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name: ms-swift description: "Use ms-swift for LLM and multimodal training, inference, deployment, data/model customization, export, evaluation, RLHF, Ray, and Megatron workflows." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

ms-swift

Use this repo skill when a task mentions ms-swift, swift, ModelScope SWIFT, swift sft, swift infer, swift deploy, swift eval, swift export, swift rlhf, GRPO, Ray, or Megatron-SWIFT.

ms-swift is a Python framework for large-model and multimodal fine-tuning, inference, evaluation, quantization, deployment, RLHF/GRPO, Ray scheduling, and Megatron-style distributed training.

First Checks

• Public package: install with pip install ms-swift -U; from source use pip install -e ..
• Import check: python -c "import swift; print(swift.__version__)".
• CLI check: swift sft --help, swift infer --help, swift export --help, and swift rlhf --help should work in a base install.
• Optional extras are workflow-specific: evaluation needs EvalScope, advanced Megatron needs Megatron packages, and accelerated serving needs vLLM/SGLang/LMDeploy as appropriate.
• Use local model and dataset paths plus offline flags when users cannot download from ModelScope or HuggingFace.

Route by User Goal

• Train or fine-tune models: read sub-skills/training/SKILL.md for swift pt, swift sft, LoRA/QLoRA/full tuning, multimodal training, checkpoints, YAML/JSON configs, and training-time evaluation flags.
• Infer, serve, or call a local model: read sub-skills/inference-deployment/SKILL.md for swift infer, swift app, swift deploy, backend selection, OpenAI-compatible APIs, adapters, batch inference, logprobs, and multimodal requests.
• Prepare data or register models/templates: read sub-skills/data-model-customization/SKILL.md for dataset formats, --columns, --custom_dataset_info, --external_plugins, registry inspection, custom models, and templates.
• Export, quantize, push, or evaluate: read sub-skills/export-evaluation/SKILL.md for swift export, swift merge-lora, quantization, hub pushes, swift eval, EvalScope backends, and custom eval datasets.
• Use RLHF, GRPO, Ray, or Megatron: read sub-skills/advanced-rl-distributed/SKILL.md for swift rlhf, swift sample, swift rollout, reward plugins, Ray configs, Megatron-SWIFT, and distributed troubleshooting.

Shared References and Scripts

• Read references/package-overview.md for package purpose, CLI route map, optional extras, and cross-skill terminology.
• Read references/troubleshooting.md for install/import, optional dependency, backend, hub/offline, CLI/config, and hardware issues that cut across workflows.
• Read references/repo-provenance.md when checking whether this skill is stale against a newer ms-swift checkout.
• Run scripts/check_ms_swift_install.py --help for a safe installed-package and optional-dependency checker.
• Run scripts/inspect_swift_cli.py --help for a safe CLI route inventory and help-command planner.

Common Decision Points

• Use --model for full checkpoints or model IDs; use --adapters for LoRA adapter checkpoints.
• If a LoRA checkpoint must run on vLLM/SGLang/LMDeploy as a merged full model, route through export/merge guidance first; do not promise QLoRA merge support.
• If a task asks for dataset rows, schemas, media fields, or loss_scale, solve that in data/model customization before constructing training or RLHF commands.
• If a task asks for deployment performance, distinguish model support from package availability: base ms-swift may install while vLLM, SGLang, LMDeploy, EvalScope, Ray, or Megatron extras remain absent.
• If a task asks for GRPO or custom rewards, confirm the dataset fields and reward function signature before planning rollout placement or Ray/Megatron parallelism.

Runtime Safety

• The bundled scripts print diagnostics or command skeletons; they do not launch training, download models, start servers, or push to hubs unless their help explicitly says otherwise.
• Treat model downloads, hub pushes, long training, evaluation benchmarks, Ray/Megatron clusters, and accelerated serving as user-controlled side effects.
• Keep credentials in environment variables such as MODELSCOPE_TOKEN, HF_TOKEN, or a user-provided shell environment; do not write tokens into command builders or config examples.