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It is built on top of <strong>Intel Extension for PyTorch</strong> (<strong>IPEX</strong>), as well as the excellent work of <strong><code><span>llama.cpp</span></code></strong>, <strong><code><span>bitsandbytes</span></code></strong>, <strong><code><span>vLLM</span></code></strong>, <strong><code><span>qlora</span></code></strong>, <strong><code><span>AutoGPTQ</span></code></strong>, <strong><code><span>AutoAWQ</span></code></strong>, etc.
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It is built on top of <strong>Intel Extension for PyTorch</strong> (<strong><code><span>IPEX</span></code></strong>), as well as the excellent work of <strong><code><span>llama.cpp</span></code></strong>, <strong><code><span>bitsandbytes</span></code></strong>, <strong><code><span>vLLM</span></code></strong>, <strong><code><span>qlora</span></code></strong>, <strong><code><span>AutoGPTQ</span></code></strong>, <strong><code><span>AutoAWQ</span></code></strong>, etc.
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It provides seamless integration with <a href=doc/LLM/Quickstart/llama_cpp_quickstart.html>llama.cpp</a>, <a href=doc/LLM/Quickstart/webui_quickstart.html>Text-Generation-WebUI</a>, <a href=https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/GPU/HF-Transformers-AutoModels>HuggingFace tansformers</a>, <a href=https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/GPU/LLM-Finetuning>HuggingFace PEFT</a>, <a href=https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/GPU/LangChain >LangChain</a>, <a href=https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/GPU/LlamaIndex >LlamaIndex</a>, <a href=https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/GPU/Deepspeed-AutoTP >DeepSpeed-AutoTP</a>, <a href=https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/GPU/vLLM-Serving >vLLM</a>, <a href=https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/src/ipex_llm/serving/fastchat>FastChat</a>, <a href=https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/GPU/LLM-Finetuning/DPO>HuggingFace TRL</a>, <a href=https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/CPU/Applications/autogen >AutoGen</a>, <a href=https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/GPU/ModelScope-Models >ModeScope</a>, etc.
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It provides seamless integration with <a href=doc/LLM/Quickstart/llama_cpp_quickstart.html>llama.cpp</a>, <a href=doc/LLM/Quickstart/webui_quickstart.html>Text-Generation-WebUI</a>, <a href=https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/GPU/HF-Transformers-AutoModels>HuggingFace tansformers</a>, <a href=https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/GPU/LLM-Finetuning>HuggingFace PEFT</a>, <a href=https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/GPU/LangChain >LangChain</a>, <a href=https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/GPU/LlamaIndex >LlamaIndex</a>, <a href=https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/GPU/Deepspeed-AutoTP >DeepSpeed-AutoTP</a>, <a href=https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/GPU/vLLM-Serving >vLLM</a>, <a href=https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/src/ipex_llm/serving/fastchat>FastChat</a>, <a href=https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/GPU/LLM-Finetuning/DPO>HuggingFace TRL</a>, <a href=https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/CPU/Applications/autogen >AutoGen</a>, <a href=https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/GPU/ModelScope-Models >ModeScope</a>, etc.
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## IPEX-LLM
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**[`ipex-llm`](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/index.html)** is a library for running **LLM** (large language model) on Intel **XPU** (from *Laptop* to *GPU* to *Cloud*) using **INT4** with very low latency[^1] (for any **PyTorch** model).
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> *It is built on top of the excellent work of [llama.cpp](https://github.com/ggerganov/llama.cpp), [gptq](https://github.com/IST-DASLab/gptq), [ggml](https://github.com/ggerganov/ggml), [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), [bitsandbytes](https://github.com/TimDettmers/bitsandbytes), [qlora](https://github.com/artidoro/qlora), [gptq_for_llama](https://github.com/qwopqwop200/GPTQ-for-LLaMa), [chatglm.cpp](https://github.com/li-plus/chatglm.cpp), [redpajama.cpp](https://github.com/togethercomputer/redpajama.cpp), [gptneox.cpp](https://github.com/byroneverson/gptneox.cpp), [bloomz.cpp](https://github.com/NouamaneTazi/bloomz.cpp/), etc.*
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### Demos
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See the ***optimized performance*** of `chatglm2-6b` and `llama-2-13b-chat` models on 12th Gen Intel Core CPU and Intel Arc GPU below.
