> [!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).***
 
---

# 💫 IPEX-LLM
**`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 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.*
> - *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), [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.* 
> - ***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 [*HuggingFace transformers*](python/llm/example/GPU/HF-Transformers-AutoModels) *(on either Intel Core Ultra laptop or Arc GPU)* with `ipex-llm`  below.

<table width="100%">
  <tr>
    <td align="center" colspan="2"><strong>Intel Core Ultra Laptop</strong></td>
    <td align="center" colspan="2"><strong>Intel Arc GPU</strong></td>
  </tr>
  <tr>
    <td>
      <video src="https://private-user-images.githubusercontent.com/1931082/319632616-895d56cd-e74b-4da1-b4d1-2157df341424.mp4?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9. .Ga8mmCAO62DFCNzU1fdoyC_4MzqhDHzjZedzmi_2L-I" width=100% controls />
    </td>
    <td>
      <video src="https://private-user-images.githubusercontent.com/1931082/319625142-68da379e-59c6-4308-88e8-c17e40baba7b.mp4?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9. .g0bYAj3J8IJci7pLzoJI6QDalyzXzMYtQkDY7aqZMc4" width=100% controls />
    </td>
    <td>
      <video src="https://private-user-images.githubusercontent.com/1931082/319625685-ff13b099-bcda-48f1-b11b-05421e7d386d.mp4?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9. .OFxex8Yj6WyqJKMi6B1Q19KkmbYqYCg1rD49wUwxdXQ" width=100% controls />
    </td>
    <td>
      <video src="https://private-user-images.githubusercontent.com/1931082/319625443-cac77ee6-3a7f-475b-9730-12491105d305.mp4?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9. .a4O2rJ1nPXH5aSgKV15mbf6Qjs4ysvC-BTRnQmbflIo" width=100% controls />
    </td>
  </tr>
  <tr>
    <td align="center" width="25%">
      <a href="https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Quickstart/webui_quickstart.html">Text-Generation-WebUI</a>
    </td>
    <td align="center" width="25%">
      <a href="https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Quickstart/chatchat_quickstart.html">Local RAG using LangChain-Chatchat</a>
    </td>
    <td align="center" width="25%">
      <a href="https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Quickstart/llama_cpp_quickstart.html">llama.cpp</a>
    </td>
    <td align="center" width="25%">
      <a href="https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/GPU/HF-Transformers-AutoModels">HuggingFace transformers</a>
    </td>  </tr>
</table>
       
## Latest Update 🔥 
- [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/main/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)).
<details><summary>More updates</summary>
<br/>
 
- [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.
 
</details> 

[^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
### Install `ipex-llm`
- [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
- [Docker](docker/llm): using `ipex-llm` dockers on Intel CPU and GPU
- *For more details, please refer to the [installation guide](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Overview/install.html)*

### Run `ipex-llm`
- [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/main/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
- [LangChain-Chatchat RAG](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Quickstart/chatchat_quickstart.html): running `ipex-llm` in `LangChain-Chatchat` (*Knowledge Base QA using **RAG** pipeline*)
- [Text-Generation-WebUI](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Quickstart/webui_quickstart.html): running `ipex-llm` in `oobabooga` **WebUI**
- [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

### 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 tansformers](python/llm/example/GPU/HF-Transformers-AutoModels)
  - [Standard PyTorch model](python/llm/example/GPU/PyTorch-Models)
  - [DeepSpeed-AutoTP](python/llm/example/GPU/Deepspeed-AutoTP)
  - [HuggingFace PEFT](python/llm/example/GPU/LLM-Finetuning/HF-PEFT)
  - [HuggingFace TRL](python/llm/example/GPU/LLM-Finetuning/DPO)
  - [LangChain](python/llm/example/GPU/LangChain)
  - [LlamaIndex](python/llm/example/GPU/LlamaIndex)
  - [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)  |
| 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) |
| 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) |
| 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) |

## 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)
