AI工具
AI工具 是 AI Skill Hub 本期精选AI工具之一。在 GitHub 上收获超过 14.4k 颗 Star，综合评分 8.5 分，整体质量较高。我们强烈推荐将其纳入你的 AI 工具库，帮助提升工作效率。
📚 深度解析
**为什么要使用开源工具而非商业 SaaS？**
对于个人开发者和有隐私需求的用户，本地部署的开源工具意味着数据不离本机，不受第三方服务商的数据政策约束。同时，开源工具通常没有使用次数限制和月度费用，一次安装即可长期使用，对于高频使用场景的总拥有成本（TCO）远低于订阅制商业工具。
**安装与环境准备**
AI工具 依赖 Python 运行环境。建议通过 pyenv（Python）或 nvm（Node.js）管理 Python 版本，避免全局环境污染。对于新手用户，推荐先创建虚拟环境（python -m venv venv && source venv/bin/activate），再安装依赖，这样即使出现问题也可以随时删除虚拟环境重新开始，不影响系统稳定性。
**社区与维护**
GitHub Issue 和 Discussion 是获取帮助的最快渠道。在提问前建议先检查 Closed Issues（已关闭的问题），大多数常见问题都已有解答。遇到 Bug 时，提供 pip list 的输出、完整错误堆栈和最小可复现示例，能显著提高开发者响应速度。AI Skill Hub 将持续追踪 AI工具 的版本更新，及时通知重要功能变化。
📋 工具概览
AI工具 是一款基于 Python 开发的开源工具，专注于 PEFT、Full-parameter、CPT 等核心功能。作为 GitHub 开源项目，它拥有活跃的社区支持和持续的版本迭代，代码完全透明可审计，支持本地部署以保护数据隐私。无论是个人使用还是集成到企业工作流，都能提供稳定可靠的解决方案。
📖 中文文档
AI工具 是一款基于 Python 开发的开源工具，专注于 PEFT、Full-parameter、CPT 等核心功能。作为 GitHub 开源项目，它拥有活跃的社区支持和持续的版本迭代，代码完全透明可审计，支持本地部署以保护数据隐私。无论是个人使用还是集成到企业工作流，都能提供稳定可靠的解决方案。
- 开源免费，支持本地部署，数据完全自主可控
- 活跃的 GitHub 开源社区，持续迭代更新
- 提供详细文档和使用示例，新手友好
- 支持自定义配置，灵活适配不同使用环境
- 可作为基础组件集成进现有技术栈或进行二次开发
- 本地部署运行，保护数据隐私，满足合规要求
- 自定义集成到现有系统，扩展技术栈能力
- 作为开源基础组件进行商业化二次开发
# 方式一：pip 安装（推荐）
pip install ms-swift
# 方式二：虚拟环境安装（推荐生产环境）
python -m venv .venv
source .venv/bin/activate # Windows: .venv\Scripts\activate
pip install ms-swift
# 方式三：从源码安装（获取最新功能）
git clone https://github.com/modelscope/ms-swift
cd ms-swift
pip install -e .
# 验证安装
python -c "import ms_swift; print('安装成功')"
- 访问 GitHub 仓库页面
- 按照 README 文档完成依赖安装
- 根据系统环境完成初始化配置
- 参考官方示例或文档开始使用
- 遇到问题可在 GitHub Issues 中查找解答
# 命令行使用
ms-swift --help
# 基本用法
ms-swift input_file -o output_file
# Python 代码中调用
import ms_swift
# 示例
result = ms_swift.process("input")
print(result)
# ms-swift 配置文件示例（config.yml） app: name: "ms-swift" debug: false log_level: "INFO" # 运行时指定配置文件 ms-swift --config config.yml # 或通过环境变量配置 export MS_SWIFT_API_KEY="your-key" export MS_SWIFT_OUTPUT_DIR="./output"
SWIFT (Scalable lightWeight Infrastructure for Fine-Tuning)
<p align="center"> <br> <img src="asset/banner.png"/> <br> <p> <p align="center"> <a href="https://modelscope.cn/home">ModelScope Community Website</a> <br> <a href="README_CN.md">中文</a>   ｜   English   </p>
<p align="center"> <img src="https://img.shields.io/badge/python-3.12-5be.svg"> <img src="https://img.shields.io/badge/pytorch-%E2%89%A52.0-orange.svg"> <a href="https://github.com/modelscope/modelscope/"><img src="https://img.shields.io/badge/modelscope-%E2%89%A51.23-5D91D4.svg"></a> <a href="https://pypi.org/project/ms-swift/"><img src="https://badge.fury.io/py/ms-swift.svg"></a> <a href="https://github.com/modelscope/ms-swift/blob/main/LICENSE"><img src="https://img.shields.io/github/license/modelscope/ms-swift"></a> <a href="https://pepy.tech/project/ms-swift"><img src="https://pepy.tech/badge/ms-swift"></a> <a href="https://github.com/modelscope/ms-swift/pulls"><img src="https://img.shields.io/badge/PR-welcome-55EB99.svg"></a> </p>
<p align="center"> <a href="https://trendshift.io/repositories/11937" target="_blank"><img src="https://trendshift.io/api/badge/repositories/11937" alt="modelscope/ms-swift | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a> </p>
<p align="center"> <a href="https://arxiv.org/abs/2408.05517">Paper</a>   ｜ <a href="https://swift.readthedocs.io/en/latest/">English Documentation</a>   ｜   <a href="https://swift.readthedocs.io/zh-cn/latest/">中文文档</a>   </p>
📝 Introduction
🍲 ms-swift is a large model and multimodal large model fine-tuning and deployment framework provided by the ModelScope community. It now supports training (pre-training, fine-tuning, human alignment), inference, evaluation, quantization, and deployment for 600+ text-only large models and 400+ multimodal large models. Large models include: Qwen3, Qwen3.5, InternLM3, GLM4.5, Mistral, DeepSeek-R1, Llama4, etc. Multimodal large models include: Qwen3-VL, Qwen3-Omni, Llava, InternVL3.5, MiniCPM-V-4, Ovis2.5, GLM4.5-V, DeepSeek-VL2, etc.
