[Doc] 增加中英文切换 (#3318)

* 增加中英文切换

* 增加中英文切换

* 修改readme
This commit is contained in:
yangjianfengo1
2025-08-12 11:20:45 +08:00
committed by GitHub
parent b21272d9ff
commit b808c49585
19 changed files with 213 additions and 15 deletions

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@@ -15,7 +15,7 @@ jobs:
- uses: actions/setup-python@v5
with:
python-version: 3.x
- run: pip install mkdocs-material mkdocs-get-deps mkdocs-material-extensions mkdocs-multilang
- run: pip install mkdocs-material mkdocs-get-deps mkdocs-material-extensions mkdocs-multilang mkdocs-static-i18n
- name: Deploy to GitHub Pages
env:
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}

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@@ -1,3 +1,4 @@
English | [简体中文](README_CN.md)
<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>
@@ -68,7 +69,7 @@ Learn how to use FastDeploy through our documentation:
- [Offline Inference Development](./docs/offline_inference.md)
- [Online Service Deployment](./docs/online_serving/README.md)
- [Full Supported Models List](./docs/supported_models.md)
- [Optimal Deployment](./docs/optimal_deployment/README.md)
- [Best Practices](./docs/best_practices/README.md)
## Supported Models

93
README_CN.md Normal file
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@@ -0,0 +1,93 @@
[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>
--------------------------------------------------------------------------------
# FastDeploy 2.0: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
**[2025-07] 《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** 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)
**注意:** 我们正在积极拓展硬件支持范围。目前包括昇腾AscendNPU、海光HygonDCU 和摩尔线程MetaXGPU 在内的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)
- [模型支持列表](./docs/zh/supported_models.md)
- [最佳实践](./docs/zh/best_practices/README.md)
## 支持模型列表
| Model | Data Type | PD Disaggregation | Chunked Prefill | Prefix Caching | MTP | CUDA Graph | Maximum Context Length |
|:--- | :------- | :---------- | :-------- | :-------- | :----- | :----- | :----- |
|ERNIE-4.5-300B-A47B | BF16/WINT4/WINT8/W4A8C8/WINT2/FP8 | ✅| ✅ | ✅|✅(WINT4)| WIP |128K |
|ERNIE-4.5-300B-A47B-Base| BF16/WINT4/WINT8 | ✅| ✅ | ✅|✅(WINT4)| WIP | 128K |
|ERNIE-4.5-VL-424B-A47B | BF16/WINT4/WINT8 | WIP | ✅ | WIP | ❌ | WIP |128K |
|ERNIE-4.5-VL-28B-A3B | BF16/WINT4/WINT8 | ❌ | ✅ | WIP | ❌ | WIP |128K |
|ERNIE-4.5-21B-A3B | BF16/WINT4/WINT8/FP8 | ❌ | ✅ | ✅ | WIP | ✅|128K |
|ERNIE-4.5-21B-A3B-Base | BF16/WINT4/WINT8/FP8 | ❌ | ✅ | ✅ | WIP | ✅|128K |
|ERNIE-4.5-0.3B | BF16/WINT8/FP8 | ❌ | ✅ | ✅ | ❌ | ✅| 128K |
## 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。

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## Environmental Preparation
### 1.1 Hardware requirements
The minimum number of GPUs required to deploy `ERNIE-4.5-0.3B` on the following hardware for each quantization is as follows:
| | WINT8 | WINT4 | FP8 |
| | WINT8 | WINT4 | FP8 |
|-----|-----|-----|-----|
|H800 80GB| 1 | 1 | 1 |
|A800 80GB| 1 | 1 | / |

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@@ -2,7 +2,8 @@
## Environmental Preparation
### 1.1 Hardware requirements
The minimum number of GPUs required to deploy `ERNIE-4.5-21B-A3B` on the following hardware for each quantization is as follows:
| | WINT8 | WINT4 | FP8 |
| | WINT8 | WINT4 | FP8 |
|-----|-----|-----|-----|
|H800 80GB| 1 | 1 | 1 |
|A800 80GB| 1 | 1 | / |

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@@ -2,7 +2,8 @@
## Environmental Preparation
### 1.1 Hardware requirements
The minimum number of GPUs required to deploy `ERNIE-4.5-300B-A47B` on the following hardware for each quantization is as follows:
| | WINT8 | WINT4 | FP8 | WINT2 | W4A8 |
| | WINT8 | WINT4 | FP8 | WINT2 | W4A8 |
|-----|-----|-----|-----|-----|-----|
|H800 80GB| 8 | 4 | 8 | 2 | 4 |
|A800 80GB| 8 | 4 | / | 2 | 4 |

