Package: nextai-vllm
Easy, fast, and cheap LLM serving for everyone|Documentation|Blog|Paper|Discord|The Second vLLM Bay Area Meetup (Jan 31st 5pm-7:30pm PT)We are thrilled to announce our second vLLM Meetup!
The vLLM team will share recent updates and roadmap.
We will also have vLLM collaborators from IBM coming up to the stage to discuss their insights on LLM optimizations.
Please registerhereand join us!Latest News🔥[2024/01] Added ROCm 6.0 support to vLLM.[2023/12] Added ROCm 5.7 support to vLLM.[2023/10] We hostedthe first vLLM meetupin SF! Please find the meetup slideshere.[2023/09] We created ourDiscord server! Join us to discuss vLLM and LLM serving! We will also post the latest announcements and updates there.[2023/09] We released ourPagedAttention paperon arXiv![2023/08] We would like to express our sincere gratitude toAndreessen 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-clickexampleto start the vLLM demo, and theblog postfor the story behind vLLM development on the clouds.[2023/06] We officially released vLLM! FastChat-vLLM integration has poweredLMSYS Vicuna and Chatbot Arenasince mid-April. Check out ourblog post.AboutvLLM is a fast and easy-to-use library for LLM inference and serving.vLLM is fast with:State-of-the-art serving throughputEfficient management of attention key and value memory withPagedAttentionContinuous batching of incoming requestsFast model execution with CUDA/HIP graphQuantization:GPTQ,AWQ,SqueezeLLMOptimized CUDA kernelsvLLM is flexible and easy to use with:Seamless integration with popular Hugging Face modelsHigh-throughput serving with various decoding algorithms, includingparallel sampling,beam search, and moreTensor parallelism support for distributed inferenceStreaming outputsOpenAI-compatible API serverSupport NVIDIA GPUs and AMD GPUsvLLM 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.)DeciLM (Deci/DeciLM-7B,Deci/DeciLM-7B-instruct, 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 (microsoft/phi-1_5,microsoft/phi-2, etc.)Qwen (Qwen/Qwen-7B,Qwen/Qwen-7B-Chat, etc.)Qwen2 (Qwen/Qwen2-7B-beta,Qwen/Qwen-7B-Chat-beta, etc.)StableLM(stabilityai/stablelm-3b-4e1t,stabilityai/stablelm-base-alpha-7b-v2, etc.)Yi (01-ai/Yi-6B,01-ai/Yi-34B, etc.)Install vLLM with pip orfrom source:pipinstallvllmGetting StartedVisit ourdocumentationto get started.InstallationQuickstartSupported ModelsContributingWe welcome and value any contributions and collaborations.
Please check outCONTRIBUTING.mdfor how to get involved.CitationIf you use vLLM for your research, please cite ourpaper:@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}}