Easy, fast, and cheap LLM serving for everyone

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--- ## vLLM GPT-4V API Alpha Branch Welcome to the vLLM GPT-4V API Alpha Branch, a cutting-edge experimental branch designed to enhance support for Vision Language Models (VLMs) and introduce capabilities for image input within the OpenAI Chat Completions API. It has been rigorously tested for compatibility with both AMD and NVIDIA GPUs. ### Purpose of This Branch This branch serves as a testing ground for our team and the broader community to evaluate the current support of VLMs, ensuring they meet all production demands, particularly on AMD GPUs. Your feedback and testing will help us refine and optimize the integration of VLMs. ### Key Features - Enhanced VLM Support - `AutoImageProcessor` Integration: Utilizes HuggingFace's AutoImageProcessor for image pre-processing, configurable via `config.json`. - Pre-built Templates: Includes a ready-to-use chat template for models like `llava-hf/llava-1.5-7b-hf`, simplifying setup. - Image Input Support in OpenAI API - Model Compatibility: Works with all `LlavaForConditionalGeneration` models, especially `llava-hf/llava-1.5-7b-hf`. - Flexible Image Sources: Accepts image data and web URLs. - Comprehensive Format Support: Supports PNG, JPEG, WEBP, GIF, BMP, and TIFF formats. - Preprocessing: Images are automatically adjusted to the required dimensions and format as per model specifications. ### Documentation New VLM Usage Guide: Detailed documentation available under the "Models" section to help you get started with VLMs. ### System Requirements Compatible with AMD and NVIDIA GPUs. ### Related Contributions This PR directly resolves issues [#2058](https://github.com/vllm-project/vllm/issues/2058) and [#3873](https://github.com/vllm-project/vllm/issues/3873). It also supercedes PR [#3467](https://github.com/vllm-project/vllm/pull/3467). PR [#3042](https://github.com/vllm-project/vllm/pull/3042) added vision language model support. ### Upstream Contributions This fork is actively maintained, and we are committed to upstreaming these enhancements in PR [#3978](https://github.com/vllm-project/vllm/pull/3978). Stay tuned for more updates and enhancements! For any issues or contributions, please refer to the issues section or submit a pull request. --- *Latest News* 🔥 - [2024/04] We hosted [the third vLLM meetup](https://robloxandvllmmeetup2024.splashthat.com/) with Roblox! Please find the meetup slides [here](https://docs.google.com/presentation/d/1A--47JAK4BJ39t954HyTkvtfwn0fkqtsL8NGFuslReM/edit?usp=sharing). - [2024/01] We hosted [the second vLLM meetup](https://lu.ma/ygxbpzhl) in SF! Please find the meetup slides [here](https://docs.google.com/presentation/d/12mI2sKABnUw5RBWXDYY-HtHth4iMSNcEoQ10jDQbxgA/edit?usp=sharing). - [2024/01] Added ROCm 6.0 support to vLLM. - [2023/12] Added ROCm 5.7 support to vLLM. - [2023/10] We hosted [the first vLLM meetup](https://lu.ma/first-vllm-meetup) in SF! Please find the meetup slides [here](https://docs.google.com/presentation/d/1QL-XPFXiFpDBh86DbEegFXBXFXjix4v032GhShbKf3s/edit?usp=sharing). - [2023/09] We created our [Discord server](https://discord.gg/jz7wjKhh6g)! 