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@@ -5,6 +5,7 @@
### 1.1 Support Status
The minimum number of cards required for deployment on the following hardware is as follows:
| Device [GPU Mem] | WINT4 | WINT8 | BFLOAT16 |
|:----------:|:----------:|:------:| :------:|
| A30 [24G] | 2 | 2 | 4 |

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@@ -4,6 +4,7 @@
## 1. Environment Preparation
### 1.1 Support Status
The minimum number of cards required for deployment on the following hardware is as follows:
| Device [GPU Mem] | WINT4 | WINT8 | BFLOAT16 |
|:----------:|:----------:|:------:| :------:|
| H20 [144G] | 8 | 8 | 8 |

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## 一、环境准备
### 1.1 支持情况
ERNIE-4.5-0.3B 各量化精度,在下列硬件上部署所需要的最小卡数如下:
| | WINT8 | WINT4 | FP8 |
|-----|-----|-----|-----|
|H800 80GB| 1 | 1 | 1 |

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@@ -2,6 +2,7 @@
## 一、环境准备
### 1.1 支持情况
ERNIE-4.5-21B-A3B 各量化精度,在下列硬件上部署所需要的最小卡数如下:
| | WINT8 | WINT4 | FP8 |
|-----|-----|-----|-----|
|H800 80GB| 1 | 1 | 1 |

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@@ -2,6 +2,7 @@
## 一、环境准备
### 1.1 支持情况
ERNIE-4.5-300B-A47B各量化精度在下列硬件上部署所需要的最小卡数如下
| | WINT8 | WINT4 | FP8 | WINT2 | W4A8 |
|-----|-----|-----|-----|-----|-----|
|H800 80GB| 8 | 4 | 8 | 2 | 4 |

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@@ -4,6 +4,7 @@
## 一、环境准备
### 1.1 支持情况
在下列硬件上部署所需要的最小卡数如下:
| 设备[显存] | WINT4 | WINT8 | BFLOAT16 |
|:----------:|:----------:|:------:| :------:|
| A30 [24G] | 2 | 2 | 4 |

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@@ -4,6 +4,7 @@
## 一、环境准备
### 1.1 支持情况
在下列硬件上部署所需要的最小卡数如下:
| 设备[显存] | WINT4 | WINT8 | BFLOAT16 |
|:----------:|:----------:|:------:| :------:|
| H20 [144G] | 8 | 8 | 8 |

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@@ -3,6 +3,7 @@
## 准备机器
首先您需要准备以下配置的机器
| CPU | 内存 | 天数 | 硬盘|
|-----|------|-----|-----|
| x86 | 1TB| 8xBI150| 1TB|

