From 9f75346603bd7c6d04b809582d9649b4fc3e10f1 Mon Sep 17 00:00:00 2001 From: wehub-resource-sync Date: Mon, 13 Jul 2026 09:53:31 +0000 Subject: [PATCH] docs: make Chinese README the default --- README.md | 118 ++++++++++++++++++++++++++++-------------------------- 1 file changed, 62 insertions(+), 56 deletions(-) diff --git a/README.md b/README.md index 4277743..1e0c925 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,9 @@ + +> [!NOTE] +> 本文档由 WeHub 基于上游 README 翻译整理，属于社区翻译，非官方中文文档。 +> [English](./README.en.md) · [原始项目](https://github.com/vllm-project/vllm) · [上游 README](https://github.com/vllm-project/vllm/blob/HEAD/README.md) +> 原作者、版权与许可证归属以原始项目及本仓库 LICENSE 文件为准。 +

@@ -7,84 +13,84 @@

-Easy, fast, and cheap LLM serving for everyone +简单、快速、低成本的 LLM serving，人人可用

-| Documentation | Blog | Paper | Twitter/X | User Forum | Developer Slack | +| 文档 | 博客 | 论文 | Twitter/X | 用户论坛 | 开发者 Slack |

-🔥 We have built a vLLM website to help you get started with vLLM. Please visit [vllm.ai](https://vllm.ai) to learn more. -For events, please visit [vllm.ai/events](https://vllm.ai/events) to join us. +🔥 我们搭建了 vLLM 官网，帮助你上手 vLLM。请访问 [vllm.ai](https://vllm.ai) 了解更多。 +有关活动，请访问 [vllm.ai/events](https://vllm.ai/events) 加入我们。 --- -## About +## 关于 -vLLM is a fast and easy-to-use library for LLM inference and serving. +vLLM 是一个用于 LLM 推理（inference）与 serving 的快速、易用库。 -Originally developed in the [Sky Computing Lab](https://sky.cs.berkeley.edu) at UC Berkeley, vLLM has grown into one of the most active open-source AI projects built and maintained by a diverse community of many dozens of academic institutions and companies from over 2000 contributors. +最初由 UC Berkeley 的 [Sky Computing Lab](https://sky.cs.berkeley.edu) 开发，vLLM 已成长为最活跃的开源 AI 项目之一，由来自全球 2000 多名贡献者的众多学术机构与企业组成的多元化社区共同构建与维护。 -vLLM is fast with: +vLLM 之所以快速，得益于： -- State-of-the-art serving throughput -- Efficient management of attention key and value memory with [**PagedAttention**](https://blog.vllm.ai/2023/06/20/vllm.html) -- Continuous batching of incoming requests, chunked prefill, prefix caching -- Fast and flexible model execution with piecewise and full CUDA/HIP graphs -- Quantization: FP8, MXFP8/MXFP4, NVFP4, INT8, INT4, GPTQ/AWQ, GGUF, compressed-tensors, ModelOpt, TorchAO, and [more](https://docs.vllm.ai/en/latest/features/quantization/index.html) -- Optimized attention kernels including FlashAttention, FlashInfer, TRTLLM-GEN, FlashMLA, and Triton -- Optimized GEMM/MoE kernels for various precisions using CUTLASS, TRTLLM-GEN, CuTeDSL -- Speculative decoding including n-gram, suffix, EAGLE, DFlash -- Automatic kernel generation and graph-level transformations using torch.compile -- Disaggregated prefill, decode, and encode +- 业界领先的 serving 吞吐量 +- 通过 [**PagedAttention**](https://blog.vllm.ai/2023/06/20/vllm.html) 高效管理注意力 key 与 value 内存 +- 对入站请求的连续批处理（continuous batching）、分块 prefill、前缀缓存（prefix caching） +- 借助分段与完整 CUDA/HIP 图实现快速、灵活的模型执行 +- 量化：FP8、MXFP8/MXFP4、NVFP4、INT8、INT4、GPTQ/AWQ、GGUF、compressed-tensors、ModelOpt、TorchAO 以及[更多](https://docs.vllm.ai/en/latest/features/quantization/index.html) +- 优化的注意力内核，包括 FlashAttention、FlashInfer、TRTLLM-GEN、FlashMLA 和 Triton +- 使用 CUTLASS、TRTLLM-GEN、CuTeDSL 为多种精度优化的 GEMM/MoE 内核 +- 推测解码（speculative decoding），包括 n-gram、suffix、EAGLE、DFlash +- 使用 torch.compile 进行自动内核生成与图级变换 +- 解耦的 prefill、decode 与 encode -vLLM is flexible and easy to use with: +vLLM 灵活且易用，具备： -- Seamless integration with popular Hugging Face models -- High-throughput serving with various decoding algorithms, including *parallel sampling*, *beam search*, and more -- Tensor, pipeline, data, expert, and context parallelism for distributed inference -- Streaming outputs -- Generation of structured outputs using xgrammar or guidance -- Tool calling and reasoning parsers -- OpenAI-compatible API server, plus Anthropic Messages API and gRPC support -- Efficient multi-LoRA support for dense and MoE layers -- Support for NVIDIA GPUs, AMD GPUs, and x86/ARM/PowerPC CPUs. Additionally, diverse hardware plugins such as Google TPUs, Intel Gaudi, IBM Spyre, Huawei Ascend, Rebellions NPU, Apple Silicon, MetaX GPU, and more. +- 与流行的 Hugging Face 模型无缝集成 +- 多种解码算法下的高吞吐量 serving，包括*并行采样*（parallel sampling）、*束搜索*（beam search）等 +- 面向分布式推理的张量、流水线、数据、专家与上下文并行 +- 流式输出 +- 使用 xgrammar 或 guidance 生成结构化输出 +- 工具调用与推理解析器 +- 兼容 OpenAI 的 API 服务器，并支持 Anthropic Messages API 与 gRPC +- 对 dense 与 MoE 层的高效 multi-LoRA 支持 +- 支持 NVIDIA GPU、AMD GPU 以及 x86/ARM/PowerPC CPU；此外还提供 Google TPU、Intel Gaudi、IBM Spyre、华为 Ascend、Rebellions NPU、Apple Silicon、MetaX GPU 等多种硬件插件。 -vLLM seamlessly supports 200+ model architectures on Hugging Face, including: +vLLM 在 Hugging Face 上无缝支持 200+ 种模型架构，包括： -- Decoder-only LLMs (e.g., Llama, Qwen, Gemma) -- Mixture-of-Expert LLMs (e.g., Mixtral, DeepSeek-V3, Qwen-MoE, GPT-OSS) -- Hybrid attention and state-space models (e.g., Mamba, Qwen3.5) -- Multi-modal models (e.g., LLaVA, Qwen-VL, Pixtral) -- Embedding and retrieval models (e.g., E5-Mistral, GTE, ColBERT) -- Reward and classification models (e.g., Qwen-Math) +- 仅解码器 LLM（例如 Llama、Qwen、Gemma） +- 混合专家（MoE）LLM（例如 Mixtral、DeepSeek-V3、Qwen-MoE、GPT-OSS） +- 混合注意力与状态空间模型（例如 Mamba、Qwen3.5） +- 多模态模型（例如 LLaVA、Qwen-VL、Pixtral） +- 嵌入与检索模型（例如 E5-Mistral、GTE、ColBERT） +- 奖励与分类模型（例如 Qwen-Math） -Find the full list of supported models [here](https://docs.vllm.ai/en/latest/models/supported_models.html). +完整支持的模型列表见[此处](https://docs.vllm.ai/en/latest/models/supported_models.html). -## Getting Started +## 快速开始 -Install vLLM with [`uv`](https://docs.astral.sh/uv/) (recommended) or `pip`: +使用 [`uv`](https://docs.astral.sh/uv/)（推荐）或 `pip` 安装 vLLM： ```bash uv pip install vllm ``` -Or [build from source](https://docs.vllm.ai/en/latest/getting_started/installation/gpu/index.html#build-wheel-from-source) for development. +或[从源码构建](https://docs.vllm.ai/en/latest/getting_started/installation/gpu/index.html#build-wheel-from-source) 以便开发。 -Visit our [documentation](https://docs.vllm.ai/en/latest/) to learn more. +访问我们的[文档](https://docs.vllm.ai/en/latest/) 了解更多。 -- [Installation](https://docs.vllm.ai/en/latest/getting_started/installation.html) -- [Quickstart](https://docs.vllm.ai/en/latest/getting_started/quickstart.html) -- [List of Supported Models](https://docs.vllm.ai/en/latest/models/supported_models.html) +- [安装](https://docs.vllm.ai/en/latest/getting_started/installation.html) +- [快速入门](https://docs.vllm.ai/en/latest/getting_started/quickstart.html) +- [支持的模型列表](https://docs.vllm.ai/en/latest/models/supported_models.html) -## Contributing +## 贡献 -We welcome and value any contributions and collaborations. -Please check out [Contributing to vLLM](https://docs.vllm.ai/en/latest/contributing/index.html) for how to get involved. +我们欢迎并重视任何贡献与合作。 +请参阅[为 vLLM 做贡献](https://docs.vllm.ai/en/latest/contributing/index.html) 了解如何参与。 -## Citation +## 引用 -If you use vLLM for your research, please cite our [paper](https://arxiv.org/abs/2309.06180): +如果你在研究中使用 vLLM，请引用我们的[论文](https://arxiv.org/abs/2309.06180): ```bibtex @inproceedings{kwon2023efficient, @@ -95,16 +101,16 @@ If you use vLLM for your research, please cite our [paper](https://arxiv.org/abs } ``` -## Contact Us +## 联系我们 -- For technical questions and feature requests, please use GitHub [Issues](https://github.com/vllm-project/vllm/issues) -- For discussing with fellow users, please use the [vLLM Forum](https://discuss.vllm.ai) -- For coordinating contributions and development, please use [Slack](https://slack.vllm.ai) -- For security disclosures, please use GitHub's [Security Advisories](https://github.com/vllm-project/vllm/security/advisories) feature -- For collaborations and partnerships, please contact us at [collaboration@vllm.ai](mailto:collaboration@vllm.ai) +- 技术问题与功能请求，请使用 GitHub [Issues](https://github.com/vllm-project/vllm/issues) +- 与其他用户交流，请使用 [vLLM 论坛](https://discuss.vllm.ai) +- 协调贡献与开发，请使用 [Slack](https://slack.vllm.ai) +- 安全漏洞披露，请使用 GitHub 的 [Security Advisories](https://github.com/vllm-project/vllm/security/advisories) 功能 +- 合作与商务洽谈，请通过 [collaboration@vllm.ai](mailto:collaboration@vllm.ai) 联系我们 -## Media Kit +## 媒体资源 -- If you wish to use vLLM's logo, please refer to [our media kit repo](https://github.com/vllm-project/media-kit) +- 如需使用 vLLM 徽标，请参阅[我们的媒体资源仓库](https://github.com/vllm-project/media-kit)