開発ツール
peft
Fine-tune huge models on small hardware with LoRA and other efficient methods
★ 21.8k スターApache-2.0
ガイドを見る →ms-swiftは、modelscopeが公開しているオープンソースの開発ツールプロジェクトです（GitHub: modelscope/ms-swift）。GitHubスターは15,796、フォークは1,726で、Apache-2.0ライセンスの下で公開されています。GitHubでの最終更新は2026年10月8日です。
Fine-tune and deploy 600+ LLMs and 300+ multimodal models with PEFT or full-parameter training.
Choose from 600+ LLMs or 300+ multimodal models, then decide between lightweight PEFT (such as LoRA) and full-parameter training.
Install the package from PyPI with pip or uv, or clone the repo and install from source for the latest main branch.
Use one of the 150+ built-in datasets or format your own for pre-training, SFT, alignment, embedding or reranker tasks.
Launch training with the CLI or scripts, evaluate the result, then quantize and serve it with vLLM, SGLang or LMDeploy.
Training-to-deployment pipelines for 600+ text LLMs and hundreds of multimodal models, with day-0 support for popular new releases.
CPT, SFT, DPO, KTO, RM, CPO, SimPO and ORPO, plus a GRPO-family reinforcement learning set including DAPO, GSPO and Reinforce++.
LoRA, QLoRA, DoRA, LoRA+, LLaMAPro, LongLoRA, Adapter, ReFT and more, alongside full-parameter training.
Tensor, pipeline, context and expert parallelism techniques from Megatron to accelerate large training runs.
NVIDIA GPUs via CUDA, AMD GPUs via ROCm, Ascend NPU and other domestic accelerators, plus Apple MPS and CPU.
Train on BNB, AWQ, GPTQ, AQLM, HQQ and EETQ quantized models, and accelerate inference and deployment with vLLM, SGLang and LMDeploy.
Clone ms-swift if installing from source; PyPI installs do not need the clone.
git clone https://github.com/modelscope/ms-swift.git
cd ms-swiftInstall the latest release from PyPI and upgrade to the newest version.
pip install ms-swift -UFor the main branch, install the cloned project in editable mode.
pip install -e .It is provided by the ModelScope community (modelscope) and open-source contributors, and was presented at AAAI 2025.
Yes. It is Apache-2.0 licensed; the models and datasets you use with it carry their own licenses.
No. Training and inference run on your own hardware; you only need model weights and data, which you download from hubs such as ModelScope or Hugging Face.
All three fine-tune open LLMs; ms-swift stands out for its very wide model and multimodal coverage, Megatron parallelism and the breadth of built-in alignment algorithms - the best fit depends on your model and hardware.
Run pip install ms-swift -U, pick a built-in dataset and a supported base model, and follow the quick-start training example in the docs.
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