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mistralai/vllm-release

Description: A high-throughput and memory-efficient inference and serving engine for LLMs

Language: Python

License: Apache-2.0

Stars: 55

Forks: 14

Open issues: 0

Created: 2023-09-27T12:23:21Z

Pushed: 2023-12-11T08:56:05Z

Default branch: main

Fork: yes

Parent repository: vllm-project/vllm

Archived: yes

README:

Easy, fast, and cheap LLM serving for everyone

| Documentation | Blog | Paper | Discord |

---

*Latest News* 🔥

  • [2023/12] Added ROCm support to vLLM.
  • [2023/10] We hosted the first vLLM meetup in SF! Please find the meetup slides here.
  • [2023/09] We created our Discord server! Join us to discuss vLLM and LLM serving! We will also post the latest announcements and updates there.
  • [2023/09] We released our PagedAttention paper on arXiv!
  • [2023/08] We would like to express our sincere gratitude to Andreessen Horowitz (a16z) for providing a generous grant to support the open-source development and research of vLLM.
  • [2023/07] Added support for LLaMA-2! You can run and serve 7B/13B/70B LLaMA-2s on vLLM with a single command!
  • [2023/06] Serving vLLM On any Cloud with SkyPilot. Check out a 1-click example to start the vLLM demo, and the blog post for the story behind vLLM development on the clouds.
  • [2023/06] We officially released vLLM! FastChat-vLLM integration has powered LMSYS Vicuna and Chatbot Arena since mid-April. Check out our blog post.

---

vLLM is a fast and easy-to-use library for LLM inference and serving.

vLLM is fast with:

  • State-of-the-art serving throughput
  • Efficient management of attention key and value memory with PagedAttention
  • Continuous batching of incoming requests
  • Optimized CUDA kernels

vLLM is flexible and easy to use with:

  • Seamless integration with popular Hugging Face models
  • High-throughput serving with various decoding algorithms, including *parallel sampling*, *beam search*, and more
  • Tensor parallelism support for distributed inference
  • Streaming outputs
  • OpenAI-compatible API server
  • Support NVIDIA CUDA and AMD ROCm.

vLLM seamlessly supports many Hugging Face models, including the following architectures:

  • Aquila & Aquila2 (BAAI/AquilaChat2-7B, BAAI/AquilaChat2-34B, BAAI/Aquila-7B, BAAI/AquilaChat-7B, etc.)
  • Baichuan & Baichuan2 (baichuan-inc/Baichuan2-13B-Chat, baichuan-inc/Baichuan-7B, etc.)
  • BLOOM (bigscience/bloom, bigscience/bloomz, etc.)
  • ChatGLM (THUDM/chatglm2-6b, THUDM/chatglm3-6b, etc.)
  • Falcon (tiiuae/falcon-7b, tiiuae/falcon-40b, tiiuae/falcon-rw-7b, etc.)
  • GPT-2 (gpt2, gpt2-xl, etc.)
  • GPT BigCode (bigcode/starcoder, bigcode/gpt_bigcode-santacoder, etc.)
  • GPT-J (EleutherAI/gpt-j-6b, nomic-ai/gpt4all-j, etc.)
  • GPT-NeoX (EleutherAI/gpt-neox-20b, databricks/dolly-v2-12b, stabilityai/stablelm-tuned-alpha-7b, etc.)
  • InternLM (internlm/internlm-7b, internlm/internlm-chat-7b, etc.)
  • LLaMA & LLaMA-2 (meta-llama/Llama-2-70b-hf, lmsys/vicuna-13b-v1.3, young-geng/koala, openlm-research/open_llama_13b, etc.)
  • Mistral (mistralai/Mistral-7B-v0.1, mistralai/Mistral-7B-Instruct-v0.1, etc.)
  • Mixtral (mistralai/Mixtral-8x7B-v0.1, mistralai/Mixtral-8x7B-Instruct-v0.1, etc.)
  • MPT (mosaicml/mpt-7b, mosaicml/mpt-30b, etc.)
  • OPT (facebook/opt-66b, facebook/opt-iml-max-30b, etc.)
  • Phi-1.5 (microsoft/phi-1_5, etc.)
  • Qwen (Qwen/Qwen-7B, Qwen/Qwen-7B-Chat, etc.)
  • Yi (01-ai/Yi-6B, 01-ai/Yi-34B, etc.)

Install vLLM with pip or from source:

pip install vllm

Getting Started

Visit our documentation to get started.

Contributing

We welcome and value any contributions and collaborations. Please check out [CONTRIBUTING.md](./CONTRIBUTING.md) for how to get involved.

Citation

If you use vLLM for your research, please cite our paper:

@inproceedings{kwon2023efficient,
title={Efficient Memory Management for Large Language Model Serving with PagedAttention},
author={Woosuk Kwon and Zhuohan Li and Siyuan Zhuang and Ying Sheng and Lianmin Zheng and Cody Hao Yu and Joseph E. Gonzalez and Hao Zhang and Ion Stoica},
booktitle={Proceedings of the ACM SIGOPS 29th Symposium on Operating Systems Principles},
year={2023}
}