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0-1.14-.243-1.847-.705-2.319-.477-.488-1.319-.862-2.824-1.025-1.487-.161-2.192.138-2.533.529-.269.307-.437.808-.438 1.578v.021c0 .265.021.562.063.893Zm-1.626 0c.042-.331.063-.628.063-.894v-.02c-.001-.77-.169-1.271-.438-1.578-.341-.391-1.046-.69-2.533-.529-1.505.163-2.347.537-2.824 1.025-.462.472-.705 1.179-.705 2.319 0 1.211.175 1.926.558 2.361.365.414 1.084.751 2.657.751 1.21 0 1.902-.394 2.344-.938.475-.584.742-1.44.878-2.497Z"></path><path d="M14.5 14.25a1 1 0 0 1 1 1v2a1 1 0 0 1-2 0v-2a1 1 0 0 1 1-1Zm-5 0a1 1 0 0 1 1 1v2a1 1 0 0 1-2 0v-2a1 1 0 0 1 1-1Z"></path> </svg> <div> <div class="color-fg-default h4">GitHub Copilot</div> Write better code with AI </div> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" data-analytics-event="{"location":"navbar","action":"security","context":"product","tag":"link","label":"security_link_product_navbar"}" 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1-.427.177L13.03 4.03 9.28 7.78a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042l3.75-3.75-1.543-1.543A.25.25 0 0 1 10.604 1Z"></path> </svg> </a></li> </ul> </div> </div> </div> </li> <li class="HeaderMenu-item position-relative flex-wrap flex-justify-between flex-items-center d-block d-lg-flex flex-lg-nowrap flex-lg-items-center js-details-container js-header-menu-item"> <button type="button" class="HeaderMenu-link border-0 width-full width-lg-auto px-0 px-lg-2 py-lg-2 no-wrap d-flex flex-items-center flex-justify-between js-details-target" aria-expanded="false"> Solutions <svg opacity="0.5" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-chevron-down HeaderMenu-icon ml-1"> <path d="M12.78 5.22a.749.749 0 0 1 0 1.06l-4.25 4.25a.749.749 0 0 1-1.06 0L3.22 6.28a.749.749 0 1 1 1.06-1.06L8 8.939l3.72-3.719a.749.749 0 0 1 1.06 0Z"></path> </svg> </button> <div class="HeaderMenu-dropdown dropdown-menu rounded 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data-view-component="true" class="octicon octicon-alert-fill"> <path d="M4.855.708c.5-.896 1.79-.896 2.29 0l4.675 8.351a1.312 1.312 0 0 1-1.146 1.954H1.33A1.313 1.313 0 0 1 .183 9.058ZM7 7V3H5v4Zm-1 3a1 1 0 1 0 0-2 1 1 0 0 0 0 2Z"></path> </svg> </span> <span></span> </div> </div> <div data-target="query-builder.screenReaderFeedback" aria-live="polite" aria-atomic="true" class="sr-only"></div> </query-builder></form> <div class="d-flex flex-row color-fg-muted px-3 text-small color-bg-default search-feedback-prompt"> <a target="_blank" href="https://docs.github.com/search-github/github-code-search/understanding-github-code-search-syntax" data-view-component="true" class="Link color-fg-accent text-normal ml-2">Search syntax tips</a> <div class="d-flex flex-1"></div> </div> </div> </div> </div> </modal-dialog></div> </div> <div data-action="click:qbsearch-input#retract" class="dark-backdrop position-fixed" hidden data-target="qbsearch-input.darkBackdrop"></div> <div 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PyTorch*\u003c/h1\u003e\u003ca id=\"user-content--intel-llm-library-for-pytorch\" class=\"anchor\" aria-label=\"Permalink: 💫 Intel® LLM Library for PyTorch*\" href=\"#-intel-llm-library-for-pytorch\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\n \u003cb\u003e\u0026lt; English\u003c/b\u003e | \u003ca href=\"/intel/ipex-llm/blob/main/README.zh-CN.md\"\u003e中文\u003c/a\u003e \u0026gt;\n\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003e\u003ccode\u003eIPEX-LLM\u003c/code\u003e\u003c/strong\u003e is an LLM acceleration library for Intel \u003ca href=\"/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/install_windows_gpu.md\"\u003eGPU\u003c/a\u003e \u003cem\u003e(e.g., local PC with iGPU, discrete GPU such as Arc, Flex and Max)\u003c/em\u003e, \u003ca href=\"/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/npu_quickstart.md\"\u003eNPU\u003c/a\u003e and CPU \u003csup\u003e\u003ca href=\"#user-content-fn-1-28711fa4219f09e11d411254f051326e\" id=\"user-content-fnref-1-28711fa4219f09e11d411254f051326e\" data-footnote-ref=\"\" aria-describedby=\"footnote-label\"\u003e1\u003c/a\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-alert markdown-alert-note\" dir=\"auto\"\u003e\u003cp class=\"markdown-alert-title\" dir=\"auto\"\u003e\u003csvg class=\"octicon octicon-info mr-2\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"M0 8a8 8 0 1 1 16 0A8 8 0 0 1 0 8Zm8-6.5a6.5 6.5 0 1 0 0 13 6.5 6.5 0 0 0 0-13ZM6.5 7.75A.75.75 0 0 1 7.25 7h1a.75.75 0 0 1 .75.75v2.75h.25a.75.75 0 0 1 0 1.5h-2a.75.75 0 0 1 0-1.5h.25v-2h-.25a.75.75 0 0 1-.75-.75ZM8 6a1 1 0 1 1 0-2 1 1 0 0 1 0 2Z\"\u003e\u003c/path\u003e\u003c/svg\u003eNote\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003cem\u003e\u003ccode\u003eIPEX-LLM\u003c/code\u003e provides seamless integration with \u003ca href=\"/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/llamacpp_portable_zip_gpu_quickstart.md\"\u003ellama.cpp\u003c/a\u003e, \u003ca href=\"/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/ollama_portable_zip_quickstart.md\"\u003eOllama\u003c/a\u003e, \u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace\"\u003eHuggingFace transformers\u003c/a\u003e, \u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/LangChain\"\u003eLangChain\u003c/a\u003e, \u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/LlamaIndex\"\u003eLlamaIndex\u003c/a\u003e, \u003ca href=\"/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/vLLM_quickstart.md\"\u003evLLM\u003c/a\u003e, \u003ca href=\"/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/webui_quickstart.md\"\u003eText-Generation-WebUI\u003c/a\u003e, \u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/Deepspeed-AutoTP\"\u003eDeepSpeed-AutoTP\u003c/a\u003e, \u003ca href=\"/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/fastchat_quickstart.md\"\u003eFastChat\u003c/a\u003e, \u003ca href=\"/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/axolotl_quickstart.md\"\u003eAxolotl\u003c/a\u003e, \u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/LLM-Finetuning\"\u003eHuggingFace PEFT\u003c/a\u003e, \u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/LLM-Finetuning/DPO\"\u003eHuggingFace TRL\u003c/a\u003e, \u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/CPU/Applications/autogen\"\u003eAutoGen\u003c/a\u003e, \u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/ModelScope-Models\"\u003eModeScope\u003c/a\u003e, etc.\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003e\u003cem\u003e\u003cstrong\u003e70+ models\u003c/strong\u003e have been optimized/verified on \u003ccode\u003eipex-llm\u003c/code\u003e (e.g., Llama, Phi, Mistral, Mixtral, DeepSeek, Qwen, ChatGLM, MiniCPM, Qwen-VL, MiniCPM-V and more), with state-of-art \u003cstrong\u003eLLM optimizations\u003c/strong\u003e, \u003cstrong\u003eXPU acceleration\u003c/strong\u003e and \u003cstrong\u003elow-bit (FP8/FP6/FP4/INT4) support\u003c/strong\u003e; see the complete list \u003ca href=\"#verified-models\"\u003ehere\u003c/a\u003e.\u003c/em\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eLatest Update 🔥\u003c/h2\u003e\u003ca id=\"user-content-latest-update-\" class=\"anchor\" aria-label=\"Permalink: Latest Update 🔥\" href=\"#latest-update-\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e[2025/03] We added support for \u003cstrong\u003eGemma3\u003c/strong\u003e model in the latest \u003ca href=\"https://github.com/intel/ipex-llm/issues/12963#issuecomment-2724032898\" data-hovercard-type=\"issue\" data-hovercard-url=\"/intel/ipex-llm/issues/12963/hovercard\"\u003ellama.cpp Portable Zip\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003e[2025/03] We can now run \u003cstrong\u003eDeepSeek-R1-671B-Q4_K_M\u003c/strong\u003e with 1 or 2 Arc A770 on Xeon using the latest \u003ca href=\"/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/llamacpp_portable_zip_gpu_quickstart.md#flashmoe-for-deepseek-v3r1\"\u003ellama.cpp Portable Zip\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003e[2025/02] We added support of \u003ca href=\"https://github.com/intel/ipex-llm/releases/tag/v2.2.0-nightly\"\u003ellama.cpp Portable Zip\u003c/a\u003e for Intel \u003cstrong\u003eGPU\u003c/strong\u003e (both \u003ca href=\"/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/llamacpp_portable_zip_gpu_quickstart.md#windows-quickstart\"\u003eWindows\u003c/a\u003e and \u003ca href=\"/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/llamacpp_portable_zip_gpu_quickstart.md#linux-quickstart\"\u003eLinux\u003c/a\u003e) and \u003cstrong\u003eNPU\u003c/strong\u003e (\u003ca href=\"/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/llama_cpp_npu_portable_zip_quickstart.md\"\u003eWindows\u003c/a\u003e only).\u003c/li\u003e\n\u003cli\u003e[2025/02] We added support of \u003ca href=\"https://github.com/intel/ipex-llm/releases/tag/v2.2.0-nightly\"\u003eOllama Portable Zip\u003c/a\u003e to directly run Ollama on Intel \u003cstrong\u003eGPU\u003c/strong\u003e for both \u003ca href=\"/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/ollama_portable_zip_quickstart.md#windows-quickstart\"\u003eWindows\u003c/a\u003e and \u003ca href=\"/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/ollama_portable_zip_quickstart.md#linux-quickstart\"\u003eLinux\u003c/a\u003e (\u003cem\u003e\u003cstrong\u003ewithout the need of manual installations\u003c/strong\u003e\u003c/em\u003e).\u003c/li\u003e\n\u003cli\u003e[2025/02] We added support for running \u003ca href=\"/intel/ipex-llm/blob/main/docs/mddocs/DockerGuides/vllm_docker_quickstart.md\"\u003evLLM 0.6.6\u003c/a\u003e on Intel Arc GPUs.\u003c/li\u003e\n\u003cli\u003e[2025/01] We added the guide for running \u003ccode\u003eipex-llm\u003c/code\u003e on Intel Arc \u003ca href=\"/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/bmg_quickstart.md\"\u003eB580\u003c/a\u003e GPU.\u003c/li\u003e\n\u003cli\u003e[2025/01] We added support for running \u003ca href=\"/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/ollama_quickstart.md\"\u003eOllama 0.5.4\u003c/a\u003e on Intel GPU.\u003c/li\u003e\n\u003cli\u003e[2024/12] We added both \u003cem\u003e\u003cstrong\u003ePython\u003c/strong\u003e\u003c/em\u003e and \u003cem\u003e\u003cstrong\u003eC++\u003c/strong\u003e\u003c/em\u003e support for Intel Core Ultra \u003ca href=\"/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/npu_quickstart.md\"\u003eNPU\u003c/a\u003e (including 100H, 200V, 200K and 200H series).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdetails\u003e\u003csummary\u003eMore updates\u003c/summary\u003e\n\u003cbr\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e[2024/11] We added support for running \u003ca href=\"/intel/ipex-llm/blob/main/docs/mddocs/DockerGuides/vllm_docker_quickstart.md\"\u003evLLM 0.6.2\u003c/a\u003e on Intel Arc GPUs.\u003c/li\u003e\n\u003cli\u003e[2024/07] We added support for running Microsoft's \u003cstrong\u003eGraphRAG\u003c/strong\u003e using local LLM on Intel GPU; see the quickstart guide \u003ca href=\"/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/graphrag_quickstart.md\"\u003ehere\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003e[2024/07] We added extensive support for Large Multimodal Models, including \u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/Multimodal/StableDiffusion\"\u003eStableDiffusion\u003c/a\u003e, \u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/Multimodal/phi-3-vision\"\u003ePhi-3-Vision\u003c/a\u003e, \u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/Multimodal/qwen-vl\"\u003eQwen-VL\u003c/a\u003e, and \u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/Multimodal\"\u003emore\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003e[2024/07] We added \u003cstrong\u003eFP6\u003c/strong\u003e support on Intel \u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/More-Data-Types\"\u003eGPU\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003e[2024/06] We added experimental \u003cstrong\u003eNPU\u003c/strong\u003e support for Intel Core Ultra processors; see the examples \u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/NPU/HF-Transformers-AutoModels\"\u003ehere\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003e[2024/06] We added extensive support of \u003cstrong\u003epipeline parallel\u003c/strong\u003e \u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/Pipeline-Parallel-Inference\"\u003einference\u003c/a\u003e, which makes it easy to run large-sized LLM using 2 or more Intel GPUs (such as Arc).\u003c/li\u003e\n\u003cli\u003e[2024/06] We added support for running \u003cstrong\u003eRAGFlow\u003c/strong\u003e with \u003ccode\u003eipex-llm\u003c/code\u003e on Intel \u003ca href=\"/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/ragflow_quickstart.md\"\u003eGPU\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003e[2024/05] \u003ccode\u003eipex-llm\u003c/code\u003e now supports \u003cstrong\u003eAxolotl\u003c/strong\u003e for LLM finetuning on Intel GPU; see the quickstart \u003ca href=\"/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/axolotl_quickstart.md\"\u003ehere\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003e[2024/05] You can now easily run \u003ccode\u003eipex-llm\u003c/code\u003e inference, serving and finetuning using the \u003cstrong\u003eDocker\u003c/strong\u003e \u003ca href=\"#docker\"\u003eimages\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003e[2024/05] You can now install \u003ccode\u003eipex-llm\u003c/code\u003e on Windows using just \"\u003cem\u003e\u003ca href=\"/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/install_windows_gpu.md#install-ipex-llm\"\u003eone command\u003c/a\u003e\u003c/em\u003e\".\u003c/li\u003e\n\u003cli\u003e[2024/04] You can now run \u003cstrong\u003eOpen WebUI\u003c/strong\u003e on Intel GPU using \u003ccode\u003eipex-llm\u003c/code\u003e; see the quickstart \u003ca href=\"/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/open_webui_with_ollama_quickstart.md\"\u003ehere\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003e[2024/04] You can now run \u003cstrong\u003eLlama 3\u003c/strong\u003e on Intel GPU using \u003ccode\u003ellama.cpp\u003c/code\u003e and \u003ccode\u003eollama\u003c/code\u003e with \u003ccode\u003eipex-llm\u003c/code\u003e; see the quickstart \u003ca href=\"/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/llama3_llamacpp_ollama_quickstart.md\"\u003ehere\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003e[2024/04] \u003ccode\u003eipex-llm\u003c/code\u003e now supports \u003cstrong\u003eLlama 3\u003c/strong\u003e on both Intel \u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/LLM/llama3\"\u003eGPU\u003c/a\u003e and \u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/llama3\"\u003eCPU\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003e[2024/04] \u003ccode\u003eipex-llm\u003c/code\u003e now provides C++ interface, which can be used as an accelerated backend for running \u003ca href=\"/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/llama_cpp_quickstart.md\"\u003ellama.cpp\u003c/a\u003e and \u003ca href=\"/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/ollama_quickstart.md\"\u003eollama\u003c/a\u003e on Intel GPU.\u003c/li\u003e\n\u003cli\u003e[2024/03] \u003ccode\u003ebigdl-llm\u003c/code\u003e has now become \u003ccode\u003eipex-llm\u003c/code\u003e (see the migration guide \u003ca href=\"/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/bigdl_llm_migration.md\"\u003ehere\u003c/a\u003e); you may find the original \u003ccode\u003eBigDL\u003c/code\u003e project \u003ca href=\"https://github.com/intel-analytics/bigdl-2.x\"\u003ehere\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003e[2024/02] \u003ccode\u003eipex-llm\u003c/code\u003e now supports directly loading model from \u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/ModelScope-Models\"\u003eModelScope\u003c/a\u003e (\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/CPU/ModelScope-Models\"\u003e魔搭\u003c/a\u003e).