstart/fastchat_quickstart.md): running `ipex-llm` in **FastChat** serving on on both Intel GPU and CPU
- [Serving on multiple Intel GPUs](Quickstart/deepspeed_autotp_fastapi_quickstart.md): running `ipex-llm` **serving on multiple Intel GPUs** by leveraging DeepSpeed AutoTP and FastAPI
- [Text-Generation-WebUI](Quickstart/webui_quickstart.md): running `ipex-llm` in `oobabooga` **WebUI**
- [Axolotl](Quickstart/axolotl_quickstart.md): running `ipex-llm` in **Axolotl** for LLM finetuning
- [Benchmarking](Quickstart/benchmark_quickstart.md): running (latency and throughput) **benchmarks** for `ipex-llm` on Intel CPU and GPU
### Docker
- [GPU Inference in C++](DockerGuides/docker_cpp_xpu_quickstart.md): running `llama.cpp`, `ollama`, etc., with `ipex-llm` on Intel GPU
- [GPU Inference in Python](DockerGuides/docker_pytorch_inference_gpu.md) : running HuggingFace `transformers`, `LangChain`, `LlamaIndex`, `ModelScope`, etc. with `ipex-llm` on Intel GPU
- [vLLM on GPU](DockerGuides/vllm_docker_quickstart.md): running `vLLM` serving with `ipex-llm` on Intel GPU
- [vLLM on CPU](DockerGuides/vllm_cpu_docker_quickstart.md): running `vLLM` serving with `ipex-llm` on Intel CPU
- [FastChat on GPU](DockerGuides/fastchat_docker_quickstart.md): running `FastChat` serving with `ipex-llm` on Intel GPU
- [VSCode on GPU](DockerGuides/docker_run_pytorch_inference_in_vscode.md): running and developing `ipex-llm` applications in Python using VSCode on Intel GPU
### Applications
- [GraphRAG](Quickstart/graphrag_quickstart.md): running Microsoft's `GraphRAG` using local LLM with `ipex-llm`
- [RAGFlow](Quickstart/ragflow_quickstart.md): running `RAGFlow` (*an open-source RAG engine*) with `ipex-llm`
- [LangChain-Chatchat](Quickstart/chatchat_quickstart.md): running `LangChain-Chatchat` (*Knowledge Base QA using RAG pipeline*) with `ipex-llm`
- [Coding copilot](Quickstart/continue_quickstart.md): running `Continue` (coding copilot in VSCode) with `ipex-llm`
- [Open WebUI](Quickstart/open_webui_with_ollama_quickstart.md): running `Open WebUI` with `ipex-llm`
- [PrivateGPT](Quickstart/privateGPT_quickstart.md): running `PrivateGPT` to interact with documents with `ipex-llm`
- [Dify platform](Quickstart/dify_quickstart.md): running `ipex-llm` in `Dify`(*production-ready LLM app development platform*)
### Install
- [Windows GPU](Quickstart/install_windows_gpu.md): installing `ipex-llm` on Windows with Intel GPU
- [Linux GPU](Quickstart/install_linux_gpu.md): installing `ipex-llm` on Linux with Intel GPU
- *For more details, please refer to the [full installation guide](Overview/install.md)*
### Code Examples
- #### Low bit inference
- [INT4 inference](../../python/llm/example/GPU/HuggingFace/LLM): **INT4** LLM inference on Intel [GPU](../../python/llm/example/GPU/HuggingFace/LLM) and [CPU](../../python/llm/example/CPU/HF-Transformers-AutoModels/Model)
- [FP8/FP6/FP4 inference](../../python/llm/example/GPU/HuggingFace/More-Data-Types): **FP8**, **FP6** and **FP4** LLM inference on Intel [GPU](../../python/llm/example/GPU/HuggingFace/More-Data-Types)
- [INT8 inference](../../python/llm/example/GPU/HuggingFace/More-Data-Types): **INT8** LLM inference on Intel [GPU](../../python/llm/example/GPU/HuggingFace/More-Data-Types) and [CPU](../../python/llm/example/CPU/HF-Transformers-AutoModels/More-Data-Types)
- [INT2 inference](../../python/llm/example/GPU/HuggingFace/Advanced-Quantizations/GGUF-IQ2): **INT2** LLM inference (based on llama.cpp IQ2 mechanism) on Intel [GPU](../../python/llm/example/GPU/HuggingFace/Advanced-Quantizations/GGUF-IQ2)
- #### FP16/BF16 inference
- **FP16** LLM inference on Intel [GPU](../../python/llm/example/GPU/Speculative-Decoding), with possible [self-speculative decoding](Inference/Self_Speculative_Decoding.md) optimization
- **BF16** LLM inference on Intel [CPU](../../python/llm/example/CPU/Speculative-Decoding), with possible [self-speculative decoding](Inference/Self_Speculative_Decoding.md) optimization
- #### Distributed inference
- **Pipeline Parallel** inference on Intel [GPU](../../python/llm/example/GPU/Pipeline-Parallel-Inference)
- **DeepSpeed AutoTP** inference on Intel [GPU](../../python/llm/example/GPU/Deepspeed-AutoTP)
- #### Save and load
- [Low-bit models](../../python/llm/example/CPU/HF-Transformers-AutoModels/Save-Load): saving and loading `ipex-llm` low-bit models (INT4/FP4/FP6/INT8/FP8/FP16/etc.)