🍔 In addition, ms-swift integrates the latest training technologies, including Megatron parallelism techniques such as TP, PP, CP, EP to accelerate training, as well as numerous GRPO algorithm family reinforcement learning algorithms including: GRPO, DAPO, GSPO, SAPO, CISPO, RLOO, Reinforce++, etc. to enhance model intelligence. ms-swift supports a wide range of training tasks, including preference learning algorithms such as DPO, KTO, RM, CPO, SimPO, ORPO, as well as Embedding, Reranker, and sequence classification tasks. ms-swift provides full-pipeline support for large model training, including acceleration for inference, evaluation, and deployment modules using vLLM, SGLang, and LMDeploy, as well as model quantization using GPTQ, AWQ, BNB, and FP8 technologies.
Why Choose ms-swift?
- 🍎 Model Types: Supports 600+ text-only large models, 400+ multimodal large models, and All-to-All full modality models from training to deployment full pipeline, with Day-0 support for popular models.
- Dataset Types: Built-in 150+ datasets for pre-training, fine-tuning, human alignment, multimodal and various other tasks, with support for custom datasets. Users only need to prepare datasets for one-click training.
- Hardware Support: Supports A10/A100/H100, RTX series, T4/V100, CPU, MPS, and domestic hardware Ascend NPU, etc.
- Lightweight Training: Supports lightweight fine-tuning methods such as LoRA, QLoRA, DoRA, LoRA+, LLaMAPro, LongLoRA, LoRA-GA, ReFT, RS-LoRA, Adapter, LISA, etc.
- Quantized Training: Supports training on BNB, AWQ, GPTQ, AQLM, HQQ, EETQ quantized models, requiring only 9GB training resources for 7B models.
- Memory Optimization: GaLore, Q-Galore, UnSloth, Liger-Kernel, Flash-Attention 2/3, and Ulysses and Ring-Attention sequence parallelism techniques support, reducing memory consumption for long-text training.
- Distributed Training: Supports distributed data parallelism (DDP), device_map simple model parallelism, DeepSpeed ZeRO2 ZeRO3, FSDP/FSDP2, and Megatron distributed training technologies.
- 🍓 Multimodal Training: Supports multimodal packing technology to improve training speed by 100%+, supports mixed modality data training with text, images, video and audio, and supports independent control of vit/aligner/llm.
- Agent Training: Supports Agent templates, allowing one dataset to be used for training different models.
- 🍊 Training Tasks: Supports pre-training and instruction fine-tuning, as well as training tasks such as DPO, GKD, KTO, RM, CPO, SimPO, ORPO, and supports Embedding/Reranker and sequence classification tasks.
- 🥥 Megatron Parallelism: Provides TP/PP/SP/CP/ETP/EP/VPP parallel strategies to significantly boost MoE model training speed. Supports full-parameter and LoRA training methods for 300+ pure text large models and 100+ multimodal large models. Supports CPT/SFT/GRPO/DPO/KTO/RM training tasks.
- 🍉 Reinforcement Learning: Built-in rich GRPO family algorithms, including GRPO, DAPO, GSPO, SAPO, CISPO, CHORD, RLOO, Reinforce++, etc. Supports synchronous and asynchronous vLLM engine inference acceleration, with extensible reward functions, multi-turn inference Schedulers, and environments through plugins.
- Full-Pipeline Capabilities: Covers the entire workflow of training, inference, evaluation, quantization, and deployment.
- UI Training: Provides Web-UI interface for training, inference, evaluation, and quantization, completing the full pipeline for large models.
- Inference Acceleration: Supports Transformers, vLLM, SGLang, and LmDeploy inference acceleration engines, providing OpenAI interfaces for accelerating inference, deployment, and evaluation modules.
- Model Evaluation: Uses EvalScope as the evaluation backend, supporting 100+ evaluation datasets for evaluating text-only and multimodal models.
- Model Quantization: Supports quantization export for AWQ, GPTQ, FP8, and BNB. Exported models support inference acceleration using vLLM/SGLang/LmDeploy.