\u003c/li\u003e\n\u003cli\u003e[2024/02] \u003ccode\u003eipex-llm\u003c/code\u003e added initial \u003cstrong\u003eINT2\u003c/strong\u003e support (based on llama.cpp \u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/Advanced-Quantizations/GGUF-IQ2\"\u003eIQ2\u003c/a\u003e mechanism), which makes it possible to run large-sized LLM (e.g., Mixtral-8x7B) on Intel GPU with 16GB VRAM.\u003c/li\u003e\n\u003cli\u003e[2024/02] Users can now use \u003ccode\u003eipex-llm\u003c/code\u003e through \u003ca href=\"https://github.com/intel-analytics/text-generation-webui\"\u003eText-Generation-WebUI\u003c/a\u003e GUI.\u003c/li\u003e\n\u003cli\u003e[2024/02] \u003ccode\u003eipex-llm\u003c/code\u003e now supports \u003cem\u003e\u003ca href=\"/intel/ipex-llm/blob/main/docs/mddocs/Inference/Self_Speculative_Decoding.md\"\u003eSelf-Speculative Decoding\u003c/a\u003e\u003c/em\u003e, which in practice brings \u003cstrong\u003e~30% speedup\u003c/strong\u003e for FP16 and BF16 inference latency on Intel \u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/Speculative-Decoding\"\u003eGPU\u003c/a\u003e and \u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/CPU/Speculative-Decoding\"\u003eCPU\u003c/a\u003e respectively.\u003c/li\u003e\n\u003cli\u003e[2024/02] \u003ccode\u003eipex-llm\u003c/code\u003e now supports a comprehensive list of LLM \u003cstrong\u003efinetuning\u003c/strong\u003e on Intel GPU (including \u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/LLM-Finetuning/LoRA\"\u003eLoRA\u003c/a\u003e, \u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/LLM-Finetuning/QLoRA\"\u003eQLoRA\u003c/a\u003e, \u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/LLM-Finetuning/DPO\"\u003eDPO\u003c/a\u003e, \u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/LLM-Finetuning/QA-LoRA\"\u003eQA-LoRA\u003c/a\u003e and \u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/LLM-Finetuning/ReLora\"\u003eReLoRA\u003c/a\u003e).\u003c/li\u003e\n\u003cli\u003e[2024/01] Using \u003ccode\u003eipex-llm\u003c/code\u003e \u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/LLM-Finetuning/QLoRA\"\u003eQLoRA\u003c/a\u003e, we managed to finetune LLaMA2-7B in \u003cstrong\u003e21 minutes\u003c/strong\u003e and LLaMA2-70B in \u003cstrong\u003e3.14 hours\u003c/strong\u003e on 8 Intel Max 1550 GPU for \u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/LLM-Finetuning/QLoRA/alpaca-qlora\"\u003eStandford-Alpaca\u003c/a\u003e (see the blog \u003ca href=\"https://www.intel.com/content/www/us/en/developer/articles/technical/finetuning-llms-on-intel-gpus-using-bigdl-llm.html\" rel=\"nofollow\"\u003ehere\u003c/a\u003e).\u003c/li\u003e\n\u003cli\u003e[2023/12] \u003ccode\u003eipex-llm\u003c/code\u003e now supports \u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/LLM-Finetuning/ReLora\"\u003eReLoRA\u003c/a\u003e (see \u003cem\u003e\u003ca href=\"https://arxiv.org/abs/2307.05695\" rel=\"nofollow\"\u003e\"ReLoRA: High-Rank Training Through Low-Rank Updates\"\u003c/a\u003e\u003c/em\u003e).\u003c/li\u003e\n\u003cli\u003e[2023/12] \u003ccode\u003eipex-llm\u003c/code\u003e now supports \u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/LLM/mixtral\"\u003eMixtral-8x7B\u003c/a\u003e on both Intel \u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/LLM/mixtral\"\u003eGPU\u003c/a\u003e and \u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/mixtral\"\u003eCPU\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003e[2023/12] \u003ccode\u003eipex-llm\u003c/code\u003e now supports \u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/LLM-Finetuning/QA-LoRA\"\u003eQA-LoRA\u003c/a\u003e (see \u003cem\u003e\u003ca href=\"https://arxiv.org/abs/2309.14717\" rel=\"nofollow\"\u003e\"QA-LoRA: Quantization-Aware Low-Rank Adaptation of Large Language Models\"\u003c/a\u003e\u003c/em\u003e).\u003c/li\u003e\n\u003cli\u003e[2023/12] \u003ccode\u003eipex-llm\u003c/code\u003e now supports \u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/More-Data-Types\"\u003eFP8 and FP4 inference\u003c/a\u003e on Intel \u003cem\u003e\u003cstrong\u003eGPU\u003c/strong\u003e\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003e[2023/11] Initial support for directly loading \u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/Advanced-Quantizations/GGUF\"\u003eGGUF\u003c/a\u003e, \u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/Advanced-Quantizations/AWQ\"\u003eAWQ\u003c/a\u003e and \u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/Advanced-Quantizations/GPTQ\"\u003eGPTQ\u003c/a\u003e models into \u003ccode\u003eipex-llm\u003c/code\u003e is available.\u003c/li\u003e\n\u003cli\u003e[2023/11] \u003ccode\u003eipex-llm\u003c/code\u003e now supports \u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/vLLM-Serving\"\u003evLLM continuous batching\u003c/a\u003e on both Intel \u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/vLLM-Serving\"\u003eGPU\u003c/a\u003e and \u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/CPU/vLLM-Serving\"\u003eCPU\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003e[2023/10] \u003ccode\u003eipex-llm\u003c/code\u003e now supports \u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/LLM-Finetuning/QLoRA\"\u003eQLoRA finetuning\u003c/a\u003e on both Intel \u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/LLM-Finetuning/QLoRA\"\u003eGPU\u003c/a\u003e and \u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/CPU/QLoRA-FineTuning\"\u003eCPU\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003e[2023/10] \u003ccode\u003eipex-llm\u003c/code\u003e now supports \u003ca href=\"/intel/ipex-llm/blob/main/python/llm/src/ipex_llm/llm/serving\"\u003eFastChat serving\u003c/a\u003e on on both Intel CPU and GPU.\u003c/li\u003e\n\u003cli\u003e[2023/09] \u003ccode\u003eipex-llm\u003c/code\u003e now supports \u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU\"\u003eIntel GPU\u003c/a\u003e (including iGPU, Arc, Flex and MAX).\u003c/li\u003e\n\u003cli\u003e[2023/09] \u003ccode\u003eipex-llm\u003c/code\u003e \u003ca href=\"https://github.com/intel-analytics/ipex-llm-tutorial\"\u003etutorial\u003c/a\u003e is released.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/details\u003e \n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e\u003ccode\u003eipex-llm\u003c/code\u003e Demo\u003c/h2\u003e\u003ca id=\"user-content-ipex-llm-demo\" class=\"anchor\" aria-label=\"Permalink: ipex-llm Demo\" href=\"#ipex-llm-demo\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eSee demos of running local LLMs \u003cem\u003eon Intel Core Ultra iGPU, Intel Core Ultra NPU, single-card Arc GPU, or multi-card Arc GPUs\u003c/em\u003e using \u003ccode\u003eipex-llm\u003c/code\u003e below.\u003c/p\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable width=\"100%\"\u003e\n \u003ctbody\u003e\u003ctr\u003e\n \u003ctd align=\"center\" colspan=\"1\"\u003e\u003cstrong\u003eIntel Core Ultra iGPU\u003c/strong\u003e\u003c/td\u003e\n \u003ctd align=\"center\" colspan=\"1\"\u003e\u003cstrong\u003eIntel Core Ultra NPU\u003c/strong\u003e\u003c/td\u003e\n \u003ctd align=\"center\" colspan=\"1\"\u003e\u003cstrong\u003eIntel Arc dGPU\u003c/strong\u003e\u003c/td\u003e\n \u003ctd align=\"center\" colspan=\"1\"\u003e\u003cstrong\u003e2-Card Intel Arc dGPUs\u003c/strong\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003ca href=\"https://llm-assets.readthedocs.io/en/latest/_images/mtl_mistral-7B_q4_k_m_ollama.gif\" rel=\"nofollow\"\u003e\n \u003cimg src=\"https://camo.githubusercontent.com/911baff640f459b7f28583d6bfb4bba834cb41f2dc94e108a2189ee8ae161993/68747470733a2f2f6c6c6d2d6173736574732e72656164746865646f63732e696f2f656e2f6c61746573742f5f696d616765732f6d746c5f6d69737472616c2d37425f71345f6b5f6d5f6f6c6c616d612e676966\" width=\"100%;\" data-animated-image=\"\" data-canonical-src=\"https://llm-assets.readthedocs.io/en/latest/_images/mtl_mistral-7B_q4_k_m_ollama.gif\" style=\"max-width: 100%;\"\u003e\n \u003c/a\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003ca href=\"https://llm-assets.readthedocs.io/en/latest/_images/npu_llama3.2-3B.gif\" rel=\"nofollow\"\u003e\n \u003cimg src=\"https://camo.githubusercontent.com/8902b9dfd1cff5788f3ea9768f8e857515a2d0caa97a6d3ffe092379ea925ecd/68747470733a2f2f6c6c6d2d6173736574732e72656164746865646f63732e696f2f656e2f6c61746573742f5f696d616765732f6e70755f6c6c616d61332e322d33422e676966\" width=\"100%;\" data-animated-image=\"\" data-canonical-src=\"https://llm-assets.readthedocs.io/en/latest/_images/npu_llama3.2-3B.gif\" style=\"max-width: 100%;\"\u003e\n \u003c/a\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003ca href=\"https://llm-assets.readthedocs.io/en/latest/_images/arc_llama3-8B_fp8_textwebui.gif\" rel=\"nofollow\"\u003e\n \u003cimg src=\"https://camo.githubusercontent.com/7f185a93c27455efcb8fa22bb4c804b4fc9e0ead8a52a6da4b6481697b5107f1/68747470733a2f2f6c6c6d2d6173736574732e72656164746865646f63732e696f2f656e2f6c61746573742f5f696d616765732f6172635f6c6c616d61332d38425f6670385f7465787477656275692e676966\" width=\"100%;\" data-animated-image=\"\" data-canonical-src=\"https://llm-assets.readthedocs.io/en/latest/_images/arc_llama3-8B_fp8_textwebui.gif\" style=\"max-width: 100%;\"\u003e\n \u003c/a\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003ca href=\"https://llm-assets.readthedocs.io/en/latest/_images/2arc_DeepSeek-R1-Distill-Qwen-32B-Q4_K_M.gif\" rel=\"nofollow\"\u003e\n \u003cimg src=\"https://camo.githubusercontent.com/36200e0e3c3c827c9ac8ecca2716a8ba8eab514d659fcc112a767dc5511846c1/68747470733a2f2f6c6c6d2d6173736574732e72656164746865646f63732e696f2f656e2f6c61746573742f5f696d616765732f326172635f446565705365656b2d52312d44697374696c6c2d5177656e2d3332422d51345f4b5f4d2e676966\" width=\"100%;\" data-animated-image=\"\" data-canonical-src=\"https://llm-assets.readthedocs.io/en/latest/_images/2arc_DeepSeek-R1-Distill-Qwen-32B-Q4_K_M.gif\" style=\"max-width: 100%;\"\u003e\n \u003c/a\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"center\" width=\"25%\"\u003e\n \u003ca href=\"/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/ollama_portable_zip_quickstart.md\"\u003eOllama \u003cbr\u003e (Mistral-7B, Q4_K) \u003c/a\u003e\n \u003c/td\u003e\n \u003ctd align=\"center\" width=\"25%\"\u003e\n \u003ca href=\"/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/npu_quickstart.md\"\u003eHuggingFace \u003cbr\u003e (Llama3.2-3B, SYM_INT4)\u003c/a\u003e\n \u003c/td\u003e\n \u003ctd align=\"center\" width=\"25%\"\u003e\n \u003ca href=\"/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/webui_quickstart.md\"\u003eTextGeneration-WebUI \u003cbr\u003e (Llama3-8B, FP8) \u003c/a\u003e\n \u003c/td\u003e\n \u003ctd align=\"center\" width=\"25%\"\u003e\n \u003ca href=\"/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/llamacpp_portable_zip_gpu_quickstart.md\"\u003ellama.cpp \u003cbr\u003e (DeepSeek-R1-Distill-Qwen-32B, Q4_K)\u003c/a\u003e\n \u003c/td\u003e \u003c/tr\u003e\n\u003c/tbody\u003e\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e\u003ccode\u003eipex-llm\u003c/code\u003e Performance\u003c/h2\u003e\u003ca id=\"user-content-ipex-llm-performance\" class=\"anchor\" aria-label=\"Permalink: ipex-llm Performance\" href=\"#ipex-llm-performance\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eSee the \u003cstrong\u003eToken Generation Speed\u003c/strong\u003e on \u003cem\u003eIntel Core Ultra\u003c/em\u003e and \u003cem\u003eIntel Arc GPU\u003c/em\u003e below\u003csup\u003e\u003ca href=\"#user-content-fn-1-28711fa4219f09e11d411254f051326e\" id=\"user-content-fnref-1-2-28711fa4219f09e11d411254f051326e\" data-footnote-ref=\"\" aria-describedby=\"footnote-label\"\u003e1\u003c/a\u003e\u003c/sup\u003e (and refer to \u003ca href=\"https://www.intel.com/content/www/us/en/developer/articles/technical/accelerate-meta-llama3-with-intel-ai-solutions.html\" rel=\"nofollow\"\u003e[2]\u003c/a\u003e\u003ca href=\"https://www.intel.com/content/www/us/en/developer/articles/technical/accelerate-microsoft-phi-3-models-intel-ai-soln.html\" rel=\"nofollow\"\u003e[3]\u003c/a\u003e\u003ca href=\"https://www.intel.com/content/www/us/en/developer/articles/technical/intel-ai-solutions-accelerate-alibaba-qwen2-llms.html\" rel=\"nofollow\"\u003e[4]\u003c/a\u003e for more details).\u003c/p\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable width=\"100%\"\u003e\n \u003ctbody\u003e\u003ctr\u003e\n \u003ctd\u003e\n \u003ca href=\"https://llm-assets.readthedocs.io/en/latest/_images/MTL_perf.jpg\" rel=\"nofollow\"\u003e\n \u003cimg src=\"https://camo.githubusercontent.com/bcad46794e073bfb8babf50e817b7d7b2a8c75bd2f34f184fa71fedfd74c1258/68747470733a2f2f6c6c6d2d6173736574732e72656164746865646f63732e696f2f656e2f6c61746573742f5f696d616765732f4d544c5f706572662e6a7067\" width=\"100%;\" data-canonical-src=\"https://llm-assets.readthedocs.io/en/latest/_images/MTL_perf.jpg\" style=\"max-width: 100%;\"\u003e\n \u003c/a\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003ca href=\"https://llm-assets.readthedocs.io/en/latest/_images/Arc_perf.jpg\" rel=\"nofollow\"\u003e\n \u003cimg src=\"https://camo.githubusercontent.com/4bb238df1bf8607900f0ab66fc9796d39354b262bd279dbe2dba32df0f33c515/68747470733a2f2f6c6c6d2d6173736574732e72656164746865646f63732e696f2f656e2f6c61746573742f5f696d616765732f4172635f706572662e6a7067\" width=\"100%;\" data-canonical-src=\"https://llm-assets.readthedocs.io/en/latest/_images/Arc_perf.jpg\" style=\"max-width: 100%;\"\u003e\n \u003c/a\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n\u003c/tbody\u003e\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003cp dir=\"auto\"\u003eYou may follow the \u003ca href=\"/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/benchmark_quickstart.md\"\u003eBenchmarking Guide\u003c/a\u003e to run \u003ccode\u003eipex-llm\u003c/code\u003e performance benchmark yourself.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eModel Accuracy\u003c/h2\u003e\u003ca id=\"user-content-model-accuracy\" class=\"anchor\" aria-label=\"Permalink: Model Accuracy\" href=\"#model-accuracy\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003ePlease see the \u003cstrong\u003ePerplexity\u003c/strong\u003e result below (tested on Wikitext dataset using the script \u003ca href=\"https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/dev/benchmark/perplexity\"\u003ehere\u003c/a\u003e).