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](https://arxiv.org/abs/2309.06180) on arXiv! - [2023/08] We would like to express our sincere gratitude to [Andreessen Horowitz](https://a16z.com/2023/08/30/supporting-the-open-source-ai-community/) (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](https://github.com/skypilot-org/skypilot/blob/master/llm/vllm) to start the vLLM demo, and the [blog post](https://blog.skypilot.co/serving-llm-24x-faster-on-the-cloud-with-vllm-and-skypilot/) 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](https://chat.lmsys.org) since mid-April. Check out our [blog post](https://vllm.ai). --- ## About 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 - Fast model execution with CUDA/HIP graph - Quantization: [GPTQ](https://arxiv.org/abs/2210.17323), [AWQ](https://arxiv.org/abs/2306.00978), [SqueezeLLM](https://arxiv.org/abs/2306.07629), FP8 KV Cache - 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 GPUs and AMD GPUs - (Experimental) Prefix caching support - (Experimental) Multi-lora support 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.) - Command-R (`CohereForAI/c4ai-command-r-v01`, etc.) - DBRX (`databricks/dbrx-base`, `databricks/dbrx-instruct` etc.) - DeciLM (`Deci/DeciLM-7B`, `Deci/DeciLM-7B-instruct`, etc.) - Falcon (`tiiuae/falcon-7b`, `tiiuae/falcon-40b`, `tiiuae/falcon-rw-7b`, etc.) - Gemma (`google/gemma-2b`, `google/gemma-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.) - InternLM2 (`internlm/internlm2-7b`, `internlm/internlm2-chat-7b`, etc.) - Jais (`core42/jais-13b`, `core42/jais-13b-chat`, `core42/jais-30b-v3`, `core42/jais-30b-chat-v3`, etc.) - LLaMA & LLaMA-2 (`meta-llama/Llama-2-70b-hf`, `lmsys/vicuna-13b-v1.3`, `young-geng/koala`, `openlm-research/open_llama_13b`, etc.) - LLavA-1.5 (`llava-hf/llava-1.5-7b-hf`, `llava-hf/llava-1.5-13b-hf`, etc.) - MiniCPM (`openbmb/MiniCPM-2B-sft-bf16`, `openbmb/MiniCPM-2B-dpo-bf16`, 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`, `mistral-community/Mixtral-8x22B-v0.1`, etc.) - MPT (`mosaicml/mpt-7b`, `mosaicml/mpt-30b`, etc.) - OLMo (`allenai/OLMo-1B`, `allenai/OLMo-7B`, etc.) - OPT (`facebook/opt-66b`, `facebook/opt-iml-max-30b`, etc.) - Orion (`OrionStarAI/Orion-14B-Base`, `OrionStarAI/Orion-14B-Chat`, etc.) - Phi (`microsoft/phi-1_5`, `microsoft/phi-2`, etc.) - Qwen (`Qwen/Qwen-7B`, `Qwen/Qwen-7B-Chat`, etc.) - Qwen2 (`Qwen/Qwen1.5-7B`, `Qwen/Qwen1.5-7B-Chat`, etc.) - Qwen2MoE (`Qwen/Qwen1.5-MoE-A2.7B`, `Qwen/Qwen1.5-MoE-A2.7B-Chat`, etc.) - StableLM(`stabilityai/stablelm-3b-4e1t`, `stabilityai/stablelm-base-alpha-7b-v2`, etc.) - Starcoder2(`bigcode/starcoder2-3b`, `bigcode/starcoder2-7b`, `bigcode/starcoder2-15b`, etc.) - Xverse (`xverse/XVERSE-7B-Chat`, `xverse/XVERSE-13B-Chat`, `xverse/XVERSE-65B-Chat`, etc.) - Yi (`01-ai/Yi-6B`, `01-ai/Yi-34B`, etc.) Install vLLM with pip or [from source](https://vllm.readthedocs.io/en/latest/getting_started/installation.html#build-from-source): ```bash pip install vllm ``` ## Getting Started Visit our [documentation](https://vllm.readthedocs.io/en/latest/) to get started. - [Installation](https://vllm.readthedocs.io/en/latest/getting_started/installation.html) - [Quickstart](https://vllm.readthedocs.io/en/latest/getting_started/quickstart.html) - [Supported Models](https://vllm.readthedocs.io/en/latest/models/supported_models.html) ## 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](https://arxiv.org/abs/2309.06180): ```bibtex @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} } ```