- [GGUF](../../python/llm/example/GPU/HuggingFace/Advanced-Quantizations/GGUF): directly loading GGUF models into `ipex-llm`
- [AWQ](../../python/llm/example/GPU/HuggingFace/Advanced-Quantizations/AWQ): directly loading AWQ models into `ipex-llm`
- [GPTQ](../../python/llm/example/GPU/HuggingFace/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/HuggingFace)
- [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](Quickstart/axolotl_quickstart.md)
- [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)
## API Doc
- [HuggingFace Transformers-style API (Auto Classes)](PythonAPI/transformers.md)
- [API for arbitrary PyTorch Model](https://github.com/intel-analytics/ipex-llm/blob/main/PythonAPI/optimize.md)
## FAQ
- [FAQ & Trouble Shooting](Overview/FAQ/faq.md)
## Verified Models
Over 70 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 | NPU Example |
|------------|----------------------------------------------|----------------------------------------------|----------------------------------------------|
| LLaMA | [link1](../../python/llm/example/CPU/Native-Models), [link2](../../python/llm/example/CPU/HF-Transformers-AutoModels/Model/vicuna) |[link](../../python/llm/example/GPU/HuggingFace/LLM/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/HuggingFace/LLM/llama2) | [Python link](../../python/llm/example/NPU/HF-Transformers-AutoModels/LLM), [C++ link](../../python/llm/example/NPU/HF-Transformers-AutoModels/LLM/CPP_Examples) |
| LLaMA 3 | [link](../../python/llm/example/CPU/HF-Transformers-AutoModels/Model/llama3) | [link](../../python/llm/example/GPU/HuggingFace/LLM/llama3) | [Python link](../../python/llm/example/NPU/HF-Transformers-AutoModels/LLM), [C++ link](../../python/llm/example/NPU/HF-Transformers-AutoModels/LLM/CPP_Examples) |
| LLaMA 3.1 | [link](../../python/llm/example/CPU/HF-Transformers-AutoModels/Model/llama3.1) | [link](../../python/llm/example/GPU/HuggingFace/LLM/llama3.1) |
| LLaMA 3.2 | | [link](../../python/llm/example/GPU/HuggingFace/LLM/llama3.2) | [Python link](../../python/llm/example/NPU/HF-Transformers-AutoModels/LLM), [C++ link](../../python/llm/example/NPU/HF-Transformers-AutoModels/LLM/CPP_Examples) |
| LLaMA 3.2-Vision | | [link](../../python/llm/example/GPU/PyTorch-Models/Model/llama3.2-vision/) |
| 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/HuggingFace/LLM/chatglm2) |
| ChatGLM3 | [link](../../python/llm/example/CPU/HF-Transformers-AutoModels/Model/chatglm3) | [link](../../python/llm/example/GPU/HuggingFace/LLM/chatglm3) |
| GLM-4 | [link](../../python/llm/example/CPU/HF-Transformers-AutoModels/Model/glm4) | [link](../../python/llm/example/GPU/HuggingFace/LLM/glm4) |
| GLM-4V | [link](../../python/llm/example/CPU/HF-Transformers-AutoModels/Model/glm-4v) | [link](../../python/llm/example/GPU/HuggingFace/Multimodal/glm-4v) |
| GLM-Edge | | [link](../../python/llm/example/GPU/HuggingFace/LLM/glm-edge) | [Python link](../../python/llm/example/NPU/HF-Transformers-AutoModels/LLM) |
| GLM-Edge-V | | [link](../../python/llm/example/GPU/HuggingFace/Multimodal/glm-edge-v) |
| Mistral | [link](../../python/llm/example/CPU/HF-Transformers-AutoModels/Model/mistral) | [link](../../python/llm/example/GPU/HuggingFace/LLM/mistral) |
| Mixtral | [link](../../python/llm/example/CPU/HF-Transformers-AutoModels/Model/mixtral) | [link](../../python/llm/example/GPU/HuggingFace/LLM/mixtral) |
| Falcon | [link](../../python/llm/example/CPU/HF-Transformers-AutoModels/Model/falcon) | [link](../../python/llm/example/GPU/HuggingFace/LLM/falcon) |
| MPT | [link](../../python/llm/example/CPU/HF-Transformers-AutoModels/Model/mpt) | [link](../../python/llm/example/GPU/HuggingFace/LLM/mpt) |
| Dolly-v1 | [link](../../python/llm/example/CPU/HF-Transformers-AutoModels/Model/dolly_v1) | [link](../../python/llm/example/GPU/HuggingFace/LLM/dolly-v1) |
| Dolly-v2 | [link](../../python/llm/example/CPU/HF-Transformers-AutoModels/Model/dolly_v2) | [link](../../python/llm/example/GPU/HuggingFace/LLM/dolly-v2) |
| Replit Code| [link](../../python/llm/example/CPU/HF-Transformers-AutoModels/Model/replit) | [link](../../python/llm/example/GPU/HuggingFace/LLM/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/HuggingFace/LLM/starcoder) |
| Baichuan | [link](../../python/llm/example/CPU/HF-Transformers-AutoModels/Model/baichuan) | [link](../../python/llm/example/GPU/HuggingFace/LLM/baichuan) |
| Baichuan2 | [link](../../python/llm/example/CPU/HF-Transformers-AutoModels/Model/baichuan2) | [link](../../python/llm/example/GPU/HuggingFace/LLM/baichuan2) | [Python link](../../python/llm/example/NPU/HF-Transformers-AutoModels/LLM) |
| InternLM | [link](../../python/llm/example/CPU/HF-Transformers-AutoModels/Model/internlm) | [link](../../python/llm/example/GPU/HuggingFace/LLM/internlm) |
| InternVL2 | | [link](../../python/llm/example/GPU/HuggingFace/Multimodal/internvl2) |
| Qwen | [link](../../python/llm/example/CPU/HF-Transformers-AutoModels/Model/qwen) | [link](../../python/llm/example/GPU/HuggingFace/LLM/qwen) |