\u003c/p\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003ePerplexity\u003c/th\u003e\n\u003cth\u003esym_int4\u003c/th\u003e\n\u003cth\u003eq4_k\u003c/th\u003e\n\u003cth\u003efp6\u003c/th\u003e\n\u003cth\u003efp8_e5m2\u003c/th\u003e\n\u003cth\u003efp8_e4m3\u003c/th\u003e\n\u003cth\u003efp16\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eLlama-2-7B-chat-hf\u003c/td\u003e\n\u003ctd\u003e6.364\u003c/td\u003e\n\u003ctd\u003e6.218\u003c/td\u003e\n\u003ctd\u003e6.092\u003c/td\u003e\n\u003ctd\u003e6.180\u003c/td\u003e\n\u003ctd\u003e6.098\u003c/td\u003e\n\u003ctd\u003e6.096\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMistral-7B-Instruct-v0.2\u003c/td\u003e\n\u003ctd\u003e5.365\u003c/td\u003e\n\u003ctd\u003e5.320\u003c/td\u003e\n\u003ctd\u003e5.270\u003c/td\u003e\n\u003ctd\u003e5.273\u003c/td\u003e\n\u003ctd\u003e5.246\u003c/td\u003e\n\u003ctd\u003e5.244\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eBaichuan2-7B-chat\u003c/td\u003e\n\u003ctd\u003e6.734\u003c/td\u003e\n\u003ctd\u003e6.727\u003c/td\u003e\n\u003ctd\u003e6.527\u003c/td\u003e\n\u003ctd\u003e6.539\u003c/td\u003e\n\u003ctd\u003e6.488\u003c/td\u003e\n\u003ctd\u003e6.508\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eQwen1.5-7B-chat\u003c/td\u003e\n\u003ctd\u003e8.865\u003c/td\u003e\n\u003ctd\u003e8.816\u003c/td\u003e\n\u003ctd\u003e8.557\u003c/td\u003e\n\u003ctd\u003e8.846\u003c/td\u003e\n\u003ctd\u003e8.530\u003c/td\u003e\n\u003ctd\u003e8.607\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eLlama-3.1-8B-Instruct\u003c/td\u003e\n\u003ctd\u003e6.705\u003c/td\u003e\n\u003ctd\u003e6.566\u003c/td\u003e\n\u003ctd\u003e6.338\u003c/td\u003e\n\u003ctd\u003e6.383\u003c/td\u003e\n\u003ctd\u003e6.325\u003c/td\u003e\n\u003ctd\u003e6.267\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003egemma-2-9b-it\u003c/td\u003e\n\u003ctd\u003e7.541\u003c/td\u003e\n\u003ctd\u003e7.412\u003c/td\u003e\n\u003ctd\u003e7.269\u003c/td\u003e\n\u003ctd\u003e7.380\u003c/td\u003e\n\u003ctd\u003e7.268\u003c/td\u003e\n\u003ctd\u003e7.270\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eBaichuan2-13B-Chat\u003c/td\u003e\n\u003ctd\u003e6.313\u003c/td\u003e\n\u003ctd\u003e6.160\u003c/td\u003e\n\u003ctd\u003e6.070\u003c/td\u003e\n\u003ctd\u003e6.145\u003c/td\u003e\n\u003ctd\u003e6.086\u003c/td\u003e\n\u003ctd\u003e6.031\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eLlama-2-13b-chat-hf\u003c/td\u003e\n\u003ctd\u003e5.449\u003c/td\u003e\n\u003ctd\u003e5.422\u003c/td\u003e\n\u003ctd\u003e5.341\u003c/td\u003e\n\u003ctd\u003e5.384\u003c/td\u003e\n\u003ctd\u003e5.332\u003c/td\u003e\n\u003ctd\u003e5.329\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eQwen1.5-14B-Chat\u003c/td\u003e\n\u003ctd\u003e7.529\u003c/td\u003e\n\u003ctd\u003e7.520\u003c/td\u003e\n\u003ctd\u003e7.367\u003c/td\u003e\n\u003ctd\u003e7.504\u003c/td\u003e\n\u003ctd\u003e7.297\u003c/td\u003e\n\u003ctd\u003e7.334\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e\u003ccode\u003eipex-llm\u003c/code\u003e Quickstart\u003c/h2\u003e\u003ca id=\"user-content-ipex-llm-quickstart\" class=\"anchor\" aria-label=\"Permalink: ipex-llm Quickstart\" href=\"#ipex-llm-quickstart\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eUse\u003c/h3\u003e\u003ca id=\"user-content-use\" class=\"anchor\" aria-label=\"Permalink: Use\" href=\"#use\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/ollama_portable_zip_quickstart.md\"\u003eOllama\u003c/a\u003e: running \u003cstrong\u003eOllama\u003c/strong\u003e on Intel GPU \u003cem\u003e\u003cstrong\u003ewithout the need of manual installations\u003c/strong\u003e\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/llamacpp_portable_zip_gpu_quickstart.md\"\u003ellama.cpp\u003c/a\u003e: running \u003cstrong\u003ellama.cpp\u003c/strong\u003e on Intel GPU \u003cem\u003e\u003cstrong\u003ewithout the need of manual installations\u003c/strong\u003e\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/bmg_quickstart.md\"\u003eArc B580\u003c/a\u003e: running \u003ccode\u003eipex-llm\u003c/code\u003e on Intel Arc \u003cstrong\u003eB580\u003c/strong\u003e GPU for Ollama, llama.cpp, PyTorch, HuggingFace, etc.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/npu_quickstart.md\"\u003eNPU\u003c/a\u003e: running \u003ccode\u003eipex-llm\u003c/code\u003e on Intel \u003cstrong\u003eNPU\u003c/strong\u003e in both Python/C++ or \u003ca href=\"/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/llama_cpp_npu_portable_zip_quickstart.md\"\u003ellama.cpp\u003c/a\u003e API.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/install_windows_gpu.md\"\u003ePyTorch/HuggingFace\u003c/a\u003e: running \u003cstrong\u003ePyTorch\u003c/strong\u003e, \u003cstrong\u003eHuggingFace\u003c/strong\u003e, \u003cstrong\u003eLangChain\u003c/strong\u003e, \u003cstrong\u003eLlamaIndex\u003c/strong\u003e, etc. (\u003cem\u003eusing Python interface of \u003ccode\u003eipex-llm\u003c/code\u003e\u003c/em\u003e) on Intel GPU for \u003ca href=\"/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/install_windows_gpu.md\"\u003eWindows\u003c/a\u003e and \u003ca href=\"/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/install_linux_gpu.md\"\u003eLinux\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/vLLM_quickstart.md\"\u003evLLM\u003c/a\u003e: running \u003ccode\u003eipex-llm\u003c/code\u003e in \u003cstrong\u003evLLM\u003c/strong\u003e on both Intel \u003ca href=\"/intel/ipex-llm/blob/main/docs/mddocs/DockerGuides/vllm_docker_quickstart.md\"\u003eGPU\u003c/a\u003e and \u003ca href=\"/intel/ipex-llm/blob/main/docs/mddocs/DockerGuides/vllm_cpu_docker_quickstart.md\"\u003eCPU\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/fastchat_quickstart.md\"\u003eFastChat\u003c/a\u003e: running \u003ccode\u003eipex-llm\u003c/code\u003e in \u003cstrong\u003eFastChat\u003c/strong\u003e serving on on both Intel GPU and CPU\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/deepspeed_autotp_fastapi_quickstart.md\"\u003eServing on multiple Intel GPUs\u003c/a\u003e: running \u003ccode\u003eipex-llm\u003c/code\u003e \u003cstrong\u003eserving on multiple Intel GPUs\u003c/strong\u003e by leveraging DeepSpeed AutoTP and FastAPI\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/webui_quickstart.md\"\u003eText-Generation-WebUI\u003c/a\u003e: running \u003ccode\u003eipex-llm\u003c/code\u003e in \u003ccode\u003eoobabooga\u003c/code\u003e \u003cstrong\u003eWebUI\u003c/strong\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/axolotl_quickstart.md\"\u003eAxolotl\u003c/a\u003e: running \u003ccode\u003eipex-llm\u003c/code\u003e in \u003cstrong\u003eAxolotl\u003c/strong\u003e for LLM finetuning\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/benchmark_quickstart.md\"\u003eBenchmarking\u003c/a\u003e: running (latency and throughput) \u003cstrong\u003ebenchmarks\u003c/strong\u003e for \u003ccode\u003eipex-llm\u003c/code\u003e on Intel CPU and GPU\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eDocker\u003c/h3\u003e\u003ca id=\"user-content-docker\" class=\"anchor\" aria-label=\"Permalink: Docker\" href=\"#docker\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"/intel/ipex-llm/blob/main/docs/mddocs/DockerGuides/docker_cpp_xpu_quickstart.md\"\u003eGPU Inference in C++\u003c/a\u003e: running \u003ccode\u003ellama.cpp\u003c/code\u003e, \u003ccode\u003eollama\u003c/code\u003e, etc., with \u003ccode\u003eipex-llm\u003c/code\u003e on Intel GPU\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/intel/ipex-llm/blob/main/docs/mddocs/DockerGuides/docker_pytorch_inference_gpu.md\"\u003eGPU Inference in Python\u003c/a\u003e : running HuggingFace \u003ccode\u003etransformers\u003c/code\u003e, \u003ccode\u003eLangChain\u003c/code\u003e, \u003ccode\u003eLlamaIndex\u003c/code\u003e, \u003ccode\u003eModelScope\u003c/code\u003e, etc. with \u003ccode\u003eipex-llm\u003c/code\u003e on Intel GPU\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/intel/ipex-llm/blob/main/docs/mddocs/DockerGuides/vllm_docker_quickstart.md\"\u003evLLM on GPU\u003c/a\u003e: running \u003ccode\u003evLLM\u003c/code\u003e serving with \u003ccode\u003eipex-llm\u003c/code\u003e on Intel GPU\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/intel/ipex-llm/blob/main/docs/mddocs/DockerGuides/vllm_cpu_docker_quickstart.md\"\u003evLLM on CPU\u003c/a\u003e: running \u003ccode\u003evLLM\u003c/code\u003e serving with \u003ccode\u003eipex-llm\u003c/code\u003e on Intel CPU\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/intel/ipex-llm/blob/main/docs/mddocs/DockerGuides/fastchat_docker_quickstart.md\"\u003eFastChat on GPU\u003c/a\u003e: running \u003ccode\u003eFastChat\u003c/code\u003e serving with \u003ccode\u003eipex-llm\u003c/code\u003e on Intel GPU\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/intel/ipex-llm/blob/main/docs/mddocs/DockerGuides/docker_run_pytorch_inference_in_vscode.md\"\u003eVSCode on GPU\u003c/a\u003e: running and developing \u003ccode\u003eipex-llm\u003c/code\u003e applications in Python using VSCode on Intel GPU\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eApplications\u003c/h3\u003e\u003ca id=\"user-content-applications\" class=\"anchor\" aria-label=\"Permalink: Applications\" href=\"#applications\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/graphrag_quickstart.md\"\u003eGraphRAG\u003c/a\u003e: running Microsoft's \u003ccode\u003eGraphRAG\u003c/code\u003e using local LLM with \u003ccode\u003eipex-llm\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/ragflow_quickstart.md\"\u003eRAGFlow\u003c/a\u003e: running \u003ccode\u003eRAGFlow\u003c/code\u003e (\u003cem\u003ean open-source RAG engine\u003c/em\u003e) with \u003ccode\u003eipex-llm\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/chatchat_quickstart.md\"\u003eLangChain-Chatchat\u003c/a\u003e: running \u003ccode\u003eLangChain-Chatchat\u003c/code\u003e (\u003cem\u003eKnowledge Base QA using RAG pipeline\u003c/em\u003e) with \u003ccode\u003eipex-llm\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/continue_quickstart.md\"\u003eCoding copilot\u003c/a\u003e: running \u003ccode\u003eContinue\u003c/code\u003e (coding copilot in VSCode) with \u003ccode\u003eipex-llm\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/open_webui_with_ollama_quickstart.md\"\u003eOpen WebUI\u003c/a\u003e: running \u003ccode\u003eOpen WebUI\u003c/code\u003e with \u003ccode\u003eipex-llm\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/privateGPT_quickstart.md\"\u003ePrivateGPT\u003c/a\u003e: running \u003ccode\u003ePrivateGPT\u003c/code\u003e to interact with documents with \u003ccode\u003eipex-llm\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/dify_quickstart.md\"\u003eDify platform\u003c/a\u003e: running \u003ccode\u003eipex-llm\u003c/code\u003e in \u003ccode\u003eDify\u003c/code\u003e(\u003cem\u003eproduction-ready LLM app development platform\u003c/em\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eInstall\u003c/h3\u003e\u003ca id=\"user-content-install\" class=\"anchor\" aria-label=\"Permalink: Install\" href=\"#install\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/install_windows_gpu.md\"\u003eWindows GPU\u003c/a\u003e: installing \u003ccode\u003eipex-llm\u003c/code\u003e on Windows with Intel GPU\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/install_linux_gpu.md\"\u003eLinux GPU\u003c/a\u003e: installing \u003ccode\u003eipex-llm\u003c/code\u003e on Linux with Intel GPU\u003c/li\u003e\n\u003cli\u003e\u003cem\u003eFor more details, please refer to the \u003ca href=\"/intel/ipex-llm/blob/main/docs/mddocs/Overview/install.md\"\u003efull installation guide\u003c/a\u003e\u003c/em\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCode Examples\u003c/h3\u003e\u003ca id=\"user-content-code-examples\" class=\"anchor\" aria-label=\"Permalink: Code Examples\" href=\"#code-examples\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eLow bit inference\u003c/h4\u003e\u003ca id=\"user-content-low-bit-inference\" class=\"anchor\" aria-label=\"Permalink: Low bit inference\" href=\"#low-bit-inference\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/LLM\"\u003eINT4 inference\u003c/a\u003e: \u003cstrong\u003eINT4\u003c/strong\u003e LLM inference on Intel \u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/LLM\"\u003eGPU\u003c/a\u003e and \u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model\"\u003eCPU\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/More-Data-Types\"\u003eFP8/FP6/FP4 inference\u003c/a\u003e: \u003cstrong\u003eFP8\u003c/strong\u003e, \u003cstrong\u003eFP6\u003c/strong\u003e and \u003cstrong\u003eFP4\u003c/strong\u003e LLM inference on Intel \u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/More-Data-Types\"\u003eGPU\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/More-Data-Types\"\u003eINT8 inference\u003c/a\u003e: \u003cstrong\u003eINT8\u003c/strong\u003e LLM inference on Intel \u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/More-Data-Types\"\u003eGPU\u003c/a\u003e and \u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/More-Data-Types\"\u003eCPU\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/Advanced-Quantizations/GGUF-IQ2\"\u003eINT2 inference\u003c/a\u003e: \u003cstrong\u003eINT2\u003c/strong\u003e LLM inference (based on llama.cpp IQ2 mechanism) on Intel \u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/Advanced-Quantizations/GGUF-IQ2\"\u003eGPU\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eFP16/BF16 inference\u003c/h4\u003e\u003ca id=\"user-content-fp16bf16-inference\" class=\"anchor\" aria-label=\"Permalink: FP16/BF16 inference\" href=\"#fp16bf16-inference\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003cstrong\u003eFP16\u003c/strong\u003e LLM inference on Intel \u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/Speculative-Decoding\"\u003eGPU\u003c/a\u003e, with possible \u003ca href=\"/intel/ipex-llm/blob/main/docs/mddocs/Inference/Self_Speculative_Decoding.md\"\u003eself-speculative decoding\u003c/a\u003e optimization\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eBF16\u003c/strong\u003e LLM inference on Intel \u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/CPU/Speculative-Decoding\"\u003eCPU\u003c/a\u003e, with possible \u003ca href=\"/intel/ipex-llm/blob/main/docs/mddocs/Inference/Self_Speculative_Decoding.md\"\u003eself-speculative decoding\u003c/a\u003e optimization\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eDistributed inference\u003c/h4\u003e\u003ca id=\"user-content-distributed-inference\" class=\"anchor\" aria-label=\"Permalink: Distributed inference\" href=\"#distributed-inference\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003cstrong\u003ePipeline Parallel\u003c/strong\u003e inference on Intel \u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/Pipeline-Parallel-Inference\"\u003eGPU\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eDeepSpeed AutoTP\u003c/strong\u003e inference on Intel \u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/Deepspeed-AutoTP\"\u003eGPU\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eSave and load\u003c/h4\u003e\u003ca id=\"user-content-save-and-load\" class=\"anchor\" aria-label=\"Permalink: Save and load\" href=\"#save-and-load\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Save-Load\"\u003eLow-bit models\u003c/a\u003e: saving and loading \u003ccode\u003eipex-llm\u003c/code\u003e low-bit models (INT4/FP4/FP6/INT8/FP8/FP16/etc.)