SWIFT (Scalable lightWeight Infrastructure for Fine-Tuning)
📖 Table of Contents
☎ Groups
You can contact us and communicate with us by adding our group:
📝 Introduction
🍲 ms-swift is a large model and multimodal large model fine-tuning and deployment framework provided by the ModelScope community. It now supports training (pre-training, fine-tuning, human alignment), inference, evaluation, quantization, and deployment for 600+ text-only large models and 400+ multimodal large models. Large models include: Qwen3, Qwen3.5, InternLM3, GLM4.5, Mistral, DeepSeek-R1, Llama4, etc. Multimodal large models include: Qwen3-VL, Qwen3-Omni, Kimi-K3, Llava, InternVL3.5, MiniCPM-V-4, Ovis2.5, GLM4.5-V, DeepSeek-VL2, etc.
🍔 In addition, ms-swift integrates the latest training technologies, including Megatron parallelism techniques such as TP, PP, CP, EP to accelerate training, as well as numerous GRPO algorithm family reinforcement learning algorithms including: GRPO, DAPO, GSPO, SAPO, CISPO, RLOO, Reinforce++, etc. to enhance model intelligence. ms-swift supports a wide range of training tasks, including preference learning algorithms such as DPO, KTO, RM, CPO, SimPO, ORPO, as well as Embedding, Reranker, and sequence classification tasks. ms-swift provides full-pipeline support for large model training, including acceleration for inference, evaluation, and deployment modules using vLLM, SGLang, and LMDeploy, as well as model quantization using GPTQ, AWQ, BNB, and FP8 technologies.
Why Choose ms-swift?
- 🍎 Model Types: Supports 600+ text-only large models, 400+ multimodal large models, and All-to-All full modality models from training to deployment full pipeline, with Day-0 support for popular models.
- Dataset Types: Built-in 150+ datasets for pre-training, fine-tuning, human alignment, multimodal and various other tasks, with support for custom datasets. Users only need to prepare datasets for one-click training.
- Hardware Support: Supports NVIDIA GPUs (A100/H100/RTX series, etc., via CUDA), AMD GPUs (MI300 series, etc., via ROCm), domestic hardware such as Huawei Ascend NPU, MetaX GPU and Moore Threads GPU (via MUSA), as well as Apple MPS and CPU.
- Lightweight Training: Supports lightweight fine-tuning methods such as LoRA, QLoRA, DoRA, LoRA+, LLaMAPro, LongLoRA, LoRA-GA, ReFT, RS-LoRA, Adapter, LISA, etc.
- Quantized Training: Supports training on BNB, AWQ, GPTQ, AQLM, HQQ, EETQ quantized models, requiring only 9GB training resources for 7B models.
- Memory Optimization: GaLore, Q-Galore, UnSloth, Liger-Kernel, Flash-Attention 2/3, and Ulysses and Ring-Attention sequence parallelism techniques support, reducing memory consumption for long-text training.
- Distributed Training: Supports distributed data parallelism (DDP), device_map simple model parallelism, DeepSpeed ZeRO2 ZeRO3, FSDP/FSDP2, and Megatron distributed training technologies.
- 🍓 Multimodal Training: Supports multimodal packing technology to improve training speed by 100%+, supports mixed modality data training with text, images, video and audio, and supports independent control of vit/aligner/llm.
- Agent Training: Supports Agent templates, allowing one dataset to be used for training different models.
- 🍊 Training Tasks: Supports pre-training and instruction fine-tuning, as well as training tasks such as DPO, GKD, KTO, RM, CPO, SimPO, ORPO, and supports Embedding/Reranker and sequence classification tasks.
- 🥥 Megatron Parallelism: Provides TP/PP/SP/CP/ETP/EP/VPP parallel strategies to significantly boost MoE model training speed. Supports full-parameter and LoRA training methods for 300+ pure text large models and 100+ multimodal large models. Supports CPT/SFT/GRPO/DPO/KTO/RM training tasks.
- 🍉 Reinforcement Learning: Built-in rich GRPO family algorithms, including GRPO, DAPO, GSPO, SAPO, CISPO, CHORD, RLOO, Reinforce++, etc. Supports synchronous and asynchronous vLLM engine inference acceleration, with extensible reward functions, multi-turn inference Schedulers, and environments through plugins.
This is the beginning of the README. Read the rest at the original. Read the full README ↗