| Qwen1.5 | [link](../../python/llm/example/CPU/HF-Transformers-AutoModels/Model/qwen1.5) | [link](../../python/llm/example/GPU/HuggingFace/LLM/qwen1.5) |
| Qwen2 | [link](../../python/llm/example/CPU/HF-Transformers-AutoModels/Model/qwen2) | [link](../../python/llm/example/GPU/HuggingFace/LLM/qwen2) | [Python link](../../python/llm/example/NPU/HF-Transformers-AutoModels/LLM), [C++ link](../../python/llm/example/NPU/HF-Transformers-AutoModels/LLM/CPP_Examples) |
| Qwen2.5 | | [link](../../python/llm/example/GPU/HuggingFace/LLM/qwen2.5) | [Python link](../../python/llm/example/NPU/HF-Transformers-AutoModels/LLM), [C++ link](../../python/llm/example/NPU/HF-Transformers-AutoModels/LLM/CPP_Examples) |
| Qwen-VL | [link](../../python/llm/example/CPU/HF-Transformers-AutoModels/Model/qwen-vl) | [link](../../python/llm/example/GPU/HuggingFace/Multimodal/qwen-vl) |
| Qwen2-VL || [link](../../python/llm/example/GPU/PyTorch-Models/Model/qwen2-vl) |
| Qwen2-Audio | | [link](../../python/llm/example/GPU/HuggingFace/Multimodal/qwen2-audio) |
| Aquila | [link](../../python/llm/example/CPU/HF-Transformers-AutoModels/Model/aquila) | [link](../../python/llm/example/GPU/HuggingFace/LLM/aquila) |
| Aquila2 | [link](../../python/llm/example/CPU/HF-Transformers-AutoModels/Model/aquila2) | [link](../../python/llm/example/GPU/HuggingFace/LLM/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/HuggingFace/Multimodal/whisper) |
| Phi-1_5 | [link](../../python/llm/example/CPU/HF-Transformers-AutoModels/Model/phi-1_5) | [link](../../python/llm/example/GPU/HuggingFace/LLM/phi-1_5) |
| Flan-t5 | [link](../../python/llm/example/CPU/HF-Transformers-AutoModels/Model/flan-t5) | [link](../../python/llm/example/GPU/HuggingFace/LLM/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/HuggingFace/LLM/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/HuggingFace/Multimodal/distil-whisper) |
| Yi | [link](../../python/llm/example/CPU/HF-Transformers-AutoModels/Model/yi) | [link](../../python/llm/example/GPU/HuggingFace/LLM/yi) |
| BlueLM | [link](../../python/llm/example/CPU/HF-Transformers-AutoModels/Model/bluelm) | [link](../../python/llm/example/GPU/HuggingFace/LLM/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/HuggingFace/LLM/solar) |
| Phixtral | [link](../../python/llm/example/CPU/HF-Transformers-AutoModels/Model/phixtral) | [link](../../python/llm/example/GPU/HuggingFace/LLM/phixtral) |
| InternLM2 | [link](../../python/llm/example/CPU/HF-Transformers-AutoModels/Model/internlm2) | [link](../../python/llm/example/GPU/HuggingFace/LLM/internlm2) |
| RWKV4 | | [link](../../python/llm/example/GPU/HuggingFace/LLM/rwkv4) |
| RWKV5 | | [link](../../python/llm/example/GPU/HuggingFace/LLM/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/HuggingFace/LLM/phi-2) |
| Phi-3 | [link](../../python/llm/example/CPU/HF-Transformers-AutoModels/Model/phi-3) | [link](../../python/llm/example/GPU/HuggingFace/LLM/phi-3) |
| Phi-3-vision | [link](../../python/llm/example/CPU/HF-Transformers-AutoModels/Model/phi-3-vision) | [link](../../python/llm/example/GPU/HuggingFace/Multimodal/phi-3-vision) |
| Yuan2 | [link](../../python/llm/example/CPU/HF-Transformers-AutoModels/Model/yuan2) | [link](../../python/llm/example/GPU/HuggingFace/LLM/yuan2) |
| Gemma | [link](../../python/llm/example/CPU/HF-Transformers-AutoModels/Model/gemma) | [link](../../python/llm/example/GPU/HuggingFace/LLM/gemma) |
| Gemma2 | | [link](../../python/llm/example/GPU/HuggingFace/LLM/gemma2) |
| DeciLM-7B | [link](../../python/llm/example/CPU/HF-Transformers-AutoModels/Model/deciLM-7b) | [link](../../python/llm/example/GPU/HuggingFace/LLM/deciLM-7b) |
| Deepseek | [link](../../python/llm/example/CPU/HF-Transformers-AutoModels/Model/deepseek) | [link](../../python/llm/example/GPU/HuggingFace/LLM/deepseek) |
| StableLM | [link](../../python/llm/example/CPU/HF-Transformers-AutoModels/Model/stablelm) | [link](../../python/llm/example/GPU/HuggingFace/LLM/stablelm) |
| CodeGemma | [link](../../python/llm/example/CPU/HF-Transformers-AutoModels/Model/codegemma) | [link](../../python/llm/example/GPU/HuggingFace/LLM/codegemma) |
| Command-R/cohere | [link](../../python/llm/example/CPU/HF-Transformers-AutoModels/Model/cohere) | [link](../../python/llm/example/GPU/HuggingFace/LLM/cohere) |
| CodeGeeX2 | [link](../../python/llm/example/CPU/HF-Transformers-AutoModels/Model/codegeex2) | [link](../../python/llm/example/GPU/HuggingFace/LLM/codegeex2) |
| MiniCPM | [link](../../python/llm/example/CPU/HF-Transformers-AutoModels/Model/minicpm) | [link](../../python/llm/example/GPU/HuggingFace/LLM/minicpm) | [Python link](../../python/llm/example/NPU/HF-Transformers-AutoModels/LLM), [C++ link](../../python/llm/example/NPU/HF-Transformers-AutoModels/LLM/CPP_Examples) |
| MiniCPM3 | | [link](../../python/llm/example/GPU/HuggingFace/LLM/minicpm3) |
| MiniCPM-V | | [link](../../python/llm/example/GPU/HuggingFace/Multimodal/MiniCPM-V) |
