ms-swift provides the most comprehensive model support of any fine-tuning framework, covering over 600 LLMs and multimodal models with particular strength in Chinese model families including Qwen, ChatGLM, Baichuan, Yi, and DeepSeek. The framework supports the full range of training methodologies from supervised fine-tuning through DPO preference alignment and RLHF, with parameter-efficient methods including LoRA, QLoRA, and adapter tuning.
The dual hub integration with both ModelScope and Hugging Face reflects ms-swift's position bridging the Chinese and international AI ecosystems. Models and datasets can be loaded from either hub interchangeably, and fine-tuned models export to both platforms. This cross-ecosystem compatibility is valuable for teams that work with models from both communities and need a unified training framework.
With over 13,500 GitHub stars, ms-swift provides both a web UI similar to LLaMA-Factory's LLaMA Board and a CLI with YAML-based configuration for automated training pipelines. The framework integrates with DeepSpeed for distributed training, supports multimodal fine-tuning for vision-language models, and includes built-in evaluation against standard benchmarks. The ModelScope team maintains active development with rapid support for new model architectures.