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site_name: 'FastDeploy 2.0: Large Language Model Deployement'
repo_url: https://github.com/PaddlePaddle/FastDeploy
repo_name: FastDeploy
theme:
name: material
highlightjs: true
icon:
repo: fontawesome/brands/github
palette:
- media: "(prefers-color-scheme: light)" # 浅色
scheme: default
primary: indigo
accent: indigo
toggle:
icon: material/brightness-7
name: Switch to dark mode
- media: "(prefers-color-scheme: dark)" # 深色
scheme: slate
primary: black
accent: indigo
toggle:
icon: material/brightness-4
name: Switch to system preference
plugins:
- search
- i18n:
docs_structure: folder
fallback_to_default: true
reconfigure_material: true
reconfigure_search: true
languages:
- locale: en
default: true
name: English
site_name: 'FastDeploy 2.0: Large Language Model Deployement'
build: true
- locale: zh
name: 简体中文
site_name: 飞桨大语言模型推理部署工具包
link: /zh/
nav_translations:
FastDeploy 2.0: FastDeploy 2.0
Quick Start: 快速入门
Installation: 安装
Nvidia GPU: 英伟达 GPU
KunlunXin XPU: 昆仑芯 XPU
HYGON DCU: 海光 DCU
Enflame S60: 燧原 S60
Iluvatar CoreX: 天数 CoreX
Quick Deployment For ERNIE-4.5-0.3B: ERNIE-4.5-0.3B快速部署
Quick Deployment for ERNIE-4.5-VL-28B-A3B: ERNIE-4.5-VL-28B-A3B快速部署
ERNIE-4.5-300B-A47B: ERNIE-4.5-300B-A47B快速部署
ERNIE-4.5-VL-424B-A47B: ERNIE-4.5-VL-424B-A47B快速部署
Online Serving: 在线服务
OpenAI-Compitable API Server: 兼容 OpenAI 协议的服务化部署
Monitor Metrics: 监控Metrics
Scheduler: 调度器
Offline Inference: 离线推理
Optimal Deployment: 最佳实践
ERNIE-4.5-0.3B: ERNIE-4.5-0.3B
ERNIE-4.5-21B-A3B: ERNIE-4.5-21B-A3B
ERNIE-4.5-300B-A47B: ERNIE-4.5-300B-A47B
ERNIE-4.5-VL-28B-A3B: ERNIE-4.5-VL-28B-A3B
ERNIE-4.5-VL-424B-A47B: ERNIE-4.5-VL-424B-A47B
FAQ: 常见问题
Quantization: 量化
Overview: 概述
Online Quantization: 在线量化
WINT2 Quantization: WINT2量化
Features: 特性
Prefix Caching: 前缀缓存
Disaggregation: 分离式部署
Chunked Prefill: 分块预填充
Load Balance: 负载均衡
Speculative Decoding: 投机解码
Structured Outputs: 结构化输出
Reasoning Output: 思考链内容
Early Stop: 早停功能
Plugins: 插件机制
Sampling: 采样策略
Supported Models: 支持模型列表
Benchmark: 基准测试
Usage: 用法
Log Description: 日志说明
Code Overview: 代码概述
Environment Variables: 环境变量
nav:
- 'FastDeploy 2.0': index.md
- 'Quick Start':
- Installation:
- 'Nvidia GPU': get_started/installation/nvidia_gpu.md
- 'KunlunXin XPU': get_started/installation/kunlunxin_xpu.md
- 'HYGON DCU': get_started/installation/hygon_dcu.md
- 'Enflame S60': get_started/installation/Enflame_gcu.md
- 'Iluvatar CoreX': get_started/installation/iluvatar_gpu.md
- 'Quick Deployment For ERNIE-4.5-0.3B-Paddle': get_started/quick_start.md
- 'Quick Deployment For ERNIE-4.5-0.3B': get_started/quick_start.md
- 'Quick Deployment for ERNIE-4.5-VL-28B-A3B': get_started/quick_start_vl.md
- 'ERNIE-4.5-300B-A47B': get_started/ernie-4.5.md
- 'ERNIE-4.5-VL-424B-A47B': get_started/ernie-4.5-vl.md
@@ -16,28 +105,32 @@ nav:
- 'Monitor Metrics': online_serving/metrics.md
- 'Scheduler': online_serving/scheduler.md
- 'Offline Inference': offline_inference.md
- Quantiation:
- Optimal Deployment:
- ERNIE-4.5-0.3B: best_practices/ERNIE-4.5-0.3B-Paddle.md
- ERNIE-4.5-21B-A3B: best_practices/ERNIE-4.5-21B-A3B-Paddle.md
- ERNIE-4.5-300B-A47B: best_practices/ERNIE-4.5-300B-A47B-Paddle.md
- ERNIE-4.5-VL-28B-A3B: best_practices/ERNIE-4.5-VL-28B-A3B-Paddle.md
- ERNIE-4.5-VL-424B-A47B: best_practices/ERNIE-4.5-VL-424B-A47B-Paddle.md
- FAQ: best_practices/FAQ.md
- Quantization:
- 'Overview': quantization/README.md
- 'Online Quantization': quantization/online_quantization.md
- 'WINT2 Quantization': quantization/wint2.md
- Features:
- 'Prefix Caching': features/prefix_caching.md
- 'Disaggration': features/disaggregated.md
- 'Disaggregation': features/disaggregated.md
- 'Chunked Prefill': features/chunked_prefill.md
- 'Load Balance': features/load_balance.md
- 'Speculative Decoding': features/speculative_decoding.md
- 'Structured Outputs': features/structured_outputs.md
- 'Reasoning Output': features/reasoning_output.md
- 'Early Stop': features/early_stop.md
- 'Plugins': features/plugins.md
- 'Sampling': features/sampling.md
- 'Supported Models': supported_models.md
- Benchmark: benchmark.md
- Usage:
- 'Log Description': usage/log.md
- 'Code Overview': usage/code_overview.md
- 'Environment Variables': usage/environment_variables.md
theme:
name: 'material'
highlightjs: true
icon:
repo: fontawesome/brands/github
repo_url: https://github.com/PaddlePaddle/FastDeploy
repo_name: FastDeploy