| MiniCPM-V-2 | [link](../../python/llm/example/CPU/HF-Transformers-AutoModels/Model/minicpm-v-2) | [link](../../python/llm/example/GPU/HuggingFace/Multimodal/MiniCPM-V-2) |
| MiniCPM-Llama3-V-2_5 | | [link](../../python/llm/example/GPU/HuggingFace/Multimodal/MiniCPM-Llama3-V-2_5) | [Python link](../../python/llm/example/NPU/HF-Transformers-AutoModels/Multimodal) |
| MiniCPM-V-2_6 | [link](../../python/llm/example/CPU/HF-Transformers-AutoModels/Model/minicpm-v-2_6) | [link](../../python/llm/example/GPU/HuggingFace/Multimodal/MiniCPM-V-2_6) | [Python link](../../python/llm/example/NPU/HF-Transformers-AutoModels/Multimodal) |
| StableDiffusion | | [link](../../python/llm/example/GPU/HuggingFace/Multimodal/StableDiffusion) |
| Bce-Embedding-Base-V1 | | | [Python link](../../python/llm/example/NPU/HF-Transformers-AutoModels/Embedding) |
| Speech_Paraformer-Large | | | [Python link](../../python/llm/example/NPU/HF-Transformers-AutoModels/Multimodal) |

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# 💫 Intel® LLM Library for PyTorch*
<p>
< <a href='./README.md'>English</a> | <b>中文 ></b>
</p>
## 最新更新 🔥
- [2025/01] 新增在 Intel Arc [B580](Quickstart/bmg_quickstart.md) GPU 上运行 `ipex-llm` 的指南。
- [2024/12] 新增在 Intel GPU 上运行 [Ollama 0.4.6](Quickstart/ollama_quickstart.zh-CN.md) 的支持。
- [2024/12] 增加了对 Intel Core Ultra [NPU](Quickstart/npu_quickstart.md)（包括 100H，200V 和 200K 系列）的 **Python** 和 **C++** 支持。
- [2024/11] 新增在 Intel Arc GPUs 上运行 [vLLM 0.6.2](DockerGuides/vllm_docker_quickstart.md) 的支持。
<details><summary>更多更新</summary>
<br/>
- [2024/07] 新增 Microsoft **GraphRAG** 的支持（使用运行在本地 Intel GPU 上的 LLM），详情参考[快速入门指南](Quickstart/graphrag_quickstart.md)。
- [2024/07] 全面增强了对多模态大模型的支持，包括 [StableDiffusion](../../python/llm/example/GPU/HuggingFace/Multimodal/StableDiffusion), [Phi-3-Vision](../../python/llm/example/GPU/HuggingFace/Multimodal/phi-3-vision), [Qwen-VL](../../python/llm/example/GPU/HuggingFace/Multimodal/qwen-vl)，更多详情请点击[这里](../../python/llm/example/GPU/HuggingFace/Multimodal)。
- [2024/07] 新增 Intel GPU 上 **FP6** 的支持，详情参考[更多数据类型样例](../../python/llm/example/GPU/HuggingFace/More-Data-Types)。
- [2024/06] 新增对 Intel Core Ultra 处理器中 **NPU** 的实验性支持，详情参考[相关示例](../../python/llm/example/NPU/HF-Transformers-AutoModels)。
- [2024/06] 增加了对[流水线并行推理](../../python/llm/example/GPU/Pipeline-Parallel-Inference)的全面支持，使得用两块或更多 Intel GPU（如 Arc）上运行 LLM 变得更容易。
- [2024/06] 新增在 Intel GPU 上运行 **RAGFlow** 的支持，详情参考[快速入门指南](Quickstart/ragflow_quickstart.md)。
- [2024/05] 新增 **Axolotl** 的支持，可以在 Intel GPU 上进行LLM微调，详情参考[快速入门指南](Quickstart/axolotl_quickstart.md)。
- [2024/05] 你可以使用 **Docker** [images](#docker) 很容易地运行 `ipex-llm` 推理、服务和微调。
- [2024/05] 你能够在 Windows 上仅使用 "*[one command](Quickstart/install_windows_gpu.zh-CN.md#安装-ipex-llm)*" 来安装 `ipex-llm`。
- [2024/04] 你现在可以在 Intel GPU 上使用 `ipex-llm` 运行 **Open WebUI** ，详情参考[快速入门指南](Quickstart/open_webui_with_ollama_quickstart.md)。
- [2024/04] 你现在可以在 Intel GPU 上使用 `ipex-llm` 以及 `llama.cpp` 和 `ollama` 运行 **Llama 3** ，详情参考[快速入门指南](Quickstart/llama3_llamacpp_ollama_quickstart.md)。
- [2024/04] `ipex-llm` 现在在Intel [GPU](../../python/llm/example/GPU/HuggingFace/LLM/llama3) 和 [CPU](../../python/llm/example/CPU/HF-Transformers-AutoModels/Model/llama3) 上都支持 **Llama 3** 了。
- [2024/04] `ipex-llm` 现在提供 C++ 推理, 在 Intel GPU 上它可以用作运行 [llama.cpp](Quickstart/llama_cpp_quickstart.zh-CN.md) 和 [ollama](Quickstart/ollama_quickstart.zh-CN.md) 的加速后端。
- [2024/03] `bigdl-llm` 现已更名为 `ipex-llm` (请参阅[此处](Quickstart/bigdl_llm_migration.md)的迁移指南)，你可以在[这里](https://github.com/intel-analytics/bigdl-2.x)找到原始BigDL项目。
- [2024/02] `ipex-llm` 现在支持直接从 [ModelScope](../../python/llm/example/GPU/ModelScope-Models) ([魔搭](../../python/llm/example/CPU/ModelScope-Models)) loading 模型。
- [2024/02] `ipex-llm` 增加 **INT2** 的支持 (基于 llama.cpp [IQ2](../../python/llm/example/GPU/HuggingFace/Advanced-Quantizations/GGUF-IQ2) 机制), 这使得在具有 16GB VRAM 的 Intel GPU 上运行大型 LLM（例如 Mixtral-8x7B）成为可能。
- [2024/02] 用户现在可以通过 [Text-Generation-WebUI](https://github.com/intel-analytics/text-generation-webui) GUI 使用 `ipex-llm`。
- [2024/02] `ipex-llm` 现在支持 *[Self-Speculative Decoding](Inference/Self_Speculative_Decoding.md)*，这使得在 Intel [GPU](../../python/llm/example/GPU/Speculative-Decoding) 和 [CPU](../../python/llm/example/CPU/Speculative-Decoding) 上为 FP16 和 BF16 推理带来 **~30% 加速** 。
- [2024/02] `ipex-llm` 现在支持在 Intel GPU 上进行各种 LLM 微调(包括 [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) 和 [ReLoRA](../../python/llm/example/GPU/LLM-Finetuning/ReLora))。
- [2024/01] 使用 `ipex-llm` [QLoRA](../../python/llm/example/GPU/LLM-Finetuning/QLoRA)，我们成功地在 8 个 Intel Max 1550 GPU 上使用 [Standford-Alpaca](../../python/llm/example/GPU/LLM-Finetuning/QLoRA/alpaca-qlora) 数据集分别对 LLaMA2-7B（**21 分钟内**）和 LLaMA2-70B（**3.14 小时内**）进行了微调，具体详情参阅[博客](https://www.intel.com/content/www/us/en/developer/articles/technical/finetuning-llms-on-intel-gpus-using-bigdl-llm.html)。
- [2023/12] `ipex-llm` 现在支持 [ReLoRA](../../python/llm/example/GPU/LLM-Finetuning/ReLora) (具体内容请参阅 *["ReLoRA: High-Rank Training Through Low-Rank Updates"](https://arxiv.org/abs/2307.05695)*).
