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LLMC: Towards Accurate and Efficient LLM Compression

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LLMC is an off-the-shell tool designed for compressing LLM, leveraging state-of-the-art compression algorithms to enhance efficiency and reduce model size without compromising performance.

English doc is here.

Chinese doc is here.

Docker hub is here.

Aliyun docker: registry.cn-hangzhou.aliyuncs.com/yongyang/llmcompression:[tag]

You can download the Docker image that can run llmc with the following command. Users in mainland China are recommended to use Alibaba Cloud Docker.

docker hub

docker pull llmcompression/llmc:pure-latest

aliyun docker

docker pull registry.cn-hangzhou.aliyuncs.com/yongyang/llmcompression:pure-latest

Community:

Latest News

Nov 20, 2024: 🔥 We now fully support the quantization of ✨DeepSeekv2(2.5) and other MOE models, as well as ✨Qwen2VL, Llama3.2, and other VLM models. Supported quantization methods include ✅integer quantization, ✅floating-point quantization, and advanced algorithms like ✅AWQ, ✅GPTQ, ✅SmoothQuant, and ✅Quarot.
Nov 12, 2024: 🔥 We have added support for 💥static per-tensor activation quantization across various models and algorithms, covering ✅integer quantization and ✅floating-point quantization to further optimize performance and efficiency. Additionally, we now support exporting ✨real quantized models and using the VLLM and SGLang backends for inference acceleration. For more details, refer to the VLLM documentation and SGLang documentation.
Sep 26, 2024: 🔥 We now support exporting 💥FP8 quantized(E4M3, E5M2) models from 🚀LLMC to advanced inference backends such as VLLM and SGLang. For detailed usage, please refer to the VLLM documentation and SGLang documentation.
Sep 24, 2024: 🔥 We have officially released ✅INT4 and ✅INT8 models of ✨Llama-3.1-405B, quantized using 🚀LLMC in save_lightllm mode. You can download the model parameters here.
Sep 23, 2024: 🔥 We now support exporting ✨real quantized(INT4, INT8) models from 🚀LLMC to advanced inference backends such as VLLM, SGLang, AutoAWQ, and MLC-LLM for quantized inference deployment, enabling ✨reduced memory usage and ✨faster inference speeds. For detailed usage, please refer to the VLLM documentation, SGLang documentation, AutoAWQ documentation, and MLC-LLM documentation.
Sep 9, 2024: 🔥 We provide some configs of our best practice towards superior performance (see Best Practice here).

Sep 3, 2024: 🔥 We support opencompass 🤗 to eval 🚀LLMC model. Follow this doc and have a try!
Aug 22, 2024: 🔥We support lots of small language models, including current SOTA SmolLM(see Supported Model List).
Aug 22, 2024: 🔥 Additionally, we also support down stream task evaluation through our modified lm-evaluation-harness 🤗. Specifically, people can first employ save_trans mode(see save part in Configuration) to save a weight modified model. After obtaining the transformed model, they can directly evaluate the quantized model referring to run_lm_eval.sh. More details can be found in here.
Jul 23, 2024: 🍺🍺🍺 We release a brand new version benchmark paper:

LLMC: Benchmarking Large Language Model Quantization with a Versatile Compression Toolkit.

Ruihao Gong*, Yang Yong*, Shiqiao Gu*, Yushi Huang*, Chengtao Lv, Yunchen Zhang, Xianglong Liu📧, Dacheng Tao

(* denotes equal contribution, 📧 denotes corresponding author.)

<details close> <summary>Previous News</summary>

Jul 16, 2024: 🔥We support Wanda/Naive(Magnitude) for llm sparsification and layer-wise mix bits quantization now!
Jul 14, 2024: 🔥We support rotation based quantization QuaRot now!
May 17, 2024: 🚀 We support some advanced large models, e.g., LLaVA, Mixtral, LLaMA V3 and Qwen V2 now. Have a try!
May 13, 2024: 🍺🍺🍺 We release our quantization benchmark paper:

LLM-QBench: A Benchmark Towards the Best Practice for Post-training Quantization of Large Language Models.

Ruihao Gong*, Yang Yong*, Shiqiao Gu*, Yushi Huang*, Yunchen Zhang, Xianglong Liu📧, Dacheng Tao

(* denotes equal contribution, 📧 denotes corresponding author.)
<div align=center> <img src="./imgs/best_practice.png" alt="comp" width="800" /> </div>
We modularly and fairly benchmark the quantization techniques considering calibration cost, inference efficiency, and quantized accuracy. Near 600 experiments on diverse models and datasets provide three insightful takeaways on the calibration data, algorithm pipeline, and quantization configuration selection. Based on the takeaways, a best practice for the LLM PTQ pipeline is designed, to achieve the best accuracy and efficiency performance balance under various scenarios.
Mar 7, 2024: 🚀 We release the quantization part of a powerful and efficient LLM compression tool. Notably, our benchmark paper is coming soon😊.

