base on Large Language Model Deployment Toolkit # FastDeploy 2.0: 大模型推理部署 <p align="center"> <a href="./LICENSE"><img src="https://img.shields.io/badge/license-Apache%202-dfd.svg"></a> <a href="https://github.com/PaddlePaddle/FastDeploy/releases"><img src="https://img.shields.io/github/v/release/PaddlePaddle/FastDeploy?color=ffa"></a> <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> FastDeploy升级2.0版本支持多种大模型推理（当前仅支持Qwen2，更多模型即将更新支持)，其推理部署功能涵盖： - 一行命令即可快速实现模型的服务化部署，并支持流式生成 - 利用张量并行技术加速模型推理 - 支持 PagedAttention 与 continuous batching（动态批处理） - 兼容 OpenAI 的 HTTP 协议 - 提供 Weight only int8/int4 无损压缩方案 - 支持 Prometheus Metrics 指标 > 注意: 如果你还在使用FastDeploy部署小模型(如PaddleClas/PaddleOCR等CV套件模型)，请checkout [release/1.1.0分支](https://github.com/PaddlePaddle/FastDeploy/tree/release/1.1.0)。 ## 环境依赖 - A800/H800/H100 - Python>=3.10 - CUDA>=12.3 - CUDNN>=9.5 - Linux X64 ## 安装 ### Docker安装(推荐) ``` docker pull ccr-2vdh3abv-pub.cnc.bj.baidubce.com/paddlepaddle/fastdeploy:2.0.0.0-alpha ``` ### 源码安装 #### 安装PaddlePaddle > 注意安装nightly build版本，代码版本需新于2025.05.30，详见[PaddlePaddle安装](https://www.paddlepaddle.org.cn/en/install/quick?docurl=/documentation/docs/en/develop/install/pip/linux-pip_en.html)，指定安装CUDA 12.6 develop(Nightly build)版本。 ``` python -m pip install --pre paddlepaddle-gpu -i https://www.paddlepaddle.org.cn/packages/nightly/cu126/ ``` #### 编译安装FastDeploy ``` # 编译 cd FastDeploy bash build.sh # 安装 pip install dist/fastdeploy-2.0.0a0-py3-none-any.whl ``` ## 快速使用 在安装后，执行如下命令快速部署Qwen2模型, 更多参数的配置与含义参考[参数说明](docs/serving.md). ``` shell # 下载与解压Qwen模型 wget https://fastdeploy.bj.bcebos.com/llm/models/Qwen2-7B-Instruct.tar.gz && tar xvf Qwen2-7B-Instruct.tar.gz # 指定单卡部署 python -m fastdeploy.entrypoints.openai.api_server --model ./Qwen2-7B-Instruct --port 8188 --tensor-parallel-size 1 ``` 使用如下命令请求模型服务 ``` shell curl -X POST "http://0.0.0.0:8188/v1/chat/completions" \ -H "Content-Type: application/json" \ -d '{ "messages": [ {"role": "user", "content": "你好，你的名字是什么？"} ] }' ``` 响应结果如下所示 ``` json { "id": "chatcmpl-db662f47-7c8c-4945-9a7a-db563b2ddd8d", "object": "chat.completion", "created": 1749451045, "model": "default", "choices": [ { "index": 0, "message": { "role": "assistant", "content": "你好！我叫通义千问。", "reasoning_content": null }, "finish_reason": "stop" } ], "usage": { "prompt_tokens": 25, "total_tokens": 35, "completion_tokens": 10, "prompt_tokens_details": null } } ``` FastDeploy提供与OpenAI完全兼容的服务API(字段`model`与`api_key`目前不支持，设定会被忽略)，用户也可基于openai python api请求服务。 ## 部署文档 - [本地部署](docs/offline_inference.md) - [服务部署](docs/serving.md) - [服务metrics](docs/metrics.md) # 代码说明 - [代码目录说明](docs/code_guide.md) - FastDeploy的使用中存在任何建议和问题，欢迎通过issue反馈。 # 开源说明 FastDeploy遵循[Apache-2.0开源协议](./LICENSE)。 在本项目的开发中，为了对齐[vLLM](https://github.com/vllm-project/vllm)使用接口，参考和直接使用了部分vLLM代码，在此表示感谢。 ", Assign "at most 3 tags" to the expected json: {"id":"4046","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"