\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/Advanced-Quantizations/GGUF\"\u003eGGUF\u003c/a\u003e: directly loading GGUF models into \u003ccode\u003eipex-llm\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/Advanced-Quantizations/AWQ\"\u003eAWQ\u003c/a\u003e: directly loading AWQ models into \u003ccode\u003eipex-llm\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/Advanced-Quantizations/GPTQ\"\u003eGPTQ\u003c/a\u003e: directly loading GPTQ models into \u003ccode\u003eipex-llm\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eFinetuning\u003c/h4\u003e\u003ca id=\"user-content-finetuning\" class=\"anchor\" aria-label=\"Permalink: Finetuning\" href=\"#finetuning\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eLLM finetuning on Intel \u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/LLM-Finetuning\"\u003eGPU\u003c/a\u003e, including \u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/LLM-Finetuning/LoRA\"\u003eLoRA\u003c/a\u003e, \u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/LLM-Finetuning/QLoRA\"\u003eQLoRA\u003c/a\u003e, \u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/LLM-Finetuning/DPO\"\u003eDPO\u003c/a\u003e, \u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/LLM-Finetuning/QA-LoRA\"\u003eQA-LoRA\u003c/a\u003e and \u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/LLM-Finetuning/ReLora\"\u003eReLoRA\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eQLoRA finetuning on Intel \u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/CPU/QLoRA-FineTuning\"\u003eCPU\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eIntegration with community libraries\u003c/h4\u003e\u003ca id=\"user-content-integration-with-community-libraries\" class=\"anchor\" aria-label=\"Permalink: Integration with community libraries\" href=\"#integration-with-community-libraries\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace\"\u003eHuggingFace transformers\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/PyTorch-Models\"\u003eStandard PyTorch model\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/LangChain\"\u003eLangChain\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/LlamaIndex\"\u003eLlamaIndex\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/Deepspeed-AutoTP\"\u003eDeepSpeed-AutoTP\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/axolotl_quickstart.md\"\u003eAxolotl\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/LLM-Finetuning/HF-PEFT\"\u003eHuggingFace PEFT\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/LLM-Finetuning/DPO\"\u003eHuggingFace TRL\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/CPU/Applications/autogen\"\u003eAutoGen\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/ModelScope-Models\"\u003eModeScope\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/intel-analytics/ipex-llm-tutorial\"\u003eTutorials\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eAPI Doc\u003c/h2\u003e\u003ca id=\"user-content-api-doc\" class=\"anchor\" aria-label=\"Permalink: API Doc\" href=\"#api-doc\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"/intel/ipex-llm/blob/main/docs/mddocs/PythonAPI/transformers.md\"\u003eHuggingFace Transformers-style API (Auto Classes)\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/intel-analytics/ipex-llm/blob/main/docs/mddocs/PythonAPI/optimize.md\"\u003eAPI for arbitrary PyTorch Model\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eFAQ\u003c/h2\u003e\u003ca id=\"user-content-faq\" class=\"anchor\" aria-label=\"Permalink: FAQ\" href=\"#faq\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"/intel/ipex-llm/blob/main/docs/mddocs/Overview/FAQ/faq.md\"\u003eFAQ \u0026amp; Trouble Shooting\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eVerified Models\u003c/h2\u003e\u003ca id=\"user-content-verified-models\" class=\"anchor\" aria-label=\"Permalink: Verified Models\" href=\"#verified-models\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eOver 70 models have been optimized/verified on \u003ccode\u003eipex-llm\u003c/code\u003e, including \u003cem\u003eLLaMA/LLaMA2, Mistral, Mixtral, Gemma, LLaVA, Whisper, ChatGLM2/ChatGLM3, Baichuan/Baichuan2, Qwen/Qwen-1.5, InternLM\u003c/em\u003e and more; see the list below.\u003c/p\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eModel\u003c/th\u003e\n\u003cth\u003eCPU Example\u003c/th\u003e\n\u003cth\u003eGPU Example\u003c/th\u003e\n\u003cth\u003eNPU Example\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eLLaMA\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/CPU/Native-Models\"\u003elink1\u003c/a\u003e, \u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/vicuna\"\u003elink2\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/LLM/vicuna\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eLLaMA 2\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/CPU/Native-Models\"\u003elink1\u003c/a\u003e, \u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/llama2\"\u003elink2\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/LLM/llama2\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/NPU/HF-Transformers-AutoModels/LLM\"\u003ePython link\u003c/a\u003e, \u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/NPU/HF-Transformers-AutoModels/LLM/CPP_Examples\"\u003eC++ link\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eLLaMA 3\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/llama3\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/LLM/llama3\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/NPU/HF-Transformers-AutoModels/LLM\"\u003ePython link\u003c/a\u003e, \u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/NPU/HF-Transformers-AutoModels/LLM/CPP_Examples\"\u003eC++ link\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eLLaMA 3.1\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/llama3.1\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/LLM/llama3.1\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eLLaMA 3.2\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/LLM/llama3.2\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/NPU/HF-Transformers-AutoModels/LLM\"\u003ePython link\u003c/a\u003e, \u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/NPU/HF-Transformers-AutoModels/LLM/CPP_Examples\"\u003eC++ link\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eLLaMA 3.2-Vision\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/PyTorch-Models/Model/llama3.2-vision\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eChatGLM\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/chatglm\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eChatGLM2\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/chatglm2\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/LLM/chatglm2\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eChatGLM3\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/chatglm3\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/LLM/chatglm3\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eGLM-4\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/glm4\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/LLM/glm4\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eGLM-4V\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/glm-4v\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/Multimodal/glm-4v\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eGLM-Edge\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/LLM/glm-edge\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/NPU/HF-Transformers-AutoModels/LLM\"\u003ePython link\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eGLM-Edge-V\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/Multimodal/glm-edge-v\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMistral\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/mistral\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/LLM/mistral\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMixtral\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/mixtral\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/LLM/mixtral\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eFalcon\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/falcon\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/LLM/falcon\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMPT\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/mpt\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/LLM/mpt\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDolly-v1\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/dolly_v1\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/LLM/dolly-v1\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDolly-v2\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/dolly_v2\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/LLM/dolly-v2\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eReplit Code\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/replit\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/LLM/replit\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eRedPajama\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/CPU/Native-Models\"\u003elink1\u003c/a\u003e, \u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/redpajama\"\u003elink2\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePhoenix\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/CPU/Native-Models\"\u003elink1\u003c/a\u003e, \u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/phoenix\"\u003elink2\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eStarCoder\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/CPU/Native-Models\"\u003elink1\u003c/a\u003e, \u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/starcoder\"\u003elink2\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/LLM/starcoder\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eBaichuan\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/baichuan\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/LLM/baichuan\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eBaichuan2\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/baichuan2\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/LLM/baichuan2\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/NPU/HF-Transformers-AutoModels/LLM\"\u003ePython link\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eInternLM\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/internlm\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/LLM/internlm\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eInternVL2\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/Multimodal/internvl2\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eQwen\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/qwen\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/LLM/qwen\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eQwen1.5\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/qwen1.5\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/LLM/qwen1.5\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eQwen2\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/qwen2\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/LLM/qwen2\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/NPU/HF-Transformers-AutoModels/LLM\"\u003ePython link\u003c/a\u003e, \u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/NPU/HF-Transformers-AutoModels/LLM/CPP_Examples\"\u003eC++ link\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eQwen2.5\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/LLM/qwen2.5\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/NPU/HF-Transformers-AutoModels/LLM\"\u003ePython link\u003c/a\u003e, \u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/NPU/HF-Transformers-AutoModels/LLM/CPP_Examples\"\u003eC++ link\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eQwen-VL\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/qwen-vl\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/Multimodal/qwen-vl\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eQwen2-VL\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/Multimodal/qwen2-vl\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eQwen2-Audio\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/Multimodal/qwen2-audio\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eAquila\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/aquila\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/LLM/aquila\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eAquila2\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/aquila2\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/LLM/aquila2\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMOSS\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/moss\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWhisper\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/whisper\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/Multimodal/whisper\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePhi-1_5\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/phi-1_5\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/LLM/phi-1_5\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eFlan-t5\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/flan-t5\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/LLM/flan-t5\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eLLaVA\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/CPU/PyTorch-Models/Model/llava\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca 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href=\"/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/deepseek-moe\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eZiya-Coding-34B-v1.0\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/ziya\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePhi-2\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/phi-2\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/LLM/phi-2\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePhi-3\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/phi-3\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/LLM/phi-3\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePhi-3-vision\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/phi-3-vision\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca 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href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/LLM/gemma\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eGemma2\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/LLM/gemma2\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDeciLM-7B\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/deciLM-7b\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/LLM/deciLM-7b\"\u003elink\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDeepseek\u003c/td\u003e\n\u003ctd\u003e\u003ca 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3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003ePlease report a bug or raise a feature request by opening a \u003ca href=\"https://github.com/intel-analytics/ipex-llm/issues\"\u003eGithub Issue\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003ePlease report a vulnerability by opening a draft \u003ca href=\"https://github.com/intel-analytics/ipex-llm/security/advisories\"\u003eGitHub Security Advisory\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003csection data-footnotes=\"\" class=\"footnotes\"\u003e\u003ch2 id=\"footnote-label\" class=\"sr-only\" dir=\"auto\"\u003eFootnotes\u003c/h2\u003e\n\u003col dir=\"auto\"\u003e\n\u003cli id=\"user-content-fn-1-28711fa4219f09e11d411254f051326e\"\u003e\n\u003cp dir=\"auto\"\u003ePerformance varies by use, configuration and other factors. \u003ccode\u003eipex-llm\u003c/code\u003e may not optimize to the same degree for non-Intel products. 