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FastDeploy/README_CN.md
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【FIX】Change the name of sparse attn from moba to plas (#4006)
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* 【FIX】Change the name of sparse attn from moba to plas (#3845)

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Co-authored-by: Jiang-Jia-Jun <163579578+Jiang-Jia-Jun@users.noreply.github.com>
2025-09-10 10:04:29 +08:00

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[English](README.md) | 简体中文
<p align="center">
<a href="https://github.com/PaddlePaddle/FastDeploy/releases"><img src="https://github.com/user-attachments/assets/42b0039f-39e3-4279-afda-6d1865dfbffb" width="500"></a>
</p>
<p align="center">
<a href=""><img src="https://img.shields.io/badge/python-3.10-aff.svg"></a>
<a href=""><img src="https://img.shields.io/badge/os-linux-pink.svg"></a>
<a href="https://github.com/PaddlePaddle/FastDeploy/graphs/contributors"><img src="https://img.shields.io/github/contributors/PaddlePaddle/FastDeploy?color=9ea"></a>
<a href="https://github.com/PaddlePaddle/FastDeploy/commits"><img src="https://img.shields.io/github/commit-activity/m/PaddlePaddle/FastDeploy?color=3af"></a>
<a href="https://github.com/PaddlePaddle/FastDeploy/issues"><img src="https://img.shields.io/github/issues/PaddlePaddle/FastDeploy?color=9cc"></a>
<a href="https://github.com/PaddlePaddle/FastDeploy/stargazers"><img src="https://img.shields.io/github/stars/PaddlePaddle/FastDeploy?color=ccf"></a>
</p>
<p align="center">
<a href="https://trendshift.io/repositories/4046" target="_blank"><img src="https://trendshift.io/api/badge/repositories/4046" alt="PaddlePaddle%2FFastDeploy | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a></br>
<a href="https://paddlepaddle.github.io/FastDeploy/zh/get_started/installation/nvidia_gpu/"><b> 安装指导 </b></a>
|
<a href="https://paddlepaddle.github.io/FastDeploy/zh/get_started/quick_start"><b> 快速入门 </b></a>
|
<a href="https://paddlepaddle.github.io/FastDeploy/zh/supported_models/"><b> 支持模型列表 </b></a>
</p>
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# FastDeploy :基于飞桨的大语言模型与视觉语言模型推理部署工具包
## 最新活动
**[2025-09] 🔥 FastDeploy v2.2 全新发布**: HuggingFace生态模型兼容性能进一步优化更新增对[baidu/ERNIE-21B-A3B-Thinking](https://huggingface.co/baidu/ERNIE-4.5-21B-A3B-Thinking)支持!
**[2025-08] FastDeploy v2.1 发布**:全新的KV Cache调度策略更多模型支持PD分离和CUDA Graph昆仑、海光等更多硬件支持增强全方面优化服务和推理引擎的性能。
**[2025-07] 《FastDeploy2.0推理部署实测》专题活动已上线!** 完成文心4.5系列开源模型的推理部署等任务即可获得骨瓷马克杯等FastDeploy2.0官方周边及丰富奖金!🎁 欢迎大家体验反馈~ 📌[报名地址](https://www.wjx.top/vm/meSsp3L.aspx#) 📌[活动详情](https://github.com/PaddlePaddle/FastDeploy/discussions/2728)
## 关于
**FastDeploy** 是基于飞桨PaddlePaddle的大语言模型LLM与视觉语言模型VLM推理部署工具包提供**开箱即用的生产级部署方案**,核心技术特性包括:
- 🚀 **负载均衡式PD分解**工业级解决方案支持上下文缓存与动态实例角色切换在保障SLO达标和吞吐量的同时优化资源利用率
- 🔄 **统一KV缓存传输**轻量级高性能传输库支持智能NVLink/RDMA选择
- 🤝 **OpenAI API服务与vLLM兼容**:单命令部署,兼容[vLLM](https://github.com/vllm-project/vllm/)接口
- 🧮 **全量化格式支持**W8A16、W8A8、W4A16、W4A8、W2A16、FP8等
-**高级加速技术**推测解码、多令牌预测MTP及分块预填充
- 🖥️ **多硬件支持**NVIDIA GPU、昆仑芯XPU、海光DCU、昇腾NPU、天数智芯GPU、燧原GCU、沐曦GPU等
## 要求
- 操作系统: Linux
- Python: 3.10 ~ 3.12
## 安装
FastDeploy 支持在**英伟达NVIDIAGPU**、**昆仑芯KunlunxinXPU**、**天数IluvatarGPU**、**燧原EnflameGCU**、**海光HygonDCU** 以及其他硬件上进行推理部署。详细安装说明如下:
- [英伟达 GPU](./docs/zh/get_started/installation/nvidia_gpu.md)
- [昆仑芯 XPU](./docs/zh/get_started/installation/kunlunxin_xpu.md)
- [天数 CoreX](./docs/zh/get_started/installation/iluvatar_gpu.md)
- [燧原 S60](./docs/zh/get_started/installation/Enflame_gcu.md)
- [海光 DCU](./docs/zh/get_started/installation/hygon_dcu.md)
- [沐曦 GPU](./docs/zh/get_started/installation/metax_gpu.md.md)
**注意:** 我们正在积极拓展硬件支持范围。目前包括昇腾AscendNPU 等其他硬件平台正在开发测试中。敬请关注更新!
## 入门指南
通过我们的文档了解如何使用 FastDeploy
- [10分钟快速部署](./docs/zh/get_started/quick_start.md)
- [ERNIE-4.5 部署](./docs/zh/get_started/ernie-4.5.md)
- [ERNIE-4.5-VL 部署](./docs/zh/get_started/ernie-4.5-vl.md)
- [离线推理](./docs/zh/offline_inference.md)
- [在线服务](./docs/zh/online_serving/README.md)
- [最佳实践](./docs/zh/best_practices/README.md)
## 支持模型列表
通过我们的文档了解如何下载模型如何支持torch格式等
- [模型支持列表](./docs/zh/supported_models.md)
## 进阶用法
- [量化](./docs/zh/quantization/README.md)
- [分离式部署](./docs/zh/features/disaggregated.md)
- [投机解码](./docs/zh/features/speculative_decoding.md)
- [前缀缓存](./docs/zh/features/prefix_caching.md)
- [分块预填充](./docs/zh/features/chunked_prefill.md)
## 致谢
FastDeploy 依据 [Apache-2.0 开源许可证](./LICENSE). 进行授权。在开发过程中,我们参考并借鉴了 [vLLM](https://github.com/vllm-project/vllm) 的部分代码,以保持接口兼容性,在此表示衷心感谢。