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Highlight Feature

💥Comprehensive Algorithm Support: Provides a broad range of ✨SOTA compression algorithms, including ✅quantization, ✅mixed-precision quantization, and ✅sparsity, while maintaining accuracy consistent with the original repositories. ✨Quantization best practices (see 🚀Best Practices here) are also available to ensure optimal performance and efficiency.
💥Supported Formats: Supports both ✨quantization (integer and floating-point) and ✨sparsity, specifically including ✅weight-activation, ✅weight-only, ✅mixed-precision quantization, as well as ✅structured and ✅unstructured sparsity.
💥Wide Model Support: Offers support for a diverse array of ✨LLM models, including ✅LLama, ✅Mistral, ✅InternLM2, ✅Qwen2, among others, as well as ✅MOE(DeepSeekv2, Deepseekv2.5) and ✅VLM(Llama3.2-vision, Qwen2-vl) models (see Supported Model List).
💥Multi-backend Compatibility: Seamlessly integrates with various backends for enhanced deployment flexibility. Multiple quantization settings and model formats are compatible with a wide range of backends and hardware platforms, such as ✅VLLM, ✅Sglang, ✅LightLLM, ✅MLC-LLM, and ✅AutoAWQ, making it highly versatile(see Section Backend here).
💥Performance Efficiency: Enables quantization of large LLMs, such as ✨Llama3.1-405B and ✨DeepSeekV2-236B, with PPL evaluation on a single A100/H100/H800 GPU.

Usage

Please refer to the 🚀Quick Start section in the documentation.

Supported Model List

✅ BLOOM

✅ LLaMA

✅ LLaMA V2

✅ StarCoder

✅ OPT

✅ Falcon

✅ InternLM2

✅ Mistral

✅ LLaMA V3

✅ Mixtral

✅ Qwen V2

✅ LLaVA

✅ InternLM2.5

✅ StableLM

✅ Gemma2

✅ Phi2

✅ Phi 1.5

✅ MiniCPM

✅ SmolLM

✅ DeepSeekv2.5

✅ LLaMA V3.2 Vision

✅ Qwen MOE

✅ Qwen2-VL

✅ InternVL2

You can add your own model type referring to files under llmc/models/*.py.

Supported Backend List

✅ VLLM

✅ LightLLM

✅ Sglang

✅ MLC-LLM

✅ AutoAWQ

Supported Algorithm List

Quantization

✅ Naive

✅ AWQ

✅ GPTQ

✅ SmoothQuant

✅ OS+

✅ OmniQuant

✅ NormTweaking

✅ AdaDim

✅ QUIK

✅ SpQR

✅ DGQ

✅ OWQ

✅ LLM.int8()

✅ HQQ

✅ QuaRot

✅ SpinQuant (See this branch)

✅ TesseraQ

Pruning

✅ Naive(Magnitude)

✅ Wanda

✅ ShortGPT

Acknowledgments

We develop our code referring to the following repos:

Star History

Citation

If you find our LLM-QBench paper/llmc toolkit useful or relevant to your research, please kindly cite our paper:

@misc{llmc,
   author = {llmc contributors},
   title = {llmc: Towards Accurate and Efficient LLM Compression},
   year = {2024},
   publisher = {GitHub},
   journal = {GitHub repository},
   howpublished = {\url{https://github.com/ModelTC/llmc}},
}

@misc{gong2024llmqbench,
      title={LLM-QBench: A Benchmark Towards the Best Practice for Post-training Quantization of Large Language Models},
      author={Ruihao Gong and Yang Yong and Shiqiao Gu and Yushi Huang and Yunchen Zhang and Xianglong Liu and Dacheng Tao},
      year={2024},
      eprint={2405.06001},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

@misc{gong2024llmcbenchmarkinglargelanguage,
      title={LLMC: Benchmarking Large Language Model Quantization with a Versatile Compression Toolkit},
      author={Ruihao Gong and Yang Yong and Shiqiao Gu and Yushi Huang and Chentao Lv and Yunchen Zhang and Xianglong Liu and Dacheng Tao},
      year={2024},
      eprint={2405.06001},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2405.06001},
}