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<table width="100%">
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<tr>
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<td align="center" colspan="2">12th Gen Intel Core CPU</td>
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<td align="center" colspan="2">Intel Arc GPU</td>
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</tr>
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<a href="https://llm-assets.readthedocs.io/en/latest/_images/chatglm2-6b.gif"><img src="https://llm-assets.readthedocs.io/en/latest/_images/chatglm2-6b.gif" ></a>
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<a href="https://llm-assets.readthedocs.io/en/latest/_images/llama-2-13b-chat.gif"><img src="https://llm-assets.readthedocs.io/en/latest/_images/llama-2-13b-chat.gif"></a>
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<a href="https://llm-assets.readthedocs.io/en/latest/_images/chatglm2-arc.gif"><img src="https://llm-assets.readthedocs.io/en/latest/_images/chatglm2-arc.gif"></a>
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<a href="https://llm-assets.readthedocs.io/en/latest/_images/llama2-13b-arc.gif"><img src="https://llm-assets.readthedocs.io/en/latest/_images/llama2-13b-arc.gif"></a>
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</td>
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</tr>
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<td align="center" width="25%"><code>chatglm2-6b</code></td>
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<td align="center" width="25%"><code>llama-2-13b-chat</code></td>
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<td align="center" width="25%"><code>chatglm2-6b</code></td>
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<td align="center" width="25%"><code>llama-2-13b-chat</code></td>
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</tr>
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</table>
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### Verified models
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Over 20 models have been optimized/verified on `ipex-llm`, including *LLaMA/LLaMA2, ChatGLM/ChatGLM2, Mistral, Falcon, MPT, Dolly, StarCoder, Whisper, Baichuan, InternLM, QWen, Aquila, MOSS,* and more; see the complete list below.
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| Model | CPU Example | GPU Example |
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|------------|----------------------------------------------------------------|-----------------------------------------------------------------|
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| LLaMA *(such as Vicuna, Guanaco, Koala, Baize, WizardLM, etc.)* | [link1](example/CPU/Native-Models), [link2](example/CPU/HF-Transformers-AutoModels/Model/vicuna) |[link](example/GPU/HF-Transformers-AutoModels/Model/vicuna)|
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| LLaMA 2 | [link1](example/CPU/Native-Models), [link2](example/CPU/HF-Transformers-AutoModels/Model/llama2) | [link1](example/GPU/HF-Transformers-AutoModels/Model/llama2), [link2-low GPU memory example](example/GPU/PyTorch-Models/Model/llama2#example-2---low-memory-version-predict-tokens-using-generate-api) |
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| ChatGLM | [link](example/CPU/HF-Transformers-AutoModels/Model/chatglm) | |
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| ChatGLM2 | [link](example/CPU/HF-Transformers-AutoModels/Model/chatglm2) | [link](example/GPU/HF-Transformers-AutoModels/Model/chatglm2) |
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| ChatGLM3 | [link](example/CPU/HF-Transformers-AutoModels/Model/chatglm3) | [link](example/GPU/HF-Transformers-AutoModels/Model/chatglm3) |
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| Mistral | [link](example/CPU/HF-Transformers-AutoModels/Model/mistral) | [link](example/GPU/HF-Transformers-AutoModels/Model/mistral) |
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| Falcon | [link](example/CPU/HF-Transformers-AutoModels/Model/falcon) | [link](example/GPU/HF-Transformers-AutoModels/Model/falcon) |
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| MPT | [link](example/CPU/HF-Transformers-AutoModels/Model/mpt) | [link](example/GPU/HF-Transformers-AutoModels/Model/mpt) |
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| Dolly-v1 | [link](example/CPU/HF-Transformers-AutoModels/Model/dolly_v1) | [link](example/GPU/HF-Transformers-AutoModels/Model/dolly_v1) |
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| Dolly-v2 | [link](example/CPU/HF-Transformers-AutoModels/Model/dolly_v2) | [link](example/GPU/HF-Transformers-AutoModels/Model/dolly_v2) |
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| Replit Code| [link](example/CPU/HF-Transformers-AutoModels/Model/replit) | [link](example/GPU/HF-Transformers-AutoModels/Model/replit) |
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| RedPajama | [link1](example/CPU/Native-Models), [link2](example/CPU/HF-Transformers-AutoModels/Model/redpajama) | |
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| Phoenix | [link1](example/CPU/Native-Models), [link2](example/CPU/HF-Transformers-AutoModels/Model/phoenix) | |
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| StarCoder | [link1](example/CPU/Native-Models), [link2](example/CPU/HF-Transformers-AutoModels/Model/starcoder) | [link](example/GPU/HF-Transformers-AutoModels/Model/starcoder) |
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| Baichuan | [link](example/CPU/HF-Transformers-AutoModels/Model/baichuan) | [link](example/GPU/HF-Transformers-AutoModels/Model/baichuan) |
