swift-ms-docs

name

swift-ms-docs

description

USE THIS SKILL WHEN working with ms-swift (ModelScope Swift): fine-tuning LLMs/VLMs, LoRA/QLoRA/DoRA training, GRPO reinforcement learning, RLHF (DPO/KTO/SimPO), pre-training, Megatron distributed training, inference/deployment with vLLM/SGLang/LmDeploy, quantization (AWQ/GPTQ/BNB/FP8), evaluation, custom datasets/models, or Web-UI. Triggers on: ms-swift, swift train, swift infer, swift deploy, swift export, LoRA fine-tuning ModelScope, GRPO training.

ms-swift Documentation

ms-swift is ModelScope's large model fine-tuning and deployment framework supporting 600+ LLMs and 400+ VLMs across the full training-to-deployment pipeline.

• GitHub | Docs

CRITICAL: grep references/ for detailed docs before answering.

Quick Start

pip install ms-swift

# Fine-tune with LoRA
swift sft \
  --model Qwen/Qwen2.5-7B-Instruct \
  --train_type lora \
  --dataset alpaca-zh#5000 \
  --num_train_epochs 1

# Inference
swift infer --model Qwen/Qwen2.5-7B-Instruct --stream true

# Deploy OpenAI-compatible API
swift deploy --model Qwen/Qwen2.5-7B-Instruct

# GRPO reinforcement learning
swift rlhf --rlhf_type grpo --model Qwen/Qwen2.5-7B-Instruct --dataset math-shepherd-mix

Key Capabilities

Feature	Description
LoRA/QLoRA/DoRA	Lightweight fine-tuning (9GB for 7B model)
GRPO family	GRPO, DAPO, GSPO, SAPO, CISPO, RLOO, Reinforce++
RLHF	DPO, KTO, RM, CPO, SimPO, ORPO
Megatron	TP/PP/CP/EP parallelism for MoE models
Inference	vLLM, SGLang, LmDeploy acceleration
Quantization	AWQ, GPTQ, FP8, BNB export
Evaluation	EvalScope backend, 100+ datasets
Multimodal	VLM training with image/video/audio

Key Topics

Getting Started

• references/GetStarted/Quick-start.md — Quick start
• references/GetStarted/SWIFT-installation.md — Installation
• references/GetStarted/Web-UI.md — Web UI guide

Training

• references/Instruction/Pre-training-and-Fine-tuning.md — SFT/CPT guide
• references/Instruction/Use-tuners.md — LoRA, QLoRA, DoRA, Adapter, etc.
• references/Instruction/RLHF.md — DPO, KTO, SimPO, ORPO
• references/Instruction/Reinforced-Fine-tuning.md — GRPO overview
• references/Instruction/GRPO/GetStarted/GRPO.md — GRPO quick start
• references/Instruction/GRPO/DeveloperGuide/ — Reward functions, multi-turn, gym env
• references/Instruction/GRPO/AdvancedResearch/ — DAPO, GSPO, SAPO, CISPO, RLOO

Inference & Deployment

• references/Instruction/Inference-and-deployment.md — Inference + deploy
• references/Instruction/Export-and-push.md — Quantization export, model push

Megatron Distributed

• references/Megatron-SWIFT/Quick-start.md — Megatron quick start
• references/Megatron-SWIFT/Command-line-parameters.md — All parameters
• references/Megatron-SWIFT/GRPO.md — Megatron GRPO
• references/Megatron-SWIFT/LoRA-Training.md — Megatron LoRA

Customization

• references/Customization/Custom-dataset.md — Custom datasets
• references/Customization/Custom-model.md — Custom models
• references/Customization/Architecture.md — Architecture overview

Best Practices

• references/BestPractices/ — Qwen3, GRPO, Embedding, Reranker, NPU, VLM
• references/Instruction/Supported-models-and-datasets.md — 600+ models list
• references/Instruction/Command-line-parameters.md — All CLI parameters
• references/Instruction/Frequently-asked-questions.md — FAQ

References

• references/ — 59 English doc files covering full training-to-deployment pipeline
• references/GetStarted/ — Installation and quick start
• references/Instruction/ — Training, inference, GRPO, RLHF
• references/Megatron-SWIFT/ — Megatron distributed training
• references/BestPractices/ — Model-specific best practices
• references/Customization/ — Custom models and datasets