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[Docs]Supplement the English and Chinese user documentation for Tool calling (#4895)
* tool calling文档编写,v1.0

* tool calling文档编写,v1.0

* tool calling文档编写,v1.0

* tool calling doc,v1.1

* tool calling doc,v1.1

* tool calling doc,v1.1

* tool calling doc,v1.1
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English | 简体中文

PaddlePaddle%2FFastDeploy | Trendshift
Installation | Quick Start | Supported Models


FastDeploy : Inference and Deployment Toolkit for LLMs and VLMs based on PaddlePaddle

News

[2025-09] FastDeploy v2.2 is newly released! It now offers compatibility with models in the HuggingFace ecosystem, has further optimized performance, and newly adds support for baidu/ERNIE-21B-A3B-Thinking!

About

FastDeploy is an inference and deployment toolkit for large language models and visual language models based on PaddlePaddle. It delivers production-ready, out-of-the-box deployment solutions with core acceleration technologies:

  • 🚀 Load-Balanced PD Disaggregation: Industrial-grade solution featuring context caching and dynamic instance role switching. Optimizes resource utilization while balancing SLO compliance and throughput.
  • 🔄 Unified KV Cache Transmission: Lightweight high-performance transport library with intelligent NVLink/RDMA selection.
  • 🤝 OpenAI API Server and vLLM Compatible: One-command deployment with vLLM interface compatibility.
  • 🧮 Comprehensive Quantization Format Support: W8A16, W8A8, W4A16, W4A8, W2A16, FP8, and more.
  • Advanced Acceleration Techniques: Speculative decoding, Multi-Token Prediction (MTP) and Chunked Prefill.
  • 🖥️ Multi-Hardware Support: NVIDIA GPU, Kunlunxin XPU, Hygon DCU, Ascend NPU, Iluvatar GPU, Enflame GCU, MetaX GPU, Intel Gaudi etc.

Requirements

  • OS: Linux
  • Python: 3.10 ~ 3.12

Installation

FastDeploy supports inference deployment on NVIDIA GPUs, Kunlunxin XPUs, Iluvatar GPUs, Enflame GCUs, Hygon DCUs and other hardware. For detailed installation instructions:

Note: We are actively working on expanding hardware support. Additional hardware platforms including Ascend NPU are currently under development and testing. Stay tuned for updates!

Get Started

Learn how to use FastDeploy through our documentation:

Supported Models

Learn how to download models, enable using the torch format, and more:

Advanced Usage

Acknowledgement

FastDeploy is licensed under the Apache-2.0 open-source license. During development, portions of vLLM code were referenced and incorporated to maintain interface compatibility, for which we express our gratitude.

Description
️An Easy-to-use and Fast Deep Learning Model Deployment Toolkit for ☁️Cloud 📱Mobile and 📹Edge. Including Image, Video, Text and Audio 20+ main stream scenarios and 150+ SOTA models with end-to-end optimization, multi-platform and multi-framework support.
Readme Apache-2.0 410 MiB
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