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| Baichuan2 | [link](example/CPU/HF-Transformers-AutoModels/Model/baichuan2) | [link](example/GPU/HF-Transformers-AutoModels/Model/baichuan2) |
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| InternLM | [link](example/CPU/HF-Transformers-AutoModels/Model/internlm) | [link](example/GPU/HF-Transformers-AutoModels/Model/internlm) |
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| Qwen | [link](example/CPU/HF-Transformers-AutoModels/Model/qwen) | [link](example/GPU/HF-Transformers-AutoModels/Model/qwen) |
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| Qwen1.5 | [link](example/CPU/HF-Transformers-AutoModels/Model/qwen1.5) | [link](example/GPU/HF-Transformers-AutoModels/Model/qwen1.5) |
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| Qwen-VL | [link](example/CPU/HF-Transformers-AutoModels/Model/qwen-vl) | [link](example/GPU/HF-Transformers-AutoModels/Model/qwen-vl) |
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| Aquila | [link](example/CPU/HF-Transformers-AutoModels/Model/aquila) | [link](example/GPU/HF-Transformers-AutoModels/Model/aquila) |
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| Aquila2 | [link](example/CPU/HF-Transformers-AutoModels/Model/aquila2) | [link](example/GPU/HF-Transformers-AutoModels/Model/aquila2) |
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| MOSS | [link](example/CPU/HF-Transformers-AutoModels/Model/moss) | |
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| Whisper | [link](example/CPU/HF-Transformers-AutoModels/Model/whisper) | [link](example/GPU/HF-Transformers-AutoModels/Model/whisper) |
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| Phi-1_5 | [link](example/CPU/HF-Transformers-AutoModels/Model/phi-1_5) | [link](example/GPU/HF-Transformers-AutoModels/Model/phi-1_5) |
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| Flan-t5 | [link](example/CPU/HF-Transformers-AutoModels/Model/flan-t5) | [link](example/GPU/HF-Transformers-AutoModels/Model/flan-t5) |
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| LLaVA | [link](example/CPU/PyTorch-Models/Model/llava) | [link](example/GPU/PyTorch-Models/Model/llava) |
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| CodeLlama | [link](example/CPU/HF-Transformers-AutoModels/Model/codellama) | [link](example/GPU/HF-Transformers-AutoModels/Model/codellama) |
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| Skywork | [link](example/CPU/HF-Transformers-AutoModels/Model/skywork) | |
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| InternLM-XComposer | [link](example/CPU/HF-Transformers-AutoModels/Model/internlm-xcomposer) | |
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| WizardCoder-Python | [link](example/CPU/HF-Transformers-AutoModels/Model/wizardcoder-python) | |
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| CodeShell | [link](example/CPU/HF-Transformers-AutoModels/Model/codeshell) | |
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| Fuyu | [link](example/CPU/HF-Transformers-AutoModels/Model/fuyu) | |
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| Distil-Whisper | [link](example/CPU/HF-Transformers-AutoModels/Model/distil-whisper) | [link](example/GPU/HF-Transformers-AutoModels/Model/distil-whisper) |
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| Yi | [link](example/CPU/HF-Transformers-AutoModels/Model/yi) | [link](example/GPU/HF-Transformers-AutoModels/Model/yi) |
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| BlueLM | [link](example/CPU/HF-Transformers-AutoModels/Model/bluelm) | [link](example/GPU/HF-Transformers-AutoModels/Model/bluelm) |
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| Mamba | [link](example/CPU/PyTorch-Models/Model/mamba) | [link](example/GPU/PyTorch-Models/Model/mamba) |
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| SOLAR | [link](example/CPU/HF-Transformers-AutoModels/Model/solar) | [link](example/GPU/HF-Transformers-AutoModels/Model/solar) |
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| Phixtral | [link](example/CPU/HF-Transformers-AutoModels/Model/phixtral) | [link](example/GPU/HF-Transformers-AutoModels/Model/phixtral) |
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| InternLM2 | [link](example/CPU/HF-Transformers-AutoModels/Model/internlm2) | [link](example/GPU/HF-Transformers-AutoModels/Model/internlm2) |
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| RWKV4 | | [link](example/GPU/HF-Transformers-AutoModels/Model/rwkv4) |
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| RWKV5 | | [link](example/GPU/HF-Transformers-AutoModels/Model/rwkv5) |
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| Bark | [link](example/CPU/PyTorch-Models/Model/bark) | [link](example/GPU/PyTorch-Models/Model/bark) |
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| SpeechT5 | | [link](example/GPU/PyTorch-Models/Model/speech-t5) |
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| DeepSeek-MoE | [link](example/CPU/HF-Transformers-AutoModels/Model/deepseek-moe) | |
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| Ziya-Coding-34B-v1.0 | [link](example/CPU/HF-Transformers-AutoModels/Model/ziya) | |
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| Phi-2 | [link](example/CPU/HF-Transformers-AutoModels/Model/phi-2) | [link](example/GPU/HF-Transformers-AutoModels/Model/phi-2) |
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| Yuan2 | [link](example/CPU/HF-Transformers-AutoModels/Model/yuan2) | [link](example/GPU/HF-Transformers-AutoModels/Model/yuan2) |
