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MS-SWIFT: 高效LLM/MLLM模型微调工具箱 (Supports 700+ Models)
A comprehensive toolkit for efficient fine-tuning of over 500 Large Language Models (LLMs) and 200+ Multimodal Large Language Models (MLLMs) using various methods like PEFT, Full-parameter, SFT, DPO, and more. Supports state-of-the-art models including Qwen3, Llama4, InternLM3, GLM4, Mistral, Yi1.5, DeepSeek-R1, Qwen2.5-VL, Ovis2, InternVL3, Llava, MiniCPM-V-2.6, GLM4v, DeepSeek-VL2, and others. Ideal for researchers and developers needing flexible and scalable model customization.
Python
Added on 2025年5月10日7,449
Stars635
ForksPython
LanguageProject Introduction
Summary
This project is a powerful and flexible open-source toolkit specifically designed for efficiently fine-tuning a wide range of large language models (LLMs) and multimodal large language models (MLLMs). It supports numerous models and fine-tuning techniques, enabling users to customize cutting-edge models for specific tasks and domains.
Problem Solved
Fine-tuning large and multimodal models is complex due to varying model architectures, diverse fine-tuning methods, and computational requirements. This project provides a unified, efficient, and easy-to-use framework that abstracts away much of this complexity, allowing users to quickly experiment with and deploy customized models.
Core Features
Extensive Model Support
Supports a vast collection of over 700+ state-of-the-art LLMs and MLLMs, providing a unified interface for fine-tuning across diverse model architectures.
Diverse Fine-tuning Methods
Offers flexibility with multiple fine-tuning strategies including PEFT methods (LoRA, QLoRA, etc.) and full-parameter tuning, alongside various optimization objectives (SFT, CPT, DPO, GRPO).
Efficient & Scalable Training
Designed for efficiency and scalability, enabling fine-tuning on various hardware setups, including distributed training configurations.
Streamlined Workflow
Simplifies the fine-tuning workflow from data preparation to model deployment, making complex tasks accessible to users.
Tech Stack
Python
PyTorch
Hugging Face Transformers
Accelerate
PEFT
DeepSpeed
使用场景
The framework can be applied in various scenarios requiring the adaptation of large pre-trained models to specific tasks or domains. Key use cases include:
场景一：特定领域模型微调
Details
Adapt a general-purpose LLM (e.g., Llama4, Qwen3) or MLLM (e.g., Llava, InternVL3) using PEFT (e.g., LoRA) on a domain-specific dataset (e.g., medical texts, legal documents, product catalogs) to improve performance on relevant tasks like question answering, entity recognition, or image captioning.
User Value
Achieve high accuracy on specialized tasks without requiring massive computational resources for full re-training or fine-tuning.
场景二：模型行为对齐与安全微调
Details
Fine-tune a base LLM using DPO or GRPO on preference datasets to align its outputs better with human values, safety guidelines, or specific stylistic requirements, creating a more helpful and harmless assistant.
User Value
Develop models that are safer, more helpful, and better controlled, reducing the risk of undesirable outputs.
场景三：多模态任务适应
Details
Utilize the full-parameter fine-tuning capabilities or efficient PEFT methods to adapt models like Qwen2.5-VL or GLM4v for specific multimodal tasks, such as visual reasoning on domain-specific images or interpreting complex diagrams.
User Value
Extend the capabilities of large multimodal models to solve niche problems involving image, text, and other data types relevant to a particular industry or application.
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