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Demo","anchor":"ipex-llm-demo","htmlText":"ipex-llm Demo"},{"level":2,"text":"ipex-llm Performance","anchor":"ipex-llm-performance","htmlText":"ipex-llm Performance"},{"level":2,"text":"Model Accuracy","anchor":"model-accuracy","htmlText":"Model Accuracy"},{"level":2,"text":"ipex-llm Quickstart","anchor":"ipex-llm-quickstart","htmlText":"ipex-llm Quickstart"},{"level":3,"text":"Use","anchor":"use","htmlText":"Use"},{"level":3,"text":"Docker","anchor":"docker","htmlText":"Docker"},{"level":3,"text":"Applications","anchor":"applications","htmlText":"Applications"},{"level":3,"text":"Install","anchor":"install","htmlText":"Install"},{"level":3,"text":"Code Examples","anchor":"code-examples","htmlText":"Code Examples"},{"level":4,"text":"Low bit inference","anchor":"low-bit-inference","htmlText":"Low bit inference"},{"level":4,"text":"FP16/BF16 inference","anchor":"fp16bf16-inference","htmlText":"FP16/BF16 inference"},{"level":4,"text":"Distributed 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aria-describedby=":Rr9ab:-loading-announcement" id=":Rr9ab:"><svg aria-hidden="true" focusable="false" class="octicon octicon-list-unordered" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" style="display:inline-block;user-select:none;vertical-align:text-bottom;overflow:visible"><path d="M5.75 2.5h8.5a.75.75 0 0 1 0 1.5h-8.5a.75.75 0 0 1 0-1.5Zm0 5h8.5a.75.75 0 0 1 0 1.5h-8.5a.75.75 0 0 1 0-1.5Zm0 5h8.5a.75.75 0 0 1 0 1.5h-8.5a.75.75 0 0 1 0-1.5ZM2 14a1 1 0 1 1 0-2 1 1 0 0 1 0 2Zm1-6a1 1 0 1 1-2 0 1 1 0 0 1 2 0ZM2 4a1 1 0 1 1 0-2 1 1 0 0 1 0 2Z"></path></svg></button></div><div class="Box-sc-g0xbh4-0 QkQOb js-snippet-clipboard-copy-unpositioned undefined" data-hpc="true"><article class="markdown-body entry-content container-lg" itemprop="text"><div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">💫 Intel® LLM Library for PyTorch*</h1><a id="user-content--intel-llm-library-for-pytorch" class="anchor" aria-label="Permalink: 💫 Intel® LLM Library for PyTorch*" href="#-intel-llm-library-for-pytorch"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"> <b>< English</b> | <a href="/intel/ipex-llm/blob/main/README.zh-CN.md">中文</a> > </p> <p dir="auto"><strong><code>IPEX-LLM</code></strong> is an LLM acceleration library for Intel <a href="/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/install_windows_gpu.md">GPU</a> <em>(e.g., local PC with iGPU, discrete GPU such as Arc, Flex and Max)</em>, <a href="/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/npu_quickstart.md">NPU</a> and CPU <sup><a href="#user-content-fn-1-28711fa4219f09e11d411254f051326e" id="user-content-fnref-1-28711fa4219f09e11d411254f051326e" data-footnote-ref="" aria-describedby="footnote-label">1</a></sup>.</p> <div class="markdown-alert markdown-alert-note" dir="auto"><p class="markdown-alert-title" dir="auto"><svg class="octicon octicon-info mr-2" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="M0 8a8 8 0 1 1 16 0A8 8 0 0 1 0 8Zm8-6.5a6.5 6.5 0 1 0 0 13 6.5 6.5 0 0 0 0-13ZM6.5 7.75A.75.75 0 0 1 7.25 7h1a.75.75 0 0 1 .75.75v2.75h.25a.75.75 0 0 1 0 1.5h-2a.75.75 0 0 1 0-1.5h.25v-2h-.25a.75.75 0 0 1-.75-.75ZM8 6a1 1 0 1 1 0-2 1 1 0 0 1 0 2Z"></path></svg>Note</p> <ul dir="auto"> <li><em><code>IPEX-LLM</code> provides seamless integration with <a href="/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/llamacpp_portable_zip_gpu_quickstart.md">llama.cpp</a>, <a href="/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/ollama_portable_zip_quickstart.md">Ollama</a>, <a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace">HuggingFace transformers</a>, <a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/LangChain">LangChain</a>, <a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/LlamaIndex">LlamaIndex</a>, <a href="/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/vLLM_quickstart.md">vLLM</a>, <a href="/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/webui_quickstart.md">Text-Generation-WebUI</a>, <a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/Deepspeed-AutoTP">DeepSpeed-AutoTP</a>, <a href="/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/fastchat_quickstart.md">FastChat</a>, <a href="/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/axolotl_quickstart.md">Axolotl</a>, <a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/LLM-Finetuning">HuggingFace PEFT</a>, <a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/LLM-Finetuning/DPO">HuggingFace TRL</a>, <a href="/intel/ipex-llm/blob/main/python/llm/example/CPU/Applications/autogen">AutoGen</a>, <a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/ModelScope-Models">ModeScope</a>, etc.</em></li> <li><em><strong>70+ models</strong> have been optimized/verified on <code>ipex-llm</code> (e.g., Llama, Phi, Mistral, Mixtral, DeepSeek, Qwen, ChatGLM, MiniCPM, Qwen-VL, MiniCPM-V and more), with state-of-art <strong>LLM optimizations</strong>, <strong>XPU acceleration</strong> and <strong>low-bit (FP8/FP6/FP4/INT4) support</strong>; see the complete list <a href="#verified-models">here</a>.</em></li> </ul> </div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Latest Update 🔥</h2><a id="user-content-latest-update-" class="anchor" aria-label="Permalink: Latest Update 🔥" href="#latest-update-"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li>[2025/03] We added support for <strong>Gemma3</strong> model in the latest <a href="https://github.com/intel/ipex-llm/issues/12963#issuecomment-2724032898" data-hovercard-type="issue" data-hovercard-url="/intel/ipex-llm/issues/12963/hovercard">llama.cpp Portable Zip</a>.</li> <li>[2025/03] We can now run <strong>DeepSeek-R1-671B-Q4_K_M</strong> with 1 or 2 Arc A770 on Xeon using the latest <a href="/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/llamacpp_portable_zip_gpu_quickstart.md#flashmoe-for-deepseek-v3r1">llama.cpp Portable Zip</a>.</li> <li>[2025/02] We added support of <a href="https://github.com/intel/ipex-llm/releases/tag/v2.2.0-nightly">llama.cpp Portable Zip</a> for Intel <strong>GPU</strong> (both <a href="/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/llamacpp_portable_zip_gpu_quickstart.md#windows-quickstart">Windows</a> and <a href="/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/llamacpp_portable_zip_gpu_quickstart.md#linux-quickstart">Linux</a>) and <strong>NPU</strong> (<a href="/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/llama_cpp_npu_portable_zip_quickstart.md">Windows</a> only).</li> <li>[2025/02] We added support of <a href="https://github.com/intel/ipex-llm/releases/tag/v2.2.0-nightly">Ollama Portable Zip</a> to directly run Ollama on Intel <strong>GPU</strong> for both <a href="/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/ollama_portable_zip_quickstart.md#windows-quickstart">Windows</a> and <a href="/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/ollama_portable_zip_quickstart.md#linux-quickstart">Linux</a> (<em><strong>without the need of manual installations</strong></em>).</li> <li>[2025/02] We added support for running <a href="/intel/ipex-llm/blob/main/docs/mddocs/DockerGuides/vllm_docker_quickstart.md">vLLM 0.6.6</a> on Intel Arc GPUs.</li> <li>[2025/01] We added the guide for running <code>ipex-llm</code> on Intel Arc <a href="/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/bmg_quickstart.md">B580</a> GPU.</li> <li>[2025/01] We added support for running <a href="/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/ollama_quickstart.md">Ollama 0.5.4</a> on Intel GPU.</li> <li>[2024/12] We added both <em><strong>Python</strong></em> and <em><strong>C++</strong></em> support for Intel Core Ultra <a href="/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/npu_quickstart.md">NPU</a> (including 100H, 200V, 200K and 200H series).</li> </ul> <details><summary>More updates</summary> <br> <ul dir="auto"> <li>[2024/11] We added support for running <a href="/intel/ipex-llm/blob/main/docs/mddocs/DockerGuides/vllm_docker_quickstart.md">vLLM 0.6.2</a> on Intel Arc GPUs.</li> <li>[2024/07] We added support for running Microsoft's <strong>GraphRAG</strong> using local LLM on Intel GPU; see the quickstart guide <a href="/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/graphrag_quickstart.md">here</a>.</li> <li>[2024/07] We added extensive support for Large Multimodal Models, including <a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/Multimodal/StableDiffusion">StableDiffusion</a>, <a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/Multimodal/phi-3-vision">Phi-3-Vision</a>, <a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/Multimodal/qwen-vl">Qwen-VL</a>, and <a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/Multimodal">more</a>.</li> <li>[2024/07] We added <strong>FP6</strong> support on Intel <a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/More-Data-Types">GPU</a>.</li> <li>[2024/06] We added experimental <strong>NPU</strong> support for Intel Core Ultra processors; see the examples <a href="/intel/ipex-llm/blob/main/python/llm/example/NPU/HF-Transformers-AutoModels">here</a>.</li> <li>[2024/06] We added extensive support of <strong>pipeline parallel</strong> <a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/Pipeline-Parallel-Inference">inference</a>, which makes it easy to run large-sized LLM using 2 or more Intel GPUs (such as Arc).</li> <li>[2024/06] We added support for running <strong>RAGFlow</strong> with <code>ipex-llm</code> on Intel <a href="/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/ragflow_quickstart.md">GPU</a>.</li> <li>[2024/05] <code>ipex-llm</code> now supports <strong>Axolotl</strong> for LLM finetuning on Intel GPU; see the quickstart <a href="/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/axolotl_quickstart.md">here</a>.</li> <li>[2024/05] You can now easily run <code>ipex-llm</code> inference, serving and finetuning using the <strong>Docker</strong> <a href="#docker">images</a>.</li> <li>[2024/05] You can now install <code>ipex-llm</code> on Windows using just "<em><a href="/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/install_windows_gpu.md#install-ipex-llm">one command</a></em>".</li> <li>[2024/04] You can now run <strong>Open WebUI</strong> on Intel GPU using <code>ipex-llm</code>; see the quickstart <a href="/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/open_webui_with_ollama_quickstart.md">here</a>.</li> <li>[2024/04] You can now run <strong>Llama 3</strong> on Intel GPU using <code>llama.cpp</code> and <code>ollama</code> with <code>ipex-llm</code>; see the quickstart <a href="/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/llama3_llamacpp_ollama_quickstart.md">here</a>.</li> <li>[2024/04] <code>ipex-llm</code> now supports <strong>Llama 3</strong> on both Intel <a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/LLM/llama3">GPU</a> and <a href="/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/llama3">CPU</a>.</li> <li>[2024/04] <code>ipex-llm</code> now provides C++ interface, which can be used as an accelerated backend for running <a href="/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/llama_cpp_quickstart.md">llama.cpp</a> and <a href="/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/ollama_quickstart.md">ollama</a> on Intel GPU.</li> <li>[2024/03] <code>bigdl-llm</code> has now become <code>ipex-llm</code> (see the migration guide <a href="/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/bigdl_llm_migration.md">here</a>); you may find the original <code>BigDL</code> project <a href="https://github.com/intel-analytics/bigdl-2.x">here</a>.</li> <li>[2024/02] <code>ipex-llm</code> now supports directly loading model from <a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/ModelScope-Models">ModelScope</a> (<a href="/intel/ipex-llm/blob/main/python/llm/example/CPU/ModelScope-Models">魔搭</a>).</li> <li>[2024/02] <code>ipex-llm</code> added initial <strong>INT2</strong> support (based on llama.cpp <a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/Advanced-Quantizations/GGUF-IQ2">IQ2</a> mechanism), which makes it possible to run large-sized LLM (e.g., Mixtral-8x7B) on Intel GPU with 16GB VRAM.</li> <li>[2024/02] Users can now use <code>ipex-llm</code> through <a href="https://github.com/intel-analytics/text-generation-webui">Text-Generation-WebUI</a> GUI.</li> <li>[2024/02] <code>ipex-llm</code> now supports <em><a href="/intel/ipex-llm/blob/main/docs/mddocs/Inference/Self_Speculative_Decoding.md">Self-Speculative Decoding</a></em>, which in practice brings <strong>~30% speedup</strong> for FP16 and BF16 inference latency on Intel <a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/Speculative-Decoding">GPU</a> and <a href="/intel/ipex-llm/blob/main/python/llm/example/CPU/Speculative-Decoding">CPU</a> respectively.</li> <li>[2024/02] <code>ipex-llm</code> now supports a comprehensive list of LLM <strong>finetuning</strong> on Intel GPU (including <a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/LLM-Finetuning/LoRA">LoRA</a>, <a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/LLM-Finetuning/QLoRA">QLoRA</a>, <a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/LLM-Finetuning/DPO">DPO</a>, <a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/LLM-Finetuning/QA-LoRA">QA-LoRA</a> and <a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/LLM-Finetuning/ReLora">ReLoRA</a>).</li> <li>[2024/01] Using <code>ipex-llm</code> <a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/LLM-Finetuning/QLoRA">QLoRA</a>, we managed to finetune LLaMA2-7B in <strong>21 minutes</strong> and LLaMA2-70B in <strong>3.14 hours</strong> on 8 Intel Max 1550 GPU for <a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/LLM-Finetuning/QLoRA/alpaca-qlora">Standford-Alpaca</a> (see the blog <a href="https://www.intel.com/content/www/us/en/developer/articles/technical/finetuning-llms-on-intel-gpus-using-bigdl-llm.html" rel="nofollow">here</a>).</li> <li>[2023/12] <code>ipex-llm</code> now supports <a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/LLM-Finetuning/ReLora">ReLoRA</a> (see <em><a href="https://arxiv.org/abs/2307.05695" rel="nofollow">"ReLoRA: High-Rank Training Through Low-Rank Updates"</a></em>).</li> <li>[2023/12] <code>ipex-llm</code> now supports <a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/LLM/mixtral">Mixtral-8x7B</a> on both Intel <a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/LLM/mixtral">GPU</a> and <a href="/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/mixtral">CPU</a>.</li> <li>[2023/12] <code>ipex-llm</code> now supports <a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/LLM-Finetuning/QA-LoRA">QA-LoRA</a> (see <em><a href="https://arxiv.org/abs/2309.14717" rel="nofollow">"QA-LoRA: Quantization-Aware Low-Rank Adaptation of Large Language Models"</a></em>).</li> <li>[2023/12] <code>ipex-llm</code> now supports <a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/More-Data-Types">FP8 and FP4 inference</a> on Intel <em><strong>GPU</strong></em>.</li> <li>[2023/11] Initial support for directly loading <a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/Advanced-Quantizations/GGUF">GGUF</a>, <a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/Advanced-Quantizations/AWQ">AWQ</a> and <a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/Advanced-Quantizations/GPTQ">GPTQ</a> models into <code>ipex-llm</code> is available.</li> <li>[2023/11] <code>ipex-llm</code> now supports <a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/vLLM-Serving">vLLM continuous batching</a> on both Intel <a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/vLLM-Serving">GPU</a> and <a href="/intel/ipex-llm/blob/main/python/llm/example/CPU/vLLM-Serving">CPU</a>.</li> <li>[2023/10] <code>ipex-llm</code> now supports <a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/LLM-Finetuning/QLoRA">QLoRA finetuning</a> on both Intel <a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/LLM-Finetuning/QLoRA">GPU</a> and <a href="/intel/ipex-llm/blob/main/python/llm/example/CPU/QLoRA-FineTuning">CPU</a>.</li> <li>[2023/10] <code>ipex-llm</code> now supports <a href="/intel/ipex-llm/blob/main/python/llm/src/ipex_llm/llm/serving">FastChat serving</a> on on both Intel CPU and GPU.</li> <li>[2023/09] <code>ipex-llm</code> now supports <a href="/intel/ipex-llm/blob/main/python/llm/example/GPU">Intel GPU</a> (including iGPU, Arc, Flex and MAX).