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| DeciLM-7B | [link](example/CPU/HF-Transformers-AutoModels/Model/deciLM-7b) | [link](example/GPU/HF-Transformers-AutoModels/Model/deciLM-7b) |
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| Deepseek | [link](example/CPU/HF-Transformers-AutoModels/Model/deepseek) | [link](example/GPU/HF-Transformers-AutoModels/Model/deepseek) |
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### Working with `ipex-llm`
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<details><summary>Table of Contents</summary>
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- [IPEX-LLM](#ipex-llm)
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- [Demos](#demos)
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- [Verified models](#verified-models)
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- [Working with `ipex-llm`](#working-with-ipex-llm)
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- [Install](#install)
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- [CPU](#cpu)
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- [GPU](#gpu)
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- [Run Model](#run-model)
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- [1. Hugging Face `transformers` API](#1-hugging-face-transformers-api)
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- [CPU INT4](#cpu-int4)
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- [GPU INT4](#gpu-int4)
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- [More Low-Bit Support](#more-low-bit-support)
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- [2. Native INT4 model](#2-native-int4-model)
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- [3. LangChain API](#3-langchain-api)
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- [4. CLI Tool](#4-cli-tool)
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- [`ipex-llm` API Doc](#ipex-llm-api-doc)
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</details>
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#### Install
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##### CPU
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You may install **`ipex-llm`** on Intel CPU as follows:
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```bash
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pip install --pre --upgrade ipex-llm[all]
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```
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> Note: `ipex-llm` has been tested on Python 3.9
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##### GPU
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You may install **`ipex-llm`** on Intel GPU as follows:
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```bash
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# below command will install intel_extension_for_pytorch==2.0.110+xpu as default
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# you can install specific ipex/torch version for your need
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pip install --pre --upgrade ipex-llm[xpu] -f https://developer.intel.com/ipex-whl-stable-xpu
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```
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> Note: `ipex-llm` has been tested on Python 3.9
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#### Run Model
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You may run the models using **`ipex-llm`** through one of the following APIs:
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1. [Hugging Face `transformers` API](#1-hugging-face-transformers-api)
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2. [Native INT4 Model](#2-native-int4-model)
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3. [LangChain API](#3-langchain-api)
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4. [CLI (command line interface) Tool](#4-cli-tool)
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##### 1. Hugging Face `transformers` API
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You may run any Hugging Face *Transformers* model as follows:
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###### CPU INT4
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You may apply INT4 optimizations to any Hugging Face *Transformers* model on Intel CPU as follows.
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```python
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#load Hugging Face Transformers model with INT4 optimizations
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from ipex_llm.transformers import AutoModelForCausalLM
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model = AutoModelForCausalLM.from_pretrained('/path/to/model/', load_in_4bit=True)
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#run the optimized model on Intel CPU
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from transformers import AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained(model_path)
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input_ids = tokenizer.encode(input_str, ...)
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output_ids = model.generate(input_ids, ...)
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output = tokenizer.batch_decode(output_ids)
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```
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See the complete examples [here](example/CPU/HF-Transformers-AutoModels/Model/).