🛠️ Installation
To install using pip: ```shell pip install ms-swift -U
pip install git+https://github.com/modelscope/ms-swift.git
git clone https://github.com/modelscope/ms-swift.git cd ms-swift
The main branch is for swift 4.x. To install swift 3.x, please run the following command:
Deployment
CUDA_VISIBLE_DEVICES=0 swift deploy \
--model Qwen/Qwen3-4B-Instruct-2507 \
--infer_backend vllm🚀 Quick Start
10 minutes of self-cognition fine-tuning of Qwen3-4B-Instruct-2507 on a single 3090 GPU:
✨ Usage
Here is a minimal example of training to deployment using ms-swift. For more details, you can check the examples.
- If you want to use other models or datasets (including multimodal models and datasets), you only need to modify
--modelto specify the corresponding model's ID or path, and modify--datasetto specify the corresponding dataset's ID or path. - By default, ModelScope is used for downloading models and datasets. If you want to use HuggingFace, simply specify
--use_hf true.
| Useful Links |
|---|
| [🔥Command Line Parameters](https://swift.readthedocs.io/en/latest/Instruction/Command-line-parameters.html) |
| [Megatron-SWIFT](https://swift.readthedocs.io/en/latest/Megatron-SWIFT/Quick-start.html) |
| [GRPO](https://swift.readthedocs.io/en/latest/Instruction/GRPO/GetStarted/GRPO.html) |
| [Supported Models and Datasets](https://swift.readthedocs.io/en/latest/Instruction/Supported-models-and-datasets.html) |
| [Custom Models](https://swift.readthedocs.io/en/latest/Customization/Custom-model.html), [🔥Custom Datasets](https://swift.readthedocs.io/en/latest/Customization/Custom-dataset.html) |
| [LLM Tutorial](https://github.com/modelscope/modelscope-classroom/tree/main/LLM-tutorial) |
Command Line Interface (Recommended)
```shell
Interface Inference
CUDA_VISIBLE_DEVICES=0 swift app \
--model Qwen/Qwen3-4B-Instruct-2507 \
--stream true \
--infer_backend transformers \
--max_new_tokens 2048Retrieve the model and template, and add a trainable LoRA module
model, tokenizer = get_model_processor(model_id_or_path, ...) template = get_template(tokenizer, ...) lora_config = LoraConfig(...) model = get_peft_model(model, lora_config)
高质量的AI工具，支持多种LLM
- 需要 ms-swift 解决具体问题的开发者与运营人员
- 先在测试环境跑通最小用例，再接入生产数据
- API key 直接提交到 git 仓库（请用 .env 并加入 .gitignore）
- Python 依赖冲突：建议用 venv / uv 隔离环境
- 云端托管：可放在 Vercel / Railway / Fly.io 等 PaaS 平台
⚡ 核心功能
- 开源免费，支持本地部署，数据完全自主可控
- 活跃的 GitHub 开源社区，持续迭代更新
- 提供详细文档和使用示例，新手友好
- 支持自定义配置，灵活适配不同使用环境
- 可作为基础组件集成进现有技术栈或进行二次开发
- 需要 ms-swift 解决具体问题的开发者与运营人员
- 先在测试环境跑通最小用例，再接入生产数据
- API key 直接提交到 git 仓库（请用 .env 并加入 .gitignore）
- Python 依赖冲突：建议用 venv / uv 隔离环境
👥 适合人群
🎯 使用场景
- 本地部署运行，保护数据隐私，满足合规要求
- 自定义集成到现有系统，扩展技术栈能力
- 作为开源基础组件进行商业化二次开发
⚖️ 优点与不足
- +GitHub 14.4k Star，社区高度认可
- +Apache-2.0 协议，可免费商用
- +完全开源免费，无授权费用
- +本地部署，数据完全自主可控
- +开发者社区支持，遇问题可查可问
- −安装和初始配置可能需要一定技术基础
- −功能完整性通常不如成熟商业产品
- −技术支持主要依赖开源社区，响应速度不稳定
AI Skill Hub 为第三方内容聚合平台，本页面信息基于公开数据整理，不对工具功能和质量作任何法律背书。
建议在沙箱或测试环境中充分验证后，再部署至生产环境，并做好必要的安全评估。
✅ Apache 2.0 — 宽松开源协议，可商用，需保留版权声明和 NOTICE 文件，含专利授权条款。
🔗 相关工具推荐
❓ 常见问题 FAQ
经综合评估，AI工具 在AI工具赛道中表现稳健，质量优秀。如果你已有明确的使用需求，可以直接上手体验；如果还在评估阶段，建议对比同类工具后再做决策。
| 原始名称 | ms-swift |
| 原始描述 | 开源AI工具：Use PEFT or Full-parameter to CPT/SFT/DPO/GRPO 600+ LLMs (Qwen3.6, DeepSeek-V4, 。⭐14.4k · Python |
| Topics | PEFTFull-parameterCPTSFTDPOGRPO |
| GitHub | https://github.com/modelscope/ms-swift |
| License | Apache-2.0 |
| 语言 | Python |
收录时间：2026-06-09 · 更新时间：2026-06-11 · License：Apache-2.0 · AI Skill Hub 不对第三方内容的准确性作法律背书。