# ms-swift **Repository Path**: superpig2021/ms-swift ## Basic Information - **Project Name**: ms-swift - **Description**: Use PEFT or Full-parameter to finetune 450+ LLMs (Qwen2.5, InternLM3, GLM4, Llama3.3, Mistral, Yi1.5, Baichuan2, DeepSeek-R1, ...) and 150+ MLLMs (Qwen2.5-VL, Qwen2-Audio, Llama3.2-Vision, Llava, InternVL2.5, MiniCPM-V-2.6, GLM4v, Xcomposer2.5, Yi-VL, DeepSeek-VL2, Phi3.5-Vision, GOT-OCR2, ...). - **Primary Language**: Unknown - **License**: Apache-2.0 - **Default Branch**: main - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2025-03-13 - **Last Updated**: 2025-03-18 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # SWIFT (Scalable lightWeight Infrastructure for Fine-Tuning)

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## 📖 Table of Contents - [Groups](#-Groups) - [Introduction](#-introduction) - [News](#-news) - [Installation](#%EF%B8%8F-installation) - [Quick Start](#-quick-Start) - [Usage](#-Usage) - [License](#-License) - [Citation](#-citation) ## ☎ Groups You can contact us and communicate with us by adding our group: [Discord Group](https://discord.com/invite/D27yfEFVz5) | WeChat Group :-------------------------:|:-------------------------:

## 📝 Introduction 🍲 ms-swift is an official framework provided by the ModelScope community for fine-tuning and deploying large language models and multi-modal large models. It currently supports the training (pre-training, fine-tuning, human alignment), inference, evaluation, quantization, and deployment of 450+ large models and 150+ multi-modal large models. These large language models (LLMs) include models such as Qwen2.5, InternLM3, GLM4, Llama3.3, Mistral, DeepSeek-R1, Yi1.5, TeleChat2, Baichuan2, and Gemma2. The multi-modal LLMs include models such as Qwen2.5-VL, Qwen2-Audio, Llama3.2-Vision, Llava, InternVL2.5, MiniCPM-V-2.6, GLM4v, Xcomposer2.5, Yi-VL, DeepSeek-VL2, Phi3.5-Vision, and GOT-OCR2. 🍔 Additionally, ms-swift incorporates the latest training technologies, including lightweight techniques such as LoRA, QLoRA, Llama-Pro, LongLoRA, GaLore, Q-GaLore, LoRA+, LISA, DoRA, FourierFt, ReFT, UnSloth, and Liger, as well as human alignment training methods like DPO, GRPO, RM, PPO, KTO, CPO, SimPO, and ORPO. ms-swift supports acceleration of inference, evaluation, and deployment modules using vLLM and LMDeploy, and it supports model quantization with technologies like GPTQ, AWQ, and BNB. Furthermore, ms-swift offers a Gradio-based Web UI and a wealth of best practices. **Why choose ms-swift?** - 🍎 **Model Types**: Supports 450+ pure text large models, **150+ multi-modal large models**, as well as All-to-All multi-modal models, sequence classification models, and embedding models, **covering the entire process from training to deployment**. - **Dataset Types**: Comes with 150+ pre-training, fine-tuning, human alignment, multi-modal datasets, and supports custom datasets. - **Hardware Support**: Compatible with CPU, RTX series, T4/V100, A10/A100/H100, Ascend NPU, MPS, etc. - 🍊 **Lightweight Training**: Supports lightweight fine-tuning methods like LoRA, QLoRA, DoRA, LoRA+, ReFT, RS-LoRA, LLaMAPro, Adapter, GaLore, Q-Galore, LISA, UnSloth, Liger-Kernel. - **Distributed Training**: Supports distributed data parallel (DDP), device_map simple model parallelism, DeepSpeed ZeRO2/ZeRO3, FSDP, and other distributed training techniques. - **Quantization Training**: Supports training quantized models like BNB, AWQ, GPTQ, AQLM, HQQ, EETQ. - **RLHF Training**: Supports human alignment training methods such as DPO, GRPO, RM, PPO, KTO, CPO, SimPO, ORPO for both pure text and multi-modal large models. - 🍓 **Multi-Modal Training**: Supports training on different modalities like images, videos, and audio, for tasks like VQA, captioning, OCR, and grounding. - **Interface