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###### GPU INT4
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You may apply INT4 optimizations to any Hugging Face *Transformers* model on Intel GPU as follows.
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```python
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#load Hugging Face Transformers model with INT4 optimizations
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from ipex_llm.transformers import AutoModelForCausalLM
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import intel_extension_for_pytorch
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model = AutoModelForCausalLM.from_pretrained('/path/to/model/', load_in_4bit=True)
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#run the optimized model on Intel GPU
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model = model.to('xpu')
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from transformers import AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained(model_path)
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input_ids = tokenizer.encode(input_str, ...).to('xpu')
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output_ids = model.generate(input_ids, ...)
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output = tokenizer.batch_decode(output_ids.cpu())
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```
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See the complete examples [here](example/GPU).
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###### More Low-Bit Support
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- Save and load
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||||||
|
|
||||||
After the model is optimized using `ipex-llm`, you may save and load the model as follows:
|
|
||||||
```python
|
|
||||||
model.save_low_bit(model_path)
|
|
||||||
new_model = AutoModelForCausalLM.load_low_bit(model_path)
|
|
||||||
```
|
|
||||||
*See the complete example [here](example/CPU/HF-Transformers-AutoModels/Save-Load).*
|
|
||||||
|
|
||||||
- Additonal data types
|
|
||||||
|
|
||||||
In addition to INT4, You may apply other low bit optimizations (such as *INT8*, *INT5*, *NF4*, etc.) as follows:
|
|
||||||
|
|
||||||
```python
|
|
||||||
model = AutoModelForCausalLM.from_pretrained('/path/to/model/', load_in_low_bit="sym_int8")
|
|
||||||
```
|
|
||||||
*See the complete example [here](example/CPU/HF-Transformers-AutoModels/More-Data-Types).*
|
|
||||||
|
|
||||||
##### 2. Native INT4 model
|
|
||||||
|
|
||||||
You may also convert Hugging Face *Transformers* models into native INT4 model format for maximum performance as follows.
|
|
||||||
|
|
||||||
>**Notes**: Currently only llama/bloom/gptneox/starcoder/chatglm model families are supported; for other models, you may use the Hugging Face `transformers` model format as described above).
|
|
||||||
|
|
||||||
```python
|
|
||||||
#convert the model
|
|
||||||
from ipex_llm import llm_convert
|
|
||||||
ipex_llm_path = llm_convert(model='/path/to/model/',
|
|
||||||
outfile='/path/to/output/', outtype='int4', model_family="llama")
|
|
||||||
|
|
||||||
#load the converted model
|
|
||||||
#switch to ChatGLMForCausalLM/GptneoxForCausalLM/BloomForCausalLM/StarcoderForCausalLM to load other models
|
|
||||||
from ipex_llm.transformers import LlamaForCausalLM
|
|
||||||
llm = LlamaForCausalLM.from_pretrained("/path/to/output/model.bin", native=True, ...)
|
|
||||||
|
|
||||||
#run the converted model
|
|
||||||
input_ids = llm.tokenize(prompt)
|
|
||||||
output_ids = llm.generate(input_ids, ...)
|
|
||||||
output = llm.batch_decode(output_ids)
|
|
||||||
```
|
|
||||||
|
|
||||||
See the complete example [here](example/CPU/Native-Models/native_int4_pipeline.py).
|
|
||||||
|
|
||||||
##### 3. LangChain API
|
|
||||||
You may run the models using the LangChain API in `ipex-llm`.
|
|
||||||
|
|
||||||
- **Using Hugging Face `transformers` model**
|
|
||||||
|
|
||||||
You may run any Hugging Face *Transformers* model (with INT4 optimiztions applied) using the LangChain API as follows:
|
|
||||||
|
|
||||||
```python
|
|
||||||
from ipex_llm.langchain.llms import TransformersLLM
|
|
||||||
from ipex_llm.langchain.embeddings import TransformersEmbeddings
|
|
||||||
from langchain.chains.question_answering import load_qa_chain
|
|
||||||
|
|
||||||
embeddings = TransformersEmbeddings.from_model_id(model_id=model_path)
|
|
||||||
ipex_llm = TransformersLLM.from_model_id(model_id=model_path, ...)
|
|
||||||
|
|
||||||
doc_chain = load_qa_chain(ipex_llm, ...)