</li> <li>[2023/09] <code>ipex-llm</code> <a href="https://github.com/intel-analytics/ipex-llm-tutorial">tutorial</a> is released.</li> </ul> </details> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto"><code>ipex-llm</code> Demo</h2><a id="user-content-ipex-llm-demo" class="anchor" aria-label="Permalink: ipex-llm Demo" href="#ipex-llm-demo"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">See demos of running local LLMs <em>on Intel Core Ultra iGPU, Intel Core Ultra NPU, single-card Arc GPU, or multi-card Arc GPUs</em> using <code>ipex-llm</code> below.</p> <markdown-accessiblity-table><table width="100%"> <tbody><tr> <td align="center" colspan="1"><strong>Intel Core Ultra iGPU</strong></td> <td align="center" colspan="1"><strong>Intel Core Ultra NPU</strong></td> <td align="center" colspan="1"><strong>Intel Arc dGPU</strong></td> <td align="center" colspan="1"><strong>2-Card Intel Arc dGPUs</strong></td> </tr> <tr> <td> <a href="https://llm-assets.readthedocs.io/en/latest/_images/mtl_mistral-7B_q4_k_m_ollama.gif" rel="nofollow"> <img src="https://camo.githubusercontent.com/911baff640f459b7f28583d6bfb4bba834cb41f2dc94e108a2189ee8ae161993/68747470733a2f2f6c6c6d2d6173736574732e72656164746865646f63732e696f2f656e2f6c61746573742f5f696d616765732f6d746c5f6d69737472616c2d37425f71345f6b5f6d5f6f6c6c616d612e676966" width="100%;" data-animated-image="" data-canonical-src="https://llm-assets.readthedocs.io/en/latest/_images/mtl_mistral-7B_q4_k_m_ollama.gif" style="max-width: 100%;"> </a> </td> <td> <a href="https://llm-assets.readthedocs.io/en/latest/_images/npu_llama3.2-3B.gif" rel="nofollow"> <img src="https://camo.githubusercontent.com/8902b9dfd1cff5788f3ea9768f8e857515a2d0caa97a6d3ffe092379ea925ecd/68747470733a2f2f6c6c6d2d6173736574732e72656164746865646f63732e696f2f656e2f6c61746573742f5f696d616765732f6e70755f6c6c616d61332e322d33422e676966" width="100%;" data-animated-image="" data-canonical-src="https://llm-assets.readthedocs.io/en/latest/_images/npu_llama3.2-3B.gif" style="max-width: 100%;"> </a> </td> <td> <a href="https://llm-assets.readthedocs.io/en/latest/_images/arc_llama3-8B_fp8_textwebui.gif" rel="nofollow"> <img src="https://camo.githubusercontent.com/7f185a93c27455efcb8fa22bb4c804b4fc9e0ead8a52a6da4b6481697b5107f1/68747470733a2f2f6c6c6d2d6173736574732e72656164746865646f63732e696f2f656e2f6c61746573742f5f696d616765732f6172635f6c6c616d61332d38425f6670385f7465787477656275692e676966" width="100%;" data-animated-image="" data-canonical-src="https://llm-assets.readthedocs.io/en/latest/_images/arc_llama3-8B_fp8_textwebui.gif" style="max-width: 100%;"> </a> </td> <td> <a href="https://llm-assets.readthedocs.io/en/latest/_images/2arc_DeepSeek-R1-Distill-Qwen-32B-Q4_K_M.gif" rel="nofollow"> <img src="https://camo.githubusercontent.com/36200e0e3c3c827c9ac8ecca2716a8ba8eab514d659fcc112a767dc5511846c1/68747470733a2f2f6c6c6d2d6173736574732e72656164746865646f63732e696f2f656e2f6c61746573742f5f696d616765732f326172635f446565705365656b2d52312d44697374696c6c2d5177656e2d3332422d51345f4b5f4d2e676966" width="100%;" data-animated-image="" data-canonical-src="https://llm-assets.readthedocs.io/en/latest/_images/2arc_DeepSeek-R1-Distill-Qwen-32B-Q4_K_M.gif" style="max-width: 100%;"> </a> </td> </tr> <tr> <td align="center" width="25%"> <a href="/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/ollama_portable_zip_quickstart.md">Ollama <br> (Mistral-7B, Q4_K) </a> </td> <td align="center" width="25%"> <a href="/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/npu_quickstart.md">HuggingFace <br> (Llama3.2-3B, SYM_INT4)</a> </td> <td align="center" width="25%"> <a href="/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/webui_quickstart.md">TextGeneration-WebUI <br> (Llama3-8B, FP8) </a> </td> <td align="center" width="25%"> <a href="/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/llamacpp_portable_zip_gpu_quickstart.md">llama.cpp <br> (DeepSeek-R1-Distill-Qwen-32B, Q4_K)</a> </td> </tr> </tbody></table></markdown-accessiblity-table> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto"><code>ipex-llm</code> Performance</h2><a id="user-content-ipex-llm-performance" class="anchor" aria-label="Permalink: ipex-llm Performance" href="#ipex-llm-performance"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">See the <strong>Token Generation Speed</strong> on <em>Intel Core Ultra</em> and <em>Intel Arc GPU</em> below<sup><a href="#user-content-fn-1-28711fa4219f09e11d411254f051326e" id="user-content-fnref-1-2-28711fa4219f09e11d411254f051326e" data-footnote-ref="" aria-describedby="footnote-label">1</a></sup> (and refer to <a href="https://www.intel.com/content/www/us/en/developer/articles/technical/accelerate-meta-llama3-with-intel-ai-solutions.html" rel="nofollow">[2]</a><a href="https://www.intel.com/content/www/us/en/developer/articles/technical/accelerate-microsoft-phi-3-models-intel-ai-soln.html" rel="nofollow">[3]</a><a href="https://www.intel.com/content/www/us/en/developer/articles/technical/intel-ai-solutions-accelerate-alibaba-qwen2-llms.html" rel="nofollow">[4]</a> for more details).</p> <markdown-accessiblity-table><table width="100%"> <tbody><tr> <td> <a href="https://llm-assets.readthedocs.io/en/latest/_images/MTL_perf.jpg" rel="nofollow"> <img src="https://camo.githubusercontent.com/bcad46794e073bfb8babf50e817b7d7b2a8c75bd2f34f184fa71fedfd74c1258/68747470733a2f2f6c6c6d2d6173736574732e72656164746865646f63732e696f2f656e2f6c61746573742f5f696d616765732f4d544c5f706572662e6a7067" width="100%;" data-canonical-src="https://llm-assets.readthedocs.io/en/latest/_images/MTL_perf.jpg" style="max-width: 100%;"> </a> </td> <td> <a href="https://llm-assets.readthedocs.io/en/latest/_images/Arc_perf.jpg" rel="nofollow"> <img src="https://camo.githubusercontent.com/4bb238df1bf8607900f0ab66fc9796d39354b262bd279dbe2dba32df0f33c515/68747470733a2f2f6c6c6d2d6173736574732e72656164746865646f63732e696f2f656e2f6c61746573742f5f696d616765732f4172635f706572662e6a7067" width="100%;" data-canonical-src="https://llm-assets.readthedocs.io/en/latest/_images/Arc_perf.jpg" style="max-width: 100%;"> </a> </td> </tr> </tbody></table></markdown-accessiblity-table> <p dir="auto">You may follow the <a href="/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/benchmark_quickstart.md">Benchmarking Guide</a> to run <code>ipex-llm</code> performance benchmark yourself.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Model Accuracy</h2><a id="user-content-model-accuracy" class="anchor" aria-label="Permalink: Model Accuracy" href="#model-accuracy"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Please see the <strong>Perplexity</strong> result below (tested on Wikitext dataset using the script <a href="https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/dev/benchmark/perplexity">here</a>).</p> <markdown-accessiblity-table><table> <thead> <tr> <th>Perplexity</th> <th>sym_int4</th> <th>q4_k</th> <th>fp6</th> <th>fp8_e5m2</th> <th>fp8_e4m3</th> <th>fp16</th> </tr> </thead> <tbody> <tr> <td>Llama-2-7B-chat-hf</td> <td>6.364</td> <td>6.218</td> <td>6.092</td> <td>6.180</td> <td>6.098</td> <td>6.096</td> </tr> <tr> <td>Mistral-7B-Instruct-v0.2</td> <td>5.365</td> <td>5.320</td> <td>5.270</td> <td>5.273</td> <td>5.246</td> <td>5.244</td> </tr> <tr> <td>Baichuan2-7B-chat</td> <td>6.734</td> <td>6.727</td> <td>6.527</td> <td>6.539</td> <td>6.488</td> <td>6.508</td> </tr> <tr> <td>Qwen1.5-7B-chat</td> <td>8.865</td> <td>8.816</td> <td>8.557</td> <td>8.846</td> <td>8.530</td> <td>8.607</td> </tr> <tr> <td>Llama-3.1-8B-Instruct</td> <td>6.705</td> <td>6.566</td> <td>6.338</td> <td>6.383</td> <td>6.325</td> <td>6.267</td> </tr> <tr> <td>gemma-2-9b-it</td> <td>7.541</td> <td>7.412</td> <td>7.269</td> <td>7.380</td> <td>7.268</td> <td>7.270</td> </tr> <tr> <td>Baichuan2-13B-Chat</td> <td>6.313</td> <td>6.160</td> <td>6.070</td> <td>6.145</td> <td>6.086</td> <td>6.031</td> </tr> <tr> <td>Llama-2-13b-chat-hf</td> <td>5.449</td> <td>5.422</td> <td>5.341</td> <td>5.384</td> <td>5.332</td> <td>5.329</td> </tr> <tr> <td>Qwen1.5-14B-Chat</td> <td>7.529</td> <td>7.520</td> <td>7.367</td> <td>7.504</td> <td>7.297</td> <td>7.334</td> </tr> </tbody> </table></markdown-accessiblity-table> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto"><code>ipex-llm</code> Quickstart</h2><a id="user-content-ipex-llm-quickstart" class="anchor" aria-label="Permalink: ipex-llm Quickstart" href="#ipex-llm-quickstart"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Use</h3><a id="user-content-use" class="anchor" aria-label="Permalink: Use" href="#use"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li><a href="/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/ollama_portable_zip_quickstart.md">Ollama</a>: running <strong>Ollama</strong> on Intel GPU <em><strong>without the need of manual installations</strong></em></li> <li><a href="/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/llamacpp_portable_zip_gpu_quickstart.md">llama.cpp</a>: running <strong>llama.cpp</strong> on Intel GPU <em><strong>without the need of manual installations</strong></em></li> <li><a href="/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/bmg_quickstart.md">Arc B580</a>: running <code>ipex-llm</code> on Intel Arc <strong>B580</strong> GPU for Ollama, llama.cpp, PyTorch, HuggingFace, etc.</li> <li><a href="/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/npu_quickstart.md">NPU</a>: running <code>ipex-llm</code> on Intel <strong>NPU</strong> in both Python/C++ or <a href="/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/llama_cpp_npu_portable_zip_quickstart.md">llama.cpp</a> API.</li> <li><a href="/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/install_windows_gpu.md">PyTorch/HuggingFace</a>: running <strong>PyTorch</strong>, <strong>HuggingFace</strong>, <strong>LangChain</strong>, <strong>LlamaIndex</strong>, etc. (<em>using Python interface of <code>ipex-llm</code></em>) on Intel GPU for <a href="/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/install_windows_gpu.md">Windows</a> and <a href="/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/install_linux_gpu.md">Linux</a></li> <li><a href="/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/vLLM_quickstart.md">vLLM</a>: running <code>ipex-llm</code> in <strong>vLLM</strong> on both Intel <a href="/intel/ipex-llm/blob/main/docs/mddocs/DockerGuides/vllm_docker_quickstart.md">GPU</a> and <a href="/intel/ipex-llm/blob/main/docs/mddocs/DockerGuides/vllm_cpu_docker_quickstart.md">CPU</a></li> <li><a href="/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/fastchat_quickstart.md">FastChat</a>: running <code>ipex-llm</code> in <strong>FastChat</strong> serving on on both Intel GPU and CPU</li> <li><a href="/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/deepspeed_autotp_fastapi_quickstart.md">Serving on multiple Intel GPUs</a>: running <code>ipex-llm</code> <strong>serving on multiple Intel GPUs</strong> by leveraging DeepSpeed AutoTP and FastAPI</li> <li><a href="/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/webui_quickstart.md">Text-Generation-WebUI</a>: running <code>ipex-llm</code> in <code>oobabooga</code> <strong>WebUI</strong></li> <li><a href="/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/axolotl_quickstart.md">Axolotl</a>: running <code>ipex-llm</code> in <strong>Axolotl</strong> for LLM finetuning</li> <li><a href="/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/benchmark_quickstart.md">Benchmarking</a>: running (latency and throughput) <strong>benchmarks</strong> for <code>ipex-llm</code> on Intel CPU and GPU</li> </ul> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Docker</h3><a id="user-content-docker" class="anchor" aria-label="Permalink: Docker" href="#docker"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li><a href="/intel/ipex-llm/blob/main/docs/mddocs/DockerGuides/docker_cpp_xpu_quickstart.md">GPU Inference in C++</a>: running <code>llama.cpp</code>, <code>ollama</code>, etc., with <code>ipex-llm</code> on Intel GPU</li> <li><a href="/intel/ipex-llm/blob/main/docs/mddocs/DockerGuides/docker_pytorch_inference_gpu.md">GPU Inference in Python</a> : running HuggingFace <code>transformers</code>, <code>LangChain</code>, <code>LlamaIndex</code>, <code>ModelScope</code>, etc. with <code>ipex-llm</code> on Intel GPU</li> <li><a href="/intel/ipex-llm/blob/main/docs/mddocs/DockerGuides/vllm_docker_quickstart.md">vLLM on GPU</a>: running <code>vLLM</code> serving with <code>ipex-llm</code> on Intel GPU</li> <li><a href="/intel/ipex-llm/blob/main/docs/mddocs/DockerGuides/vllm_cpu_docker_quickstart.md">vLLM on CPU</a>: running <code>vLLM</code> serving with <code>ipex-llm</code> on Intel CPU</li> <li><a href="/intel/ipex-llm/blob/main/docs/mddocs/DockerGuides/fastchat_docker_quickstart.md">FastChat on GPU</a>: running <code>FastChat</code> serving with <code>ipex-llm</code> on Intel GPU</li> <li><a href="/intel/ipex-llm/blob/main/docs/mddocs/DockerGuides/docker_run_pytorch_inference_in_vscode.md">VSCode on GPU</a>: running and developing <code>ipex-llm</code> applications in Python using VSCode on Intel GPU</li> </ul> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Applications</h3><a id="user-content-applications" class="anchor" aria-label="Permalink: Applications" href="#applications"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li><a href="/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/graphrag_quickstart.md">GraphRAG</a>: running Microsoft's <code>GraphRAG</code> using local LLM with <code>ipex-llm</code></li> <li><a href="/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/ragflow_quickstart.md">RAGFlow</a>: running <code>RAGFlow</code> (<em>an open-source RAG engine</em>) with <code>ipex-llm</code></li> <li><a href="/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/chatchat_quickstart.md">LangChain-Chatchat</a>: running <code>LangChain-Chatchat</code> (<em>Knowledge Base QA using RAG pipeline</em>) with <code>ipex-llm</code></li> <li><a href="/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/continue_quickstart.md">Coding copilot</a>: running <code>Continue</code> (coding copilot in VSCode) with <code>ipex-llm</code></li> <li><a href="/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/open_webui_with_ollama_quickstart.md">Open WebUI</a>: running <code>Open WebUI</code> with <code>ipex-llm</code></li> <li><a href="/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/privateGPT_quickstart.md">PrivateGPT</a>: running <code>PrivateGPT</code> to interact with documents with <code>ipex-llm</code></li> <li><a href="/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/dify_quickstart.md">Dify platform</a>: running <code>ipex-llm</code> in <code>Dify</code>(<em>production-ready LLM app development platform</em>)</li> </ul> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Install</h3><a id="user-content-install" class="anchor" aria-label="Permalink: Install" href="#install"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li><a href="/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/install_windows_gpu.md">Windows GPU</a>: installing <code>ipex-llm</code> on Windows with Intel GPU</li> <li><a href="/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/install_linux_gpu.md">Linux GPU</a>: installing <code>ipex-llm</code> on Linux with Intel GPU</li> <li><em>For more details, please refer to the <a href="/intel/ipex-llm/blob/main/docs/mddocs/Overview/install.md">full installation guide</a></em></li> </ul> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Code