- [2023/12] `ipex-llm` 现在在 Intel [GPU](../../python/llm/example/GPU/HuggingFace/LLM/mixtral) 和 [CPU](../../python/llm/example/CPU/HF-Transformers-AutoModels/Model/mixtral) 上均支持 [Mixtral-8x7B](../../python/llm/example/GPU/HuggingFace/LLM/mixtral)。
- [2023/12] `ipex-llm` 现在支持 [QA-LoRA](../../python/llm/example/GPU/LLM-Finetuning/QA-LoRA) (具体内容请参阅 *["QA-LoRA: Quantization-Aware Low-Rank Adaptation of Large Language Models"](https://arxiv.org/abs/2309.14717)*).
- [2023/12] `ipex-llm` 现在在 Intel ***GPU*** 上支持 [FP8 and FP4 inference](../../python/llm/example/GPU/HuggingFace/More-Data-Types)。
- [2023/11] 初步支持直接将 [GGUF](../../python/llm/example/GPU/HuggingFace/Advanced-Quantizations/GGUF)，[AWQ](../../python/llm/example/GPU/HuggingFace/Advanced-Quantizations/AWQ) 和 [GPTQ](../../python/llm/example/GPU/HuggingFace/Advanced-Quantizations/GPTQ) 模型加载到 `ipex-llm` 中。
- [2023/11] `ipex-llm` 现在在 Intel [GPU](../../python/llm/example/GPU/vLLM-Serving) 和 [CPU](../../python/llm/example/CPU/vLLM-Serving) 上都支持 [vLLM continuous batching](../../python/llm/example/GPU/vLLM-Serving) 。
- [2023/10] `ipex-llm` 现在在 Intel [GPU](../../python/llm/example/GPU/LLM-Finetuning/QLoRA) 和 [CPU](../../python/llm/example/CPU/QLoRA-FineTuning) 上均支持 [QLoRA finetuning](../../python/llm/example/GPU/LLM-Finetuning/QLoRA) 。
- [2023/10] `ipex-llm` 现在在 Intel GPU 和 CPU 上都支持 [FastChat serving](../../python/llm/src/ipex_llm/llm/serving) 。
- [2023/09] `ipex-llm` 现在支持 [Intel GPU](../../python/llm/example/GPU) (包括 iGPU, Arc, Flex 和 MAX)。
- [2023/09] `ipex-llm` [教程](https://github.com/intel-analytics/ipex-llm-tutorial) 已发布。
</details>
## `ipex-llm` 快速入门
### 使用
- [Arc B580](Quickstart/bmg_quickstart.md): 在 Intel Arc **B580** GPU 上运行 `ipex-llm`（包括 Ollama, llama.cpp, PyTorch, HuggingFace 等）
- [NPU](Quickstart/npu_quickstart.md): 在 Intel **NPU** 上运行 `ipex-llm`（支持 Python 和 C++）
- [llama.cpp](Quickstart/llama_cpp_quickstart.zh-CN.md): 在 Intel GPU 上运行 **llama.cpp** (*使用 `ipex-llm` 的 C++ 接口*)
- [Ollama](Quickstart/ollama_quickstart.zh-CN.md): 在 Intel GPU 上运行 **ollama** (*使用 `ipex-llm` 的 C++ 接口*)
- [PyTorch/HuggingFace](Quickstart/install_windows_gpu.zh-CN.md): 使用 [Windows](Quickstart/install_windows_gpu.zh-CN.md) 和 [Linux](Quickstart/install_linux_gpu.zh-CN.md) 在 Intel GPU 上运行 **PyTorch**、**HuggingFace**、**LangChain**、**LlamaIndex** 等 (*使用 `ipex-llm` 的 Python 接口*)
- [vLLM](Quickstart/vLLM_quickstart.md): 在 Intel [GPU](DockerGuides/vllm_docker_quickstart.md) 和 [CPU](DockerGuides/vllm_cpu_docker_quickstart.md) 上使用 `ipex-llm` 运行 **vLLM**
- [FastChat](Quickstart/fastchat_quickstart.md): 在 Intel GPU 和 CPU 上使用 `ipex-llm` 运行 **FastChat** 服务
- [Serving on multiple Intel GPUs](Quickstart/deepspeed_autotp_fastapi_quickstart.md): 利用 DeepSpeed AutoTP 和 FastAPI 在 **多个 Intel GPU** 上运行 `ipex-llm` 推理服务
- [Text-Generation-WebUI](Quickstart/webui_quickstart.md): 使用 `ipex-llm` 运行 `oobabooga` **WebUI**
- [Axolotl](Quickstart/axolotl_quickstart.md): 使用 **Axolotl** 和 `ipex-llm` 进行 LLM 微调
- [Benchmarking](Quickstart/benchmark_quickstart.md): 在 Intel GPU 和 CPU 上运行**性能基准测试**（延迟和吞吐量）
### Docker
- [GPU Inference in C++](DockerGuides/docker_cpp_xpu_quickstart.md): 在 Intel GPU 上使用 `ipex-llm` 运行 `llama.cpp`, `ollama`等
- [GPU Inference in Python](DockerGuides/docker_pytorch_inference_gpu.md) : 在 Intel GPU 上使用 `ipex-llm` 运行 HuggingFace `transformers`, `LangChain`, `LlamaIndex`, `ModelScope`，等
- [vLLM on GPU](DockerGuides/vllm_docker_quickstart.md): 在 Intel GPU 上使用 `ipex-llm` 运行 `vLLM` 推理服务
- [vLLM on CPU](DockerGuides/vllm_cpu_docker_quickstart.md): 在 Intel CPU 上使用 `ipex-llm` 运行 `vLLM` 推理服务
- [FastChat on GPU](DockerGuides/fastchat_docker_quickstart.md): 在 Intel GPU 上使用 `ipex-llm` 运行 `FastChat` 推理服务
- [VSCode on GPU](DockerGuides/docker_run_pytorch_inference_in_vscode.md): 在 Intel GPU 上使用 VSCode 开发并运行基于 Python 的 `ipex-llm` 应用
### 应用
- [GraphRAG](Quickstart/graphrag_quickstart.md): 基于 `ipex-llm` 使用本地 LLM 运行 Microsoft 的 `GraphRAG`
- [RAGFlow](Quickstart/ragflow_quickstart.md): 基于 `ipex-llm` 运行 `RAGFlow` (*一个开源的 RAG 引擎*)
- [LangChain-Chatchat](Quickstart/chatchat_quickstart.md): 基于 `ipex-llm` 运行 `LangChain-Chatchat` (*使用 RAG pipline 的知识问答库*)
- [Coding copilot](Quickstart/continue_quickstart.md): 基于 `ipex-llm` 运行 `Continue` (VSCode 里的编码智能助手)
- [Open WebUI](Quickstart/open_webui_with_ollama_quickstart.md): 基于 `ipex-llm` 运行 `Open WebUI`
- [PrivateGPT](Quickstart/privateGPT_quickstart.md): 基于 `ipex-llm` 运行 `PrivateGPT` 与文档进行交互