Training**: Provides capabilities for training, inference, evaluation, quantization through an interface, completing the whole large model pipeline. - **Plugin and Extension**: Supports custom model and dataset extensions, as well as customization of components like loss, metric, trainer, loss-scale, callback, optimizer. - 🍉 **Toolbox Capabilities**: Offers not only training support for large models and multi-modal large models but also covers the entire process of inference, evaluation, quantization, and deployment. - **Inference Acceleration**: Supports inference acceleration engines like PyTorch, vLLM, LmDeploy, and provides OpenAI API for accelerating inference, deployment, and evaluation modules. - **Model Evaluation**: Uses EvalScope as the evaluation backend and supports evaluation on 100+ datasets for both pure text and multi-modal models. - **Model Quantization**: Supports AWQ, GPTQ, and BNB quantized exports, with models that can use vLLM/LmDeploy for inference acceleration and continue training. ## 🎉 News - 🎁 2025.03.13: We provide a script of GRPO to train a 72B model with only 4 GPUs(4*80G), please check [here](examples/train/grpo/train_72b_4gpu.sh) - 🎁 2025.03.05: We support the hybrid mode of GRPO(rollout and actor on the same GPU, rollout sleep when actor training), meanwhile tensor parallel for GRPO, check[training script here](examples/train/grpo/multi_gpu_mp_colocate.sh) - 🎁 2025.02.21: We test the speed performance of GRPO，and with some tricks to [speed up to 300%](examples/train/grpo/full_lmdeploy.sh). WanDB charts can be found [here](https://wandb.ai/tastelikefeet/grpo_perf_test?nw=nwuseryuzezyz) - 🎁 2025.02.21: Support distill from LLM API，Please check[this example](examples/sampler/distill/distill.sh) - 🎁 2025.02.17: Support SwanLab, just add [a few of arguments](docs/source_en/Instruction/Command-line-parameters.md#swanlab) you can use swanlab to analysis your training results - 🎁 2025.02.16: Support LMDeploy in GRPO, use `--use_lmdeploy true`. Please check [this script](examples/train/grpo/full_lmdeploy.sh) - 🔥 2025.02.12: Support for GRPO(Group Relative Policy Optimization) algorithm for llm and mllm, document can be found in [here](docs/source_en/Instruction/GRPO.md) - 🎁 2025.02.10: SWIFT support the fine-tuning of embedding models，please check the [training script](examples/train/embedding/train.sh)。 - 🎁 2025.01.23: SWIFT support the `sample` command, this is a very important feature for complex CoT and RFT. Meanwhile, we support an [Reinforced Fine-tuning script](docs/source_en/Instruction/Reinforced_Fine_tuning.md). - 🎁 2024.12.04: **SWIFT3.0** major version update. Please check the [Release Notes and Changes](https://swift.readthedocs.io/en/latest/Instruction/ReleaseNote3.0.html). - 🎉 2024.08.12: The SWIFT paper has been published on arXiv, and you can read it [here](https://arxiv.org/abs/2408.05517). - 🔥 2024.08.05: Support for using [evalscope](https://github.com/modelscope/evalscope/) as a backend for evaluating large models and multimodal models. - 🔥 2024.07.29: Support for using [vllm](https://github.com/vllm-project/vllm) and [lmdeploy](https://github.com/InternLM/lmdeploy) to accelerate inference for large models and multimodal models. When performing infer/deploy/eval, you can specify `--infer_backend vllm/lmdeploy`. - 🔥 2024.07.24: Support for human preference alignment training for multimodal large models, including DPO/ORPO/SimPO/CPO/KTO/RM/PPO. - 🔥 2024.02.01: Support for Agent training! The training algorithm is derived from [this paper](https://arxiv.org/pdf/2309.00986.pdf). ## 🛠️ Installation To install using pip: ```shell pip install ms-swift -U ``` To install from source: ```shell # pip install git+https://github.com/modelscope/ms-swift.git git clone https://github.com/modelscope/ms-swift.git