|
|
||||||
output = doc_chain.run(...)
|
|
||||||
```
|
|
||||||
See the examples [here](example/CPU/LangChain/transformers_int4).
|
|
||||||
|
|
||||||
- **Using native INT4 model**
|
|
||||||
|
|
||||||
You may also convert Hugging Face *Transformers* models into *native INT4* format, and then run the converted models using the LangChain API as follows.
|
|
||||||
|
|
||||||
>**Notes**:* Currently only llama/bloom/gptneox/starcoder/chatglm model families are supported; for other models, you may use the Hugging Face `transformers` model format as described above).
|
|
||||||
|
|
||||||
```python
|
|
||||||
from ipex_llm.langchain.llms import LlamaLLM
|
|
||||||
from ipex_llm.langchain.embeddings import LlamaEmbeddings
|
|
||||||
from langchain.chains.question_answering import load_qa_chain
|
|
||||||
|
|
||||||
#switch to ChatGLMEmbeddings/GptneoxEmbeddings/BloomEmbeddings/StarcoderEmbeddings to load other models
|
|
||||||
embeddings = LlamaEmbeddings(model_path='/path/to/converted/model.bin')
|
|
||||||
#switch to ChatGLMLLM/GptneoxLLM/BloomLLM/StarcoderLLM to load other models
|
|
||||||
ipex_llm = LlamaLLM(model_path='/path/to/converted/model.bin')
|
|
||||||
|
|
||||||
doc_chain = load_qa_chain(ipex_llm, ...)
|
|
||||||
doc_chain.run(...)
|
|
||||||
```
|
|
||||||
|
|
||||||
See the examples [here](example/CPU/LangChain/native_int4).
|
|
||||||
|
|
||||||
##### 4. CLI Tool
|
|
||||||
>**Note**: Currently `ipex-llm` CLI supports *LLaMA* (e.g., *vicuna*), *GPT-NeoX* (e.g., *redpajama*), *BLOOM* (e.g., *pheonix*) and *GPT2* (e.g., *starcoder*) model architecture; for other models, you may use the Hugging Face `transformers` or LangChain APIs.
|
|
||||||
|
|
||||||
- ##### Convert model
|
|
||||||
|
|
||||||
You may convert the downloaded model into native INT4 format using `llm-convert`.
|
|
||||||
|
|
||||||
```bash
|
|
||||||
#convert PyTorch (fp16 or fp32) model;
|
|
||||||
#llama/bloom/gptneox/starcoder model family is currently supported
|
|
||||||
llm-convert "/path/to/model/" --model-format pth --model-family "bloom" --outfile "/path/to/output/"
|
|
||||||
|
|
||||||
#convert GPTQ-4bit model
|
|
||||||
#only llama model family is currently supported
|
|
||||||
llm-convert "/path/to/model/" --model-format gptq --model-family "llama" --outfile "/path/to/output/"
|
|
||||||
```
|
|
||||||
|
|
||||||
- ##### Run model
|
|
||||||
|
|
||||||
You may run the converted model using `llm-cli` or `llm-chat` (*built on top of `main.cpp` in [llama.cpp](https://github.com/ggerganov/llama.cpp)*)
|
|
||||||
|
|
||||||
```bash
|
|
||||||
#help
|
|
||||||
#llama/bloom/gptneox/starcoder model family is currently supported
|
|
||||||
llm-cli -x gptneox -h
|
|
||||||
|
|
||||||
#text completion
|
|
||||||
#llama/bloom/gptneox/starcoder model family is currently supported
|
|
||||||
llm-cli -t 16 -x gptneox -m "/path/to/output/model.bin" -p 'Once upon a time,'
|
|
||||||
|
|
||||||
#chat mode
|
|
||||||
#llama/gptneox model family is currently supported
|
|
||||||
llm-chat -m "/path/to/output/model.bin" -x llama
|
|
||||||
```
|
|
||||||
|
|
||||||
### `ipex-llm` API Doc
|
|
||||||
See the inital `ipex-llm` API Doc [here](https://ipex-llm.readthedocs.io/en/latest/doc/PythonAPI/LLM/index.html).
|
|
||||||
|
|
||||||
[^1]: Performance varies by use, configuration and other factors. `ipex-llm` may not optimize to the same degree for non-Intel products. Learn more at www.Intel.com/PerformanceIndex.
|
|
||||||
Loading…
Reference in a new issue