Examples</h3><a id="user-content-code-examples" class="anchor" aria-label="Permalink: Code Examples" href="#code-examples"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">Low bit inference</h4><a id="user-content-low-bit-inference" class="anchor" aria-label="Permalink: Low bit inference" href="#low-bit-inference"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li><a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/LLM">INT4 inference</a>: <strong>INT4</strong> LLM inference on Intel <a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/LLM">GPU</a> and <a href="/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model">CPU</a></li> <li><a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/More-Data-Types">FP8/FP6/FP4 inference</a>: <strong>FP8</strong>, <strong>FP6</strong> and <strong>FP4</strong> LLM inference on Intel <a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/More-Data-Types">GPU</a></li> <li><a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/More-Data-Types">INT8 inference</a>: <strong>INT8</strong> LLM inference on Intel <a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/More-Data-Types">GPU</a> and <a href="/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/More-Data-Types">CPU</a></li> <li><a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/Advanced-Quantizations/GGUF-IQ2">INT2 inference</a>: <strong>INT2</strong> LLM inference (based on llama.cpp IQ2 mechanism) on Intel <a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/Advanced-Quantizations/GGUF-IQ2">GPU</a></li> </ul> </li> <li> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">FP16/BF16 inference</h4><a id="user-content-fp16bf16-inference" class="anchor" aria-label="Permalink: FP16/BF16 inference" href="#fp16bf16-inference"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li><strong>FP16</strong> LLM inference on Intel <a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/Speculative-Decoding">GPU</a>, with possible <a href="/intel/ipex-llm/blob/main/docs/mddocs/Inference/Self_Speculative_Decoding.md">self-speculative decoding</a> optimization</li> <li><strong>BF16</strong> LLM inference on Intel <a href="/intel/ipex-llm/blob/main/python/llm/example/CPU/Speculative-Decoding">CPU</a>, with possible <a href="/intel/ipex-llm/blob/main/docs/mddocs/Inference/Self_Speculative_Decoding.md">self-speculative decoding</a> optimization</li> </ul> </li> <li> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">Distributed inference</h4><a id="user-content-distributed-inference" class="anchor" aria-label="Permalink: Distributed inference" href="#distributed-inference"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li><strong>Pipeline Parallel</strong> inference on Intel <a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/Pipeline-Parallel-Inference">GPU</a></li> <li><strong>DeepSpeed AutoTP</strong> inference on Intel <a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/Deepspeed-AutoTP">GPU</a></li> </ul> </li> <li> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">Save and load</h4><a id="user-content-save-and-load" class="anchor" aria-label="Permalink: Save and load" href="#save-and-load"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li><a href="/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Save-Load">Low-bit models</a>: saving and loading <code>ipex-llm</code> low-bit models (INT4/FP4/FP6/INT8/FP8/FP16/etc.)</li> <li><a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/Advanced-Quantizations/GGUF">GGUF</a>: directly loading GGUF models into <code>ipex-llm</code></li> <li><a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/Advanced-Quantizations/AWQ">AWQ</a>: directly loading AWQ models into <code>ipex-llm</code></li> <li><a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/Advanced-Quantizations/GPTQ">GPTQ</a>: directly loading GPTQ models into <code>ipex-llm</code></li> </ul> </li> <li> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">Finetuning</h4><a id="user-content-finetuning" class="anchor" aria-label="Permalink: Finetuning" href="#finetuning"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li>LLM finetuning on Intel <a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/LLM-Finetuning">GPU</a>, including <a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/LLM-Finetuning/LoRA">LoRA</a>, <a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/LLM-Finetuning/QLoRA">QLoRA</a>, <a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/LLM-Finetuning/DPO">DPO</a>, <a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/LLM-Finetuning/QA-LoRA">QA-LoRA</a> and <a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/LLM-Finetuning/ReLora">ReLoRA</a></li> <li>QLoRA finetuning on Intel <a href="/intel/ipex-llm/blob/main/python/llm/example/CPU/QLoRA-FineTuning">CPU</a></li> </ul> </li> <li> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">Integration with community libraries</h4><a id="user-content-integration-with-community-libraries" class="anchor" aria-label="Permalink: Integration with community libraries" href="#integration-with-community-libraries"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li><a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace">HuggingFace transformers</a></li> <li><a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/PyTorch-Models">Standard PyTorch model</a></li> <li><a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/LangChain">LangChain</a></li> <li><a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/LlamaIndex">LlamaIndex</a></li> <li><a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/Deepspeed-AutoTP">DeepSpeed-AutoTP</a></li> <li><a href="/intel/ipex-llm/blob/main/docs/mddocs/Quickstart/axolotl_quickstart.md">Axolotl</a></li> <li><a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/LLM-Finetuning/HF-PEFT">HuggingFace PEFT</a></li> <li><a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/LLM-Finetuning/DPO">HuggingFace TRL</a></li> <li><a href="/intel/ipex-llm/blob/main/python/llm/example/CPU/Applications/autogen">AutoGen</a></li> <li><a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/ModelScope-Models">ModeScope</a></li> </ul> </li> <li><a href="https://github.com/intel-analytics/ipex-llm-tutorial">Tutorials</a></li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">API Doc</h2><a id="user-content-api-doc" class="anchor" aria-label="Permalink: API Doc" href="#api-doc"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li><a href="/intel/ipex-llm/blob/main/docs/mddocs/PythonAPI/transformers.md">HuggingFace Transformers-style API (Auto Classes)</a></li> <li><a href="https://github.com/intel-analytics/ipex-llm/blob/main/docs/mddocs/PythonAPI/optimize.md">API for arbitrary PyTorch Model</a></li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">FAQ</h2><a id="user-content-faq" class="anchor" aria-label="Permalink: FAQ" href="#faq"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li><a href="/intel/ipex-llm/blob/main/docs/mddocs/Overview/FAQ/faq.md">FAQ & Trouble Shooting</a></li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Verified Models</h2><a id="user-content-verified-models" class="anchor" aria-label="Permalink: Verified Models" href="#verified-models"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Over 70 models have been optimized/verified on <code>ipex-llm</code>, including <em>LLaMA/LLaMA2, Mistral, Mixtral, Gemma, LLaVA, Whisper, ChatGLM2/ChatGLM3, Baichuan/Baichuan2, Qwen/Qwen-1.5, InternLM</em> and more; see the list below.</p> <markdown-accessiblity-table><table> <thead> <tr> <th>Model</th> <th>CPU Example</th> <th>GPU Example</th> <th>NPU Example</th> </tr> </thead> <tbody> <tr> <td>LLaMA</td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/CPU/Native-Models">link1</a>, <a href="/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/vicuna">link2</a></td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/LLM/vicuna">link</a></td> <td></td> </tr> <tr> <td>LLaMA 2</td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/CPU/Native-Models">link1</a>, <a href="/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/llama2">link2</a></td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/LLM/llama2">link</a></td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/NPU/HF-Transformers-AutoModels/LLM">Python link</a>, <a href="/intel/ipex-llm/blob/main/python/llm/example/NPU/HF-Transformers-AutoModels/LLM/CPP_Examples">C++ link</a></td> </tr> <tr> <td>LLaMA 3</td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/llama3">link</a></td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/LLM/llama3">link</a></td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/NPU/HF-Transformers-AutoModels/LLM">Python link</a>, <a href="/intel/ipex-llm/blob/main/python/llm/example/NPU/HF-Transformers-AutoModels/LLM/CPP_Examples">C++ link</a></td> </tr> <tr> <td>LLaMA 3.1</td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/llama3.1">link</a></td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/LLM/llama3.1">link</a></td> <td></td> </tr> <tr> <td>LLaMA 3.2</td> <td></td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/LLM/llama3.2">link</a></td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/NPU/HF-Transformers-AutoModels/LLM">Python link</a>, <a href="/intel/ipex-llm/blob/main/python/llm/example/NPU/HF-Transformers-AutoModels/LLM/CPP_Examples">C++ link</a></td> </tr> <tr> <td>LLaMA 3.2-Vision</td> <td></td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/PyTorch-Models/Model/llama3.2-vision">link</a></td> <td></td> </tr> <tr> <td>ChatGLM</td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/chatglm">link</a></td> <td></td> <td></td> </tr> <tr> <td>ChatGLM2</td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/chatglm2">link</a></td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/LLM/chatglm2">link</a></td> <td></td> </tr> <tr> <td>ChatGLM3</td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/chatglm3">link</a></td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/LLM/chatglm3">link</a></td> <td></td> </tr> <tr> <td>GLM-4</td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/glm4">link</a></td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/LLM/glm4">link</a></td> <td></td> </tr> <tr> <td>GLM-4V</td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/glm-4v">link</a></td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/Multimodal/glm-4v">link</a></td> <td></td> </tr> <tr> <td>GLM-Edge</td> <td></td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/LLM/glm-edge">link</a></td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/NPU/HF-Transformers-AutoModels/LLM">Python link</a></td> </tr> <tr> <td>GLM-Edge-V</td> <td></td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/Multimodal/glm-edge-v">link</a></td> <td></td> </tr> <tr> <td>Mistral</td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/mistral">link</a></td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/LLM/mistral">link</a></td> <td></td> </tr> <tr> <td>Mixtral</td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/mixtral">link</a></td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/LLM/mixtral">link</a></td> <td></td> </tr> <tr> <td>Falcon</td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/falcon">link</a></td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/LLM/falcon">link</a></td> <td></td> </tr> <tr> <td>MPT</td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/mpt">link</a></td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/LLM/mpt">link</a></td> <td></td> </tr> <tr> <td>Dolly-v1</td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/dolly_v1">link</a></td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/LLM/dolly-v1">link</a></td> <td></td> </tr> <tr> <td>Dolly-v2</td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/dolly_v2">link</a></td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/LLM/dolly-v2">link</a></td> <td></td> </tr> <tr> <td>Replit Code</td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/replit">link</a></td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/LLM/replit">link</a></td> <td></td> </tr> <tr> <td>RedPajama</td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/CPU/Native-Models">link1</a>, <a href="/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/redpajama">link2</a></td> <td></td> <td></td> </tr> <tr> <td>Phoenix</td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/CPU/Native-Models">link1</a>, <a href="/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/phoenix">link2</a></td> <td></td> <td></td> </tr> <tr> <td>StarCoder</td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/CPU/Native-Models">link1</a>, <a href="/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/starcoder">link2</a></td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/LLM/starcoder">link</a></td> <td></td> </tr> <tr> <td>Baichuan</td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/baichuan">link</a></td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/LLM/baichuan">link</a></td> <td></td> </tr> <tr> <td>Baichuan2</td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/baichuan2">link</a></td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/LLM/baichuan2">link</a></td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/NPU/HF-Transformers-AutoModels/LLM">Python link</a></td> </tr> <tr> <td>InternLM</td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/internlm">link</a></td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/LLM/internlm">link</a></td> <td></td> </tr> <tr> <td>InternVL2</td> <td></td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/Multimodal/internvl2">link</a></td> <td></td> </tr> <tr> <td>Qwen</td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/qwen">link</a></td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/LLM/qwen">link</a></td> <td></td> </tr> <tr> <td>Qwen1.5</td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/qwen1.5">link</a></td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/LLM/qwen1.5">link</a></td> <td></td> </tr> <tr> <td>Qwen2</td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/qwen2">link</a></td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/LLM/qwen2">link</a></td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/NPU/HF-Transformers-AutoModels/LLM">Python link</a>, <a href="/intel/ipex-llm/blob/main/python/llm/example/NPU/HF-Transformers-AutoModels/LLM/CPP_Examples">C++ link</a></td> </tr> <tr> <td>Qwen2.5</td> <td></td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/LLM/qwen2.5">link</a></td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/NPU/HF-Transformers-AutoModels/LLM">Python link</a>, <a href="/intel/ipex-llm/blob/main/python/llm/example/NPU/HF-Transformers-AutoModels/LLM/CPP_Examples">C++ link</a></td> </tr> <tr> <td>Qwen-VL</td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/qwen-vl">link</a></td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/Multimodal/qwen-vl">link</a></td> <td></td> </tr> <tr> <td>Qwen2-VL</td> <td></td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/Multimodal/qwen2-vl">link</a></td> <td></td> </tr> <tr> <td>Qwen2-Audio</td> <td></td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/Multimodal/qwen2-audio">link</a></td> <td></td> </tr> <tr> <td>Aquila</td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/aquila">link</a></td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/LLM/aquila">link</a></td> <td></td> </tr> <tr> <td>Aquila2</td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/aquila2">link</a></td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/LLM/aquila2">link</a></td> <td></td> </tr> <tr> <td>MOSS</td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/moss">link</a></td> <td></td> <td></td> </tr> <tr> <td>Whisper</td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/whisper">link</a></td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/Multimodal/whisper">link</a></td> <td></td> </tr> <tr> <td>Phi-1_5</td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/phi-1_5">link</a></td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/LLM/phi-1_5">link</a></td> <td></td> </tr> <tr> <td>Flan-t5</td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/flan-t5">link</a></td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/LLM/flan-t5">link</a></td> <td></td> </tr> <tr> <td>LLaVA</td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/CPU/PyTorch-Models/Model/llava">link</a></td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/PyTorch-Models/Model/llava">link</a></td> <td></td> </tr> <tr> <td>CodeLlama</td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/codellama">link</a></td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/LLM/codellama">link</a></td> <td></td> </tr> <tr> <td>Skywork</td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/skywork">link</a></td> <td></td> <td></td> </tr> <tr> <td>InternLM-XComposer</td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/internlm-xcomposer">link</a></td> <td></td> <td></td> </tr> <tr> <td>WizardCoder-Python</td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/wizardcoder-python">link</a></td> <td></td> <td></td> </tr> <tr> <td>CodeShell</td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/codeshell">link</a></td> <td></td> <td></td> </tr> <tr> <td>Fuyu</td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/fuyu">link</a></td> <td></td> <td></td> </tr> <tr> <td>Distil-Whisper</td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/distil-whisper">link</a></td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/Multimodal/distil-whisper">link</a></td> <td></td> </tr> <tr> <td>Yi</td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/yi">link</a></td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/LLM/yi">link</a></td> <td></td> </tr> <tr> <td>BlueLM</td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/bluelm">link</a></td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/LLM/bluelm">link</a></td> <td></td> </tr> <tr> <td>Mamba</td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/CPU/PyTorch-Models/Model/mamba">link</a></td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/PyTorch-Models/Model/mamba">link</a></td> <td></td> </tr> <tr> <td>SOLAR</td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/solar">link</a></td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/LLM/solar">link</a></td> <td></td> </tr> <tr> <td>Phixtral</td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/phixtral">link</a></td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/LLM/phixtral">link</a></td> <td></td> </tr> <tr> <td>InternLM2</td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/internlm2">link</a></td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/LLM/internlm2">link</a></td> <td></td> </tr> <tr> <td>RWKV4</td> <td></td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/LLM/rwkv4">link</a></td> <td></td> </tr> <tr> <td>RWKV5</td> <td></td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/LLM/rwkv5">link</a></td> <td></td> </tr> <tr> <td>Bark</td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/CPU/PyTorch-Models/Model/bark">link</a></td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/PyTorch-Models/Model/bark">link</a></td> <td></td> </tr> <tr> <td>SpeechT5</td> <td></td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/PyTorch-Models/Model/speech-t5">link</a></td> <td></td> </tr> <tr> <td>DeepSeek-MoE</td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/deepseek-moe">link</a></td> <td></td> <td></td> </tr> <tr> <td>Ziya-Coding-34B-v1.0</td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/ziya">link</a></td> <td></td> <td></td> </tr> <tr> <td>Phi-2</td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/phi-2">link</a></td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/LLM/phi-2">link</a></td> <td></td> </tr> <tr> <td>Phi-3</td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/phi-3">link</a></td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/LLM/phi-3">link</a></td> <td></td> </tr> <tr> <td>Phi-3-vision</td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/phi-3-vision">link</a></td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/Multimodal/phi-3-vision">link</a></td> <td></td> </tr> <tr> <td>Yuan2</td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/yuan2">link</a></td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/LLM/yuan2">link</a></td> <td></td> </tr> <tr> <td>Gemma</td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/gemma">link</a></td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/LLM/gemma">link</a></td> <td></td> </tr> <tr> <td>Gemma2</td> <td></td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/LLM/gemma2">link</a></td> <td></td> </tr> <tr> <td>DeciLM-7B</td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/deciLM-7b">link</a></td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/LLM/deciLM-7b">link</a></td> <td></td> </tr> <tr> <td>Deepseek</td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/deepseek">link</a></td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/LLM/deepseek">link</a></td> <td></td> </tr> <tr> <td>StableLM</td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/stablelm">link</a></td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/LLM/stablelm">link</a></td> <td></td> </tr> <tr> <td>CodeGemma</td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/codegemma">link</a></td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/LLM/codegemma">link</a></td> <td></td> </tr> <tr> <td>Command-R/cohere</td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/cohere">link</a></td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/LLM/cohere">link</a></td> <td></td> </tr> <tr> <td>CodeGeeX2</td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/codegeex2">link</a></td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/LLM/codegeex2">link</a></td> <td></td> </tr> <tr> <td>MiniCPM</td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/minicpm">link</a></td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/LLM/minicpm">link</a></td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/NPU/HF-Transformers-AutoModels/LLM">Python link</a>, <a href="/intel/ipex-llm/blob/main/python/llm/example/NPU/HF-Transformers-AutoModels/LLM/CPP_Examples">C++ link</a></td> </tr> <tr> <td>MiniCPM3</td> <td></td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/LLM/minicpm3">link</a></td> <td></td> </tr> <tr> <td>MiniCPM-V</td> <td></td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/Multimodal/MiniCPM-V">link</a></td> <td></td> </tr> <tr> <td>MiniCPM-V-2</td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/minicpm-v-2">link</a></td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/Multimodal/MiniCPM-V-2">link</a></td> <td></td> </tr> <tr> <td>MiniCPM-Llama3-V-2_5</td> <td></td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/Multimodal/MiniCPM-Llama3-V-2_5">link</a></td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/NPU/HF-Transformers-AutoModels/Multimodal">Python link</a></td> </tr> <tr> <td>MiniCPM-V-2_6</td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/minicpm-v-2_6">link</a></td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/Multimodal/MiniCPM-V-2_6">link</a></td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/NPU/HF-Transformers-AutoModels/Multimodal">Python link</a></td> </tr> <tr> <td>MiniCPM-o-2_6</td> <td></td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/Multimodal/MiniCPM-o-2_6">link</a></td> <td></td> </tr> <tr> <td>Janus-Pro</td> <td></td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/Multimodal/janus-pro">link</a></td> <td></td> </tr> <tr> <td>Moonlight</td> <td></td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/LLM/moonlight">link</a></td> <td></td> </tr> <tr> <td>StableDiffusion</td> <td></td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/GPU/HuggingFace/Multimodal/StableDiffusion">link</a></td> <td></td> </tr> <tr> <td>Bce-Embedding-Base-V1</td> <td></td> <td></td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/NPU/HF-Transformers-AutoModels/Embedding">Python link</a></td> </tr> <tr> <td>Speech_Paraformer-Large</td> <td></td> <td></td> <td><a href="/intel/ipex-llm/blob/main/python/llm/example/NPU/HF-Transformers-AutoModels/Multimodal">Python link</a></td> </tr> </tbody> </table></markdown-accessiblity-table> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Get Support</h2><a id="user-content-get-support" class="anchor" aria-label="Permalink: Get Support" href="#get-support"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li>Please report a bug or raise a feature request by opening a <a href="https://github.com/intel-analytics/ipex-llm/issues">Github Issue</a></li> <li>Please report a vulnerability by opening a draft <a href="https://github.com/intel-analytics/ipex-llm/security/advisories">GitHub Security Advisory</a></li> </ul> <section data-footnotes="" class="footnotes"><h2 id="footnote-label" class="sr-only" dir="auto">Footnotes</h2> <ol dir="auto"> <li id="user-content-fn-1-28711fa4219f09e11d411254f051326e"> <p dir="auto">Performance varies by use, configuration and other factors. <code>ipex-llm</code> may not optimize to the same degree for non-Intel products. Learn more at <a href="http://www.Intel.com/PerformanceIndex">www.Intel.com/PerformanceIndex</a>. <a href="#user-content-fnref-1-28711fa4219f09e11d411254f051326e" data-footnote-backref="" aria-label="Back to reference 1" class="data-footnote-backref">↩</a> <a href="#user-content-fnref-1-2-28711fa4219f09e11d411254f051326e" data-footnote-backref="" aria-label="Back to reference 1-2" class="data-footnote-backref">↩<sup>2</sup></a></p> </li> </ol> </section> </article></div></div></div></div></div>   <script type="application/json" id="__PRIMER_DATA_:R0:__">{"resolvedServerColorMode":"day"}</script></div> </react-partial> <input type="hidden" data-csrf="true" value="TQdcON7VNAuoYq7k1I1SlDNz8GTR3Vl/giy4NfLsXyQq5RjobHtvM4lX768BmH2LG5yyGvijM4c+j+p+ezuLbg==" /> </div> <div data-view-component="true" class="Layout-sidebar"> <div class="BorderGrid about-margin" data-pjax> <div class="BorderGrid-row"> <div class="BorderGrid-cell"> <div class="hide-sm hide-md"> <h2 class="mb-3 h4">About</h2> <p class="f4 my-3"> Accelerate local LLM inference and finetuning (LLaMA, Mistral, ChatGLM, Qwen, DeepSeek, Mixtral, Gemma, Phi, MiniCPM, Qwen-VL, MiniCPM-V, etc.) on Intel XPU (e.g., local PC with iGPU and NPU, discrete GPU such as Arc, Flex and Max); seamlessly integrate with llama.cpp, Ollama, HuggingFace, LangChain, LlamaIndex, vLLM, DeepSpeed, Axolotl, etc. </p> <h3 class="sr-only">Topics</h3> <div class="my-3"> <div class="f6"> <a href="/topics/gpu" title="Topic: gpu" data-view-component="true" class="topic-tag topic-tag-link"> gpu </a> <a href="/topics/transformers" title="Topic: transformers" data-view-component="true" class="topic-tag topic-tag-link"> transformers </a> <a href="/topics/pytorch" title="Topic: pytorch" data-view-component="true" class="topic-tag topic-tag-link"> pytorch </a> <a href="/topics/llm" title="Topic: llm" data-view-component="true" class="topic-tag topic-tag-link"> llm </a> </div> </div> <h3 class="sr-only">Resources</h3> <div class="mt-2"> <a 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.25Zm0 2.445L6.615 5.5a.75.75 0 0 1-.564.41l-3.097.45 2.24 2.184a.75.75 0 0 1 .216.664l-.528 3.084 2.769-1.456a.75.75 0 0 1 .698 0l2.77 1.456-.53-3.084a.75.75 0 0 1 .216-.664l2.24-2.183-3.096-.45a.75.75 0 0 1-.564-.41L8 2.694Z"></path> </svg> <strong>7.6k</strong> stars</a> </div> <h3 class="sr-only">Watchers</h3> <div class="mt-2"> <a href="/intel/ipex-llm/watchers" data-view-component="true" class="Link Link--muted"><svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-eye mr-2"> <path d="M8 2c1.981 0 3.671.992 4.933 2.078 1.27 1.091 2.187 2.345 2.637 3.023a1.62 1.62 0 0 1 0 1.798c-.45.678-1.367 1.932-2.637 3.023C11.67 13.008 9.981 14 8 14c-1.981 0-3.671-.992-4.933-2.078C1.797 10.83.88 9.576.43 8.898a1.62 1.62 0 0 1 0-1.798c.45-.677 1.367-1.931 2.637-3.022C4.33 2.992 6.019 2 8 2ZM1.679 7.932a.12.12 0 0 0 0 .136c.411.622 1.241 1.75 2.366 2.717C5.176 11.758 6.527 12.5 8 12.5c1.473 0 2.825-.742 3.955-1.715 1.124-.967 1.954-2.096 2.366-2.717a.12.12 0 0 0 0-.136c-.412-.621-1.242-1.75-2.366-2.717C10.824 4.242 9.473 3.5 8 3.5c-1.473 0-2.825.742-3.955 1.715-1.124.967-1.954 2.096-2.366 2.717ZM8 10a2 2 0 1 1-.001-3.999A2 2 0 0 1 8 10Z"></path> </svg> <strong>260</strong> watching</a> </div> <h3 class="sr-only">Forks</h3> <div class="mt-2"> <a href="/intel/ipex-llm/forks" data-view-component="true" class="Link Link--muted"><svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-repo-forked mr-2"> <path d="M5 5.372v.878c0 .414.336.75.75.75h4.5a.75.75 0 0 0 .75-.75v-.878a2.25 2.25 0 1 1 1.5 0v.878a2.25 2.25 0 0 1-2.25 2.25h-1.5v2.128a2.251 2.251 0 1 1-1.5 0V8.5h-1.5A2.25 2.25 0 0 1 3.5 6.25v-.878a2.25 2.25 0 1 1 1.5 0ZM5 3.25a.75.75 0 1 0-1.5 0 .75.75 0 0 0 1.5 0Zm6.75.75a.75.75 0 1 0 0-1.5.75.75 0 0 0 0 1.5Zm-3 8.75a.75.75 0 1 0-1.5 0 .75.75 0 0 0 1.5 0Z"></path> </svg> <strong>1.3k</strong> forks</a> </div> <div class="mt-2"> <a class="Link--muted" href="/contact/report-content?content_url=https%3A%2F%2Fgithub.com%2Fintel%2Fipex-llm&report=intel+%28user%29"> Report repository </a> </div> </div> </div> </div> <div class="BorderGrid-row"> <div class="BorderGrid-cell"> <h2 class="h4 mb-3" data-pjax="#repo-content-pjax-container" data-turbo-frame="repo-content-turbo-frame"> <a href="/intel/ipex-llm/releases" data-view-component="true" class="Link--primary no-underline Link">Releases <span title="20" data-view-component="true" class="Counter">20</span></a></h2> <a class="Link--primary d-flex no-underline" data-pjax="#repo-content-pjax-container" data-turbo-frame="repo-content-turbo-frame" href="/intel/ipex-llm/releases/tag/v2.1.0"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-tag flex-shrink-0 mt-1 color-fg-success"> <path d="M1 7.775V2.75C1 1.784 1.784 1 2.75 1h5.025c.464 0 .91.184 1.238.513l6.25 6.25a1.75 1.75 0 0 1 0 2.474l-5.026 5.026a1.75 1.75 0 0 1-2.474 0l-6.25-6.25A1.752 1.752 0 0 1 1 7.775Zm1.5 0c0 .066.026.13.073.177l6.25 6.25a.25.25 0 0 0 .354 0l5.025-5.025a.25.25 0 0 0 0-.354l-6.25-6.25a.25.25 0 0 0-.177-.073H2.75a.25.25 0 0 0-.25.25ZM6 5a1 1 0 1 1 0 2 1 1 0 0 1 0-2Z"></path> </svg> <div class="ml-2 min-width-0"> <div class="d-flex"> <span class="css-truncate css-truncate-target text-bold mr-2" style="max-width: none;">IPEX-LLM release 2.1.0</span> <span title="Label: Latest" data-view-component="true" class="Label Label--success flex-shrink-0"> Latest </span> </div> <div class="text-small color-fg-muted"><relative-time datetime="2024-08-22T09:06:57Z" class="no-wrap">Aug 22, 2024</relative-time></div> </div> </a> <div data-view-component="true" class="mt-3"> <a text="small" data-pjax="#repo-content-pjax-container" data-turbo-frame="repo-content-turbo-frame" href="/intel/ipex-llm/releases" data-view-component="true" class="Link">+ 19 releases</a></div> </div> </div> <div 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