- [Dify platform](Quickstart/dify_quickstart.md): 在`Dify`(*一款开源的大语言模型应用开发平台*) 里接入 `ipex-llm` 加速本地 LLM
### 安装
- [Windows GPU](Quickstart/install_windows_gpu.zh-CN.md): 在带有 Intel GPU 的 Windows 系统上安装 `ipex-llm`
- [Linux GPU](Quickstart/install_linux_gpu.zh-CN.md): 在带有 Intel GPU 的Linux系统上安装 `ipex-llm`
- *更多内容, 请参考[完整安装指南](Overview/install.md)*
### 代码示例
- #### 低比特推理
- [INT4 inference](../../python/llm/example/GPU/HuggingFace/LLM): 在 Intel [GPU](../../python/llm/example/GPU/HuggingFace/LLM) 和 [CPU](../../python/llm/example/CPU/HF-Transformers-AutoModels/Model) 上进行 **INT4** LLM 推理
- [FP8/FP6/FP4 inference](../../python/llm/example/GPU/HuggingFace/More-Data-Types): 在 Intel [GPU](../../python/llm/example/GPU/HuggingFace/More-Data-Types) 上进行 **FP8**，**FP6** 和 **FP4** LLM 推理
- [INT8 inference](../../python/llm/example/GPU/HuggingFace/More-Data-Types): 在 Intel [GPU](../../python/llm/example/GPU/HuggingFace/More-Data-Types) 和 [CPU](../../python/llm/example/CPU/HF-Transformers-AutoModels/More-Data-Types) 上进行 **INT8** LLM 推理
- [INT2 inference](../../python/llm/example/GPU/HuggingFace/Advanced-Quantizations/GGUF-IQ2): 在 Intel [GPU](../../python/llm/example/GPU/HuggingFace/Advanced-Quantizations/GGUF-IQ2) 上进行 **INT2** LLM 推理 (基于 llama.cpp IQ2 机制)
- #### FP16/BF16 推理
- 在 Intel [GPU](../../python/llm/example/GPU/Speculative-Decoding) 上进行 **FP16** LLM 推理（并使用 [self-speculative decoding](Inference/Self_Speculative_Decoding.md) 优化）
- 在 Intel [CPU](../../python/llm/example/CPU/Speculative-Decoding) 上进行 **BF16** LLM 推理（并使用 [self-speculative decoding](Inference/Self_Speculative_Decoding.md) 优化）
- #### 分布式推理
- 在 Intel [GPU](../../python/llm/example/GPU/Pipeline-Parallel-Inference) 上进行 **流水线并行** 推理
- 在 Intel [GPU](../../python/llm/example/GPU/Deepspeed-AutoTP) 上进行 **DeepSpeed AutoTP** 推理
- #### 保存和加载
- [Low-bit models](../../python/llm/example/CPU/HF-Transformers-AutoModels/Save-Load): 保存和加载 `ipex-llm` 低比特模型 (INT4/FP4/FP6/INT8/FP8/FP16/etc.)
- [GGUF](../../python/llm/example/GPU/HuggingFace/Advanced-Quantizations/GGUF): 直接将 GGUF 模型加载到 `ipex-llm` 中
- [AWQ](../../python/llm/example/GPU/HuggingFace/Advanced-Quantizations/AWQ): 直接将 AWQ 模型加载到 `ipex-llm` 中
- [GPTQ](../../python/llm/example/GPU/HuggingFace/Advanced-Quantizations/GPTQ): 直接将 GPTQ 模型加载到 `ipex-llm` 中
- #### 微调
- 在 Intel [GPU](../../python/llm/example/GPU/LLM-Finetuning) 进行 LLM 微调，包括 [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) 和 [ReLoRA](../../python/llm/example/GPU/LLM-Finetuning/ReLora)
- 在 Intel [CPU](../../python/llm/example/CPU/QLoRA-FineTuning) 进行 QLoRA 微调
- #### 与社区库集成
- [HuggingFace transformers](../../python/llm/example/GPU/HuggingFace)
- [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](Quickstart/axolotl_quickstart.md)
- [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)
- [教程](https://github.com/intel-analytics/ipex-llm-tutorial)
## API 文档
- [HuggingFace Transformers 兼容的 API (Auto Classes)](PythonAPI/transformers.md)
- [适用于任意 Pytorch 模型的 API](https://github.com/intel-analytics/ipex-llm/blob/main/PythonAPI/optimize.md)
## FAQ
- [常见问题解答](Overview/FAQ/faq.md)
## 模型验证
50+ 模型已经在 `ipex-llm` 上得到优化和验证，包括 *LLaMA/LLaMA2, Mistral, Mixtral, Gemma, LLaVA, Whisper, ChatGLM2/ChatGLM3, Baichuan/Baichuan2, Qwen/Qwen-1.5, InternLM,* 更多模型请参看下表，
| 模型 | CPU 示例 | GPU 示例 | NPU 示例 |
|----------- |------------------------------------------|-------------------------------------------|-------------------------------------------|
| LLaMA | [link1](../../python/llm/example/CPU/Native-Models), [link2](../../python/llm/example/CPU/HF-Transformers-AutoModels/Model/vicuna) |[link](../../python/llm/example/GPU/HuggingFace/LLM/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/HuggingFace/LLM/llama2) | [Python link](../../python/llm/example/NPU/HF-Transformers-AutoModels/LLM), [C++ link](../../python/llm/example/NPU/HF-Transformers-AutoModels/LLM/CPP_Examples) |
| LLaMA 3 | [link](../../python/llm/example/CPU/HF-Transformers-AutoModels/Model/llama3) | [link](../../python/llm/example/GPU/HuggingFace/LLM/llama3) | [Python link](../../python/llm/example/NPU/HF-Transformers-AutoModels/LLM), [C++ link](../../python/llm/example/NPU/HF-Transformers-AutoModels/LLM/CPP_Examples) |
| LLaMA 3.1 | [link](../../python/llm/example/CPU/HF-Transformers-AutoModels/Model/llama3.1) | [link](../../python/llm/example/GPU/HuggingFace/LLM/llama3.1) |
