What Sets Them Apart
ms-swift holds the crown for model count with support for over 600 LLMs and multimodal models, significantly exceeding LLaMA-Factory's 100+ model coverage. This breadth comes partly from deep coverage of Chinese model families including Qwen variants, ChatGLM versions, Baichuan, Yi, and DeepSeek architectures that may receive support in ms-swift before other frameworks.
ms-swift and LLaMA-Factory at a Glance
LLaMA-Factory has earned over 69,000 GitHub stars through its combination of accessibility and depth. The LLaMA Board web UI makes fine-tuning approachable without coding, while YAML configuration and CLI workflows serve advanced practitioners. Day-zero support for major model releases like Llama 4 and Qwen3 keeps it current with the fast-moving model landscape.
The hub integration story defines each framework's ecosystem positioning. ms-swift integrates natively with both ModelScope Hub and Hugging Face, enabling bidirectional model and dataset loading from either platform. LLaMA-Factory integrates primarily with Hugging Face, the dominant global model hub, with community contributions providing ModelScope access.
Training methodology coverage is comparable with both frameworks supporting SFT, DPO, PPO, ORPO, LoRA, QLoRA, and full fine-tuning. LLaMA-Factory has added OFT and OFTv2 orthogonal fine-tuning and multimodal training for audio models. ms-swift provides similar breadth with additional support for reward modeling and specific Chinese model training recipes.
Acceleration Backends and Training Speed
The acceleration backend story favors LLaMA-Factory which natively integrates Unsloth as an optional speed optimizer delivering 2-5x faster training on single GPUs. ms-swift uses standard DeepSpeed and FSDP for distributed training optimization without the custom kernel acceleration that Unsloth provides.
Community size and global reach heavily favor LLaMA-Factory with its larger contributor base, more extensive documentation in English, and broader adoption across international AI research and industry. ms-swift's community is substantial within the Chinese AI ecosystem but smaller globally.
The web UI experiences are similar in capability, both offering model selection, dataset management, hyperparameter configuration, and training monitoring through browser interfaces. LLaMA-Factory's LLaMA Board is more widely documented and featured in English-language tutorials.
Multimodal Fine-Tuning and Model Support
Multimodal fine-tuning for vision-language and audio models is supported by both frameworks with comparable coverage. Both handle the specific data preprocessing, model architecture modifications, and training patterns needed for multimodal model adaptation.
Deployment and inference integration differ in emphasis. LLaMA-Factory provides vLLM, SGLang, and OpenAI-compatible API serving out of the box. ms-swift integrates with ModelScope's inference ecosystem alongside standard Hugging Face and vLLM deployment patterns.
The Bottom Line
For teams primarily working within the Chinese AI ecosystem with models from ModelScope and needing the broadest model support, ms-swift provides native ecosystem integration. For teams wanting the most popular and globally supported fine-tuning framework with Unsloth acceleration, comprehensive documentation, and the largest community, LLaMA-Factory is the established standard.