Practical overview
When it fits
Use Ms Swift when you need a self-hosted framework for adapting a supported model and want both lightweight PEFT and full-parameter training options across supervised, preference or reinforcement-learning tasks.
Who it is for
- AI developers adapting supported ModelScope language models.
- Teams training multimodal models on text, image, video or audio data.
- Researchers experimenting with supervised, preference and reinforcement-learning methods.
Documented uses
- Fine-tune a model for a domain-specific assistant.
- Train a multimodal model for a specialized vision-language task.
- Apply continual pretraining to supported domain text.
- Build DPO or GRPO-based training workflows.
- Use documented inference, evaluation, quantization and deployment components around a trained model.
Requirements & limits
The project repository is publicly accessible, open source and licensed under Apache-2.0. It documents Linux, Windows, macOS, CLI, Docker, self-hosted and server environments.
- Model coverage: the repository describes support for more than 600 text-only and 400 multimodal models, but the selected model family still needs to be checked.
- Training method: PEFT reduces the scope of parameter updates compared with full-parameter training, while CPT, SFT, DPO and GRPO serve different adaptation goals.
- Modality: the framework documents text, image, video and audio training workflows, making dataset structure a key design consideration.
- Infrastructure: documented options include CUDA, ROCm, Apple MPS, CPU, distributed training and several memory-optimization techniques.
- Pipeline scope: Ms Swift covers training alongside documented inference, evaluation, quantization and deployment components, rather than only a single fine-tuning method.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Prepare a supported model for task-specific training
Set up the documented stages for adapting a supported ModelScope language or multimodal model.
Before you start
- A supported ModelScope model family.
- A prepared dataset for the intended training task.
- Python 3.12, PyTorch 2.0 or later, and ModelScope 1.23 or later, as shown in the repository badges.
- A supported execution environment such as Linux, Windows, macOS, CUDA, ROCm, Apple MPS or CPU.
Steps
Select a supported language or multimodal model family from ModelScope.
Prepare the dataset for the intended domain or task.
Choose PEFT or full-parameter training.
Select a documented task such as continual pretraining, supervised fine-tuning, DPO or GRPO.
Expected outcome
A configured training path for adapting the selected supported model to the chosen task.
Guide limitations
- The available project description does not provide model-specific hardware, configuration or execution details.
- Actual support depends on the selected model family and training method.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 2 October 2026
- Original website title
- modelscope/ms-swift · Official homepage title · retained in its source language