| LLaMA 3.2 | | [link](../../python/llm/example/GPU/HuggingFace/LLM/llama3.2) | [Python link](../../python/llm/example/NPU/HF-Transformers-AutoModels/LLM), [C++ link](../../python/llm/example/NPU/HF-Transformers-AutoModels/LLM/CPP_Examples) |
| LLaMA 3.2-Vision | | [link](../../python/llm/example/GPU/PyTorch-Models/Model/llama3.2-vision/) |
| 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/HuggingFace/LLM/chatglm2) |
| ChatGLM3 | [link](../../python/llm/example/CPU/HF-Transformers-AutoModels/Model/chatglm3) | [link](../../python/llm/example/GPU/HuggingFace/LLM/chatglm3) |
| GLM-4 | [link](../../python/llm/example/CPU/HF-Transformers-AutoModels/Model/glm4) | [link](../../python/llm/example/GPU/HuggingFace/LLM/glm4) |
| GLM-4V | [link](../../python/llm/example/CPU/HF-Transformers-AutoModels/Model/glm-4v) | [link](../../python/llm/example/GPU/HuggingFace/Multimodal/glm-4v) |
| GLM-Edge | | [link](../../python/llm/example/GPU/HuggingFace/LLM/glm-edge) | [Python link](../../python/llm/example/NPU/HF-Transformers-AutoModels/LLM) |
| GLM-Edge-V | | [link](../../python/llm/example/GPU/HuggingFace/Multimodal/glm-edge-v) |
| Mistral | [link](../../python/llm/example/CPU/HF-Transformers-AutoModels/Model/mistral) | [link](../../python/llm/example/GPU/HuggingFace/LLM/mistral) |
| Mixtral | [link](../../python/llm/example/CPU/HF-Transformers-AutoModels/Model/mixtral) | [link](../../python/llm/example/GPU/HuggingFace/LLM/mixtral) |
| Falcon | [link](../../python/llm/example/CPU/HF-Transformers-AutoModels/Model/falcon) | [link](../../python/llm/example/GPU/HuggingFace/LLM/falcon) |
| MPT | [link](../../python/llm/example/CPU/HF-Transformers-AutoModels/Model/mpt) | [link](../../python/llm/example/GPU/HuggingFace/LLM/mpt) |
| Dolly-v1 | [link](../../python/llm/example/CPU/HF-Transformers-AutoModels/Model/dolly_v1) | [link](../../python/llm/example/GPU/HuggingFace/LLM/dolly-v1) |
| Dolly-v2 | [link](../../python/llm/example/CPU/HF-Transformers-AutoModels/Model/dolly_v2) | [link](../../python/llm/example/GPU/HuggingFace/LLM/dolly-v2) |
| Replit Code| [link](../../python/llm/example/CPU/HF-Transformers-AutoModels/Model/replit) | [link](../../python/llm/example/GPU/HuggingFace/LLM/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/HuggingFace/LLM/starcoder) |
| Baichuan | [link](../../python/llm/example/CPU/HF-Transformers-AutoModels/Model/baichuan) | [link](../../python/llm/example/GPU/HuggingFace/LLM/baichuan) |
| Baichuan2 | [link](../../python/llm/example/CPU/HF-Transformers-AutoModels/Model/baichuan2) | [link](../../python/llm/example/GPU/HuggingFace/LLM/baichuan2) | [Python link](../../python/llm/example/NPU/HF-Transformers-AutoModels/LLM) |
| InternLM | [link](../../python/llm/example/CPU/HF-Transformers-AutoModels/Model/internlm) | [link](../../python/llm/example/GPU/HuggingFace/LLM/internlm) |
| InternVL2 | | [link](../../python/llm/example/GPU/HuggingFace/Multimodal/internvl2) |
| Qwen | [link](../../python/llm/example/CPU/HF-Transformers-AutoModels/Model/qwen) | [link](../../python/llm/example/GPU/HuggingFace/LLM/qwen) |
| Qwen1.5 | [link](../../python/llm/example/CPU/HF-Transformers-AutoModels/Model/qwen1.5) | [link](../../python/llm/example/GPU/HuggingFace/LLM/qwen1.5) |
| Qwen2 | [link](../../python/llm/example/CPU/HF-Transformers-AutoModels/Model/qwen2) | [link](../../python/llm/example/GPU/HuggingFace/LLM/qwen2) | [Python link](../../python/llm/example/NPU/HF-Transformers-AutoModels/LLM), [C++ link](../../python/llm/example/NPU/HF-Transformers-AutoModels/LLM/CPP_Examples) |
| Qwen2.5 | | [link](../../python/llm/example/GPU/HuggingFace/LLM/qwen2.5) | [Python link](../../python/llm/example/NPU/HF-Transformers-AutoModels/LLM), [C++ link](../../python/llm/example/NPU/HF-Transformers-AutoModels/LLM/CPP_Examples) |
| Qwen-VL | [link](../../python/llm/example/CPU/HF-Transformers-AutoModels/Model/qwen-vl) | [link](../../python/llm/example/GPU/HuggingFace/Multimodal/qwen-vl) |
| Qwen2-VL || [link](../../python/llm/example/GPU/PyTorch-Models/Model/qwen2-vl) |
| Qwen2-Audio | | [link](../../python/llm/example/GPU/HuggingFace/Multimodal/qwen2-audio) |
| Aquila | [link](../../python/llm/example/CPU/HF-Transformers-AutoModels/Model/aquila) | [link](../../python/llm/example/GPU/HuggingFace/LLM/aquila) |
| Aquila2 | [link](../../python/llm/example/CPU/HF-Transformers-AutoModels/Model/aquila2) | [link](../../python/llm/example/GPU/HuggingFace/LLM/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/HuggingFace/Multimodal/whisper) |
| Phi-1_5 | [link](../../python/llm/example/CPU/HF-Transformers-AutoModels/Model/phi-1_5) | [link](../../python/llm/example/GPU/HuggingFace/LLM/phi-1_5) |
| Flan-t5 | [link](../../python/llm/example/CPU/HF-Transformers-AutoModels/Model/flan-t5) | [link](../../python/llm/example/GPU/HuggingFace/LLM/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/HuggingFace/LLM/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/HuggingFace/Multimodal/distil-whisper) |
| Yi | [link](../../python/llm/example/CPU/HF-Transformers-AutoModels/Model/yi) | [link](../../python/llm/example/GPU/HuggingFace/LLM/yi) |
| BlueLM | [link](../../python/llm/example/CPU/HF-Transformers-AutoModels/Model/bluelm) | [link](../../python/llm/example/GPU/HuggingFace/LLM/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/HuggingFace/LLM/solar) |
| Phixtral | [link](../../python/llm/example/CPU/HF-Transformers-AutoModels/Model/phixtral) | [link](../../python/llm/example/GPU/HuggingFace/LLM/phixtral) |
| InternLM2 | [link](../../python/llm/example/CPU/HF-Transformers-AutoModels/Model/internlm2) | [link](../../python/llm/example/GPU/HuggingFace/LLM/internlm2) |
| RWKV4 | | [link](../../python/llm/example/GPU/HuggingFace/LLM/rwkv4) |
| RWKV5 | | [link](../../python/llm/example/GPU/HuggingFace/LLM/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/HuggingFace/LLM/phi-2) |
| Phi-3 | [link](../../python/llm/example/CPU/HF-Transformers-AutoModels/Model/phi-3) | [link](../../python/llm/example/GPU/HuggingFace/LLM/phi-3) |
| Phi-3-vision | [link](../../python/llm/example/CPU/HF-Transformers-AutoModels/Model/phi-3-vision) | [link](../../python/llm/example/GPU/HuggingFace/Multimodal/phi-3-vision) |
| Yuan2 | [link](../../python/llm/example/CPU/HF-Transformers-AutoModels/Model/yuan2) | [link](../../python/llm/example/GPU/HuggingFace/LLM/yuan2) |
| Gemma | [link](../../python/llm/example/CPU/HF-Transformers-AutoModels/Model/gemma) | [link](../../python/llm/example/GPU/HuggingFace/LLM/gemma) |
| Gemma2 | | [link](../../python/llm/example/GPU/HuggingFace/LLM/gemma2) |
| DeciLM-7B | [link](../../python/llm/example/CPU/HF-Transformers-AutoModels/Model/deciLM-7b) | [link](../../python/llm/example/GPU/HuggingFace/LLM/deciLM-7b) |
| Deepseek | [link](../../python/llm/example/CPU/HF-Transformers-AutoModels/Model/deepseek) | [link](../../python/llm/example/GPU/HuggingFace/LLM/deepseek) |
| StableLM | [link](../../python/llm/example/CPU/HF-Transformers-AutoModels/Model/stablelm) | [link](../../python/llm/example/GPU/HuggingFace/LLM/stablelm) |
| CodeGemma | [link](../../python/llm/example/CPU/HF-Transformers-AutoModels/Model/codegemma) | [link](../../python/llm/example/GPU/HuggingFace/LLM/codegemma) |
| Command-R/cohere | [link](../../python/llm/example/CPU/HF-Transformers-AutoModels/Model/cohere) | [link](../../python/llm/example/GPU/HuggingFace/LLM/cohere) |
| CodeGeeX2 | [link](../../python/llm/example/CPU/HF-Transformers-AutoModels/Model/codegeex2) | [link](../../python/llm/example/GPU/HuggingFace/LLM/codegeex2) |
| MiniCPM | [link](../../python/llm/example/CPU/HF-Transformers-AutoModels/Model/minicpm) | [link](../../python/llm/example/GPU/HuggingFace/LLM/minicpm) | [Python link](../../python/llm/example/NPU/HF-Transformers-AutoModels/LLM), [C++ link](../../python/llm/example/NPU/HF-Transformers-AutoModels/LLM/CPP_Examples) |
| MiniCPM3 | | [link](../../python/llm/example/GPU/HuggingFace/LLM/minicpm3) |
| MiniCPM-V | | [link](../../python/llm/example/GPU/HuggingFace/Multimodal/MiniCPM-V) |
| MiniCPM-V-2 | [link](../../python/llm/example/CPU/HF-Transformers-AutoModels/Model/minicpm-v-2) | [link](../../python/llm/example/GPU/HuggingFace/Multimodal/MiniCPM-V-2) |
| MiniCPM-Llama3-V-2_5 | | [link](../../python/llm/example/GPU/HuggingFace/Multimodal/MiniCPM-Llama3-V-2_5) | [Python link](../../python/llm/example/NPU/HF-Transformers-AutoModels/Multimodal) |
| MiniCPM-V-2_6 | [link](../../python/llm/example/CPU/HF-Transformers-AutoModels/Model/minicpm-v-2_6) | [link](../../python/llm/example/GPU/HuggingFace/Multimodal/MiniCPM-V-2_6) | [Python link](../../python/llm/example/NPU/HF-Transformers-AutoModels/Multimodal) |
| StableDiffusion | | [link](../../python/llm/example/GPU/HuggingFace/Multimodal/StableDiffusion) |
| Bce-Embedding-Base-V1 | | | [Python link](../../python/llm/example/NPU/HF-Transformers-AutoModels/Embedding) |
| Speech_Paraformer-Large | | | [Python link](../../python/llm/example/NPU/HF-Transformers-AutoModels/Multimodal) |