cd ms-swift pip install -e . ``` Running Environment: | | Range | Recommended | Notes | | ------------ | -------------------- | ----------- | ----------------------------------------- | | python | >=3.9 | 3.10 | | | cuda | | cuda12 | No need to install if using CPU, NPU, MPS | | torch | >=2.0 | | | | transformers | >=4.33 | 4.49 | | | modelscope | >=1.19 | | | | peft | >=0.11,<0.15 | || | trl | >=0.13,<0.17 | 0.15 |RLHF| | deepspeed | >=0.14 | 0.14.5 | Training | | vllm | >=0.5.1 | 0.7.3 | Inference/Deployment/Evaluation | | lmdeploy | >=0.5 | 0.7.1 | Inference/Deployment/Evaluation | | evalscope | >=0.11 | | Evaluation | For more optional dependencies, you can refer to [here](https://github.com/modelscope/ms-swift/blob/main/requirements/install_all.sh). ## 🚀 Quick Start 10 minutes of self-cognition fine-tuning of Qwen2.5-7B-Instruct on a single 3090 GPU: ### Command Line Interface ```shell # 22GB CUDA_VISIBLE_DEVICES=0 \ swift sft \ --model Qwen/Qwen2.5-7B-Instruct \ --train_type lora \ --dataset 'AI-ModelScope/alpaca-gpt4-data-zh#500' \ 'AI-ModelScope/alpaca-gpt4-data-en#500' \ 'swift/self-cognition#500' \ --torch_dtype bfloat16 \ --num_train_epochs 1 \ --per_device_train_batch_size 1 \ --per_device_eval_batch_size 1 \ --learning_rate 1e-4 \ --lora_rank 8 \ --lora_alpha 32 \ --target_modules all-linear \ --gradient_accumulation_steps 16 \ --eval_steps 50 \ --save_steps 50 \ --save_total_limit 5 \ --logging_steps 5 \ --max_length 2048 \ --output_dir output \ --system 'You are a helpful assistant.' \ --warmup_ratio 0.05 \ --dataloader_num_workers 4 \ --model_author swift \ --model_name swift-robot ``` Tips: - If you want to train with a custom dataset, you can refer to [this guide](https://swift.readthedocs.io/en/latest/Customization/Custom-dataset.html) to organize your dataset format and specify `--dataset `. - The `--model_author` and `--model_name` parameters are only effective when the dataset includes `swift/self-cognition`. - To train with a different model, simply modify `--model `. - By default, ModelScope is used for downloading models and datasets. If you want to use HuggingFace, simply specify `--use_hf true`. After training is complete, use the following command to infer with the trained weights: - Here, `--adapters` should be replaced with the last checkpoint folder generated during training. Since the adapters folder contains the training parameter file `args.json`, there is no need to specify `--model`, `--system` separately; Swift will automatically read these parameters. To disable this behavior, you can set `--load_args false`. ```shell # Using an interactive command line for inference. CUDA_VISIBLE_DEVICES=0 \ swift infer \ --adapters output/vx-xxx/checkpoint-xxx \ --stream true \ --temperature 0 \ --max_new_tokens 2048 # merge-lora and use vLLM for inference acceleration CUDA_VISIBLE_DEVICES=0 \ swift infer \ --adapters output/vx-xxx/checkpoint-xxx \ --stream true \ --merge_lora true \ --infer_backend vllm \ --max_model_len 8192 \ --temperature 0 \ --max_new_tokens 2048 ``` Finally, use the following command to push the model to ModelScope: ```shell CUDA_VISIBLE_DEVICES=0 \ swift export \ --adapters output/vx-xxx/checkpoint-xxx \ --push_to_hub true \ --hub_model_id '' \ --hub_token '' \ --use_hf false ``` ### Web-UI The Web-UI is a **zero-threshold** training and deployment interface solution based on Gradio interface technology. For more details, you can check [here](https://swift.readthedocs.io/en/latest/GetStarted/Web-UI.html). ```shell SWIFT_UI_LANG=en swift web-ui ``` ![image.png](./docs/resources/web-ui-en.jpg) ### Using Python ms-swift also supports training and inference using Python. Below is pseudocode for training and inference. For more details, you can refer to [here](https://github.com/modelscope/ms-swift/blob/main/examples/notebook/qwen2_5-self-cognition/self-cognition-sft.ipynb). Training: ```python # Retrieve the model and template, and add a trainable LoRA module model, tokenizer = get_model_tokenizer(model_id_or_path, ...) template = get_template(model.model_meta.template, tokenizer, ...) model = Swift.prepare_model(model, lora_config) # Download and load the dataset, and encode the text into tokens train_dataset, val_dataset = load_dataset(dataset_id_or_path, ...) train_dataset = EncodePreprocessor(template=template)(train_dataset, num_proc=num_proc) val_dataset = EncodePreprocessor(template=template)(val_dataset, num_proc=num_proc) # Train the model trainer = Seq2SeqTrainer( model=model, args=training_args, data_collator=template.data_collator, train_dataset=train_dataset, eval_dataset=val_dataset, template=template, ) trainer.train() ``` Inference: ```python # Perform inference using the native PyTorch engine engine = PtEngine(model_id_or_path, adapters=[lora_checkpoint]) infer_request = InferRequest(messages=[{'role': 'user', 'content': 'who are you?'}]) request_config = RequestConfig(max_tokens=max_new_tokens, temperature=temperature) resp_list = engine.infer([infer_request], request_config) print(f'response: {resp_list[0].choices[0].message.content}') ``` ## ✨ Usage Here is a minimal example of training to deployment using ms-swift. For more details, you can check the [examples](https://github.com/modelscope/ms-swift/tree/main/examples). - If you want to use other models or datasets (including multimodal models and datasets), you only need to modify `--model` to specify the corresponding model's ID or path, and modify `--dataset` to specify the corresponding dataset's ID or path. - By default, ModelScope is used for downloading models and datasets. If you want to use HuggingFace, simply specify `--use_hf true`. | Useful Links | | ------ | | [🔥Command Line Parameters](https://swift.readthedocs.io/en/latest/Instruction/Command-line-parameters.html) | | [Supported Models and Datasets](https://swift.readthedocs.io/en/latest/Instruction/Supported-models-and-datasets.html) | | [Custom Models](https://swift.readthedocs.io/en/latest/Customization/Custom-model.html), [🔥Custom Datasets](https://swift.readthedocs.io/en/latest/Customization/Custom-dataset.html) | | [LLM Tutorial](https://github.com/modelscope/modelscope-classroom/tree/main/LLM-tutorial) | ### Training Supported Training Methods: | Method | Full-Parameter | LoRA | QLoRA | Deepspeed | Multi-Node | Multi-Modal | |------------------------------------|--------------------------------------------------------------|--------------------------------------------------------------|--------------------------------------------------------------|--------------------------------------------------------------|--------------------------------------------------------------|--------------------------------------------------------------| | Pre-training | [✅](https://github.com/modelscope/ms-swift/blob/main/examples/train/pretrain/train.sh) | ✅ | ✅ | ✅ | ✅ | ✅ | | Instruction Supervised Fine-tuning | [✅](https://github.com/modelscope/ms-swift/blob/main/examples/train/full/train.sh) | [✅](https://github.com/modelscope/ms-swift/blob/main/examples/train/lora_sft.sh) | [✅](https://github.com/modelscope/ms-swift/tree/main/examples/train/qlora) | [✅](https://github.com/modelscope/ms-swift/tree/main/examples/train/multi-gpu/deepspeed) | [✅](https://github.com/modelscope/ms-swift/tree/main/examples/train/multi-node) | [✅](https://github.com/modelscope/ms-swift/tree/main/examples/train/multimodal) | | DPO Training | ✅ | [✅](https://github.com/modelscope/ms-swift/blob/main/examples/train/rlhf/dpo.sh) | ✅ | [✅](https://github.com/modelscope/ms-swift/blob/main/examples/train/rlhf/dpo.sh) | ✅ | [✅](https://github.com/modelscope/ms-swift/blob/main/examples/train/multimodal/rlhf/dpo.sh) | | GRPO Training | [✅]((https://github.com/modelscope/ms-swift/blob/main/examples/train/grpo/grpo_zero2.sh)) | ✅ | ✅ | ✅ | [✅](https://github.com/modelscope/ms-swift/blob/main/examples/train/grpo/multi_node) | ✅ | | Reward Model Training | ✅ | [✅](https://github.com/modelscope/ms-swift/blob/main/examples/train/rlhf/rm.sh) | ✅ | [✅](https://github.com/modelscope/ms-swift/blob/main/examples/train/rlhf/rm.sh) | ✅ | ✅ | | PPO Training | ✅ | [✅](https://github.com/modelscope/ms-swift/blob/main/examples/train/rlhf/ppo.sh) | ✅ | [✅](https://github.com/modelscope/ms-swift/blob/main/examples/train/rlhf/ppo.sh) | ✅ | ❌ | | KTO Training | ✅ | [✅](https://github.com/modelscope/ms-swift/blob/main/examples/train/rlhf/kto.sh) | ✅ | [✅](https://github.com/modelscope/ms-swift/blob/main/examples/train/rlhf/kto.sh) | ✅ | [✅](https://github.com/modelscope/ms-swift/blob/main/examples/train/multimodal/rlhf/kto.sh) | | CPO Training | ✅ | [✅](https://github.com/modelscope/ms-swift/blob/main/examples/train/rlhf/cpo.sh) | ✅ | [✅](https://github.com/modelscope/ms-swift/blob/main/examples/train/rlhf/cpo.sh) | ✅ | ✅ | | SimPO Training | ✅ | [✅](https://github.com/modelscope/ms-swift/blob/main/examples/train/rlhf/simpo.sh) | ✅ | [✅](https://github.com/modelscope/ms-swift/blob/main/examples/train/rlhf/simpo.sh) | ✅ | ✅ | | ORPO Training | ✅ | [✅](https://github.com/modelscope/ms-swift/blob/main/examples/train/rlhf/orpo.sh) | ✅ | [✅](https://github.com/modelscope/ms-swift/blob/main/examples/train/rlhf/orpo.sh) | ✅ | ✅ | | Classification Model Training | ✅ | [✅](https://github.com/modelscope/ms-swift/blob/main/examples/train/seq_cls/qwen2_5/sft.sh) | ✅ | ✅ | ✅ | [✅](https://github.com/modelscope/ms-swift/blob/main/examples/train/seq_cls/qwen2_vl/sft.sh) | | Embedding Model Training | ✅ | [✅](https://github.com/modelscope/ms-swift/blob/main/examples/train/embedding/train.sh) | ✅ | ✅ | ✅ | ❌ | Pre-training: ```shell # 8*A100 NPROC_PER_NODE=8 \ CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \ swift pt \ --model Qwen/Qwen2.5-7B \ --dataset swift/chinese-c4 \ --streaming true \ --train_type full \ --deepspeed zero2 \ --output_dir output \ --max_steps 100000 \ ... ``` Fine-tuning: ```shell CUDA_VISIBLE_DEVICES=0 swift sft \ --model Qwen/Qwen2.5-7B-Instruct \ --dataset AI-ModelScope/alpaca-gpt4-data-en \ --train_type lora \ --output_dir output \ ... ``` RLHF: ```shell CUDA_VISIBLE_DEVICES=0 swift rlhf \ --rlhf_type dpo \ --model Qwen/Qwen2.5-7B-Instruct \ --dataset hjh0119/shareAI-Llama3-DPO-zh-en-emoji \ --train_type lora \ --output_dir output \ ... ``` ### Inference ```shell CUDA_VISIBLE_DEVICES=0 swift infer \ --model Qwen/Qwen2.5-7B-Instruct \ --stream true \ --infer_backend pt \ --max_new_tokens 2048 # LoRA CUDA_VISIBLE_DEVICES=0 swift infer \ --model Qwen/Qwen2.5-7B-Instruct \ --adapters swift/test_lora \ --stream true \ --infer_backend pt \ --temperature 0 \ --max_new_tokens 2048 ``` ### Interface Inference ```shell CUDA_VISIBLE_DEVICES=0 swift app \ --model Qwen/Qwen2.5-7B-Instruct \ --stream true \ --infer_backend pt \ --max_new_tokens 2048 ``` ### Deployment ```shell CUDA_VISIBLE_DEVICES=0 swift deploy \ --model Qwen/Qwen2.5-7B-Instruct \ --infer_backend vllm ``` ### Sampling ```shell CUDA_VISIBLE_DEVICES=0 swift sample \ --model LLM-Research/Meta-Llama-3.1-8B-Instruct \ --sampler_engine pt \ --num_return_sequences 5 \ --dataset AI-ModelScope/alpaca-gpt4-data-zh#5 ``` ### Evaluation ```shell CUDA_VISIBLE_DEVICES=0 swift eval \ --model Qwen/Qwen2.5-7B-Instruct \ --infer_backend lmdeploy \ --eval_backend OpenCompass \ --eval_dataset ARC_c ``` ### Quantization ```shell CUDA_VISIBLE_DEVICES=0 swift export \ --model Qwen/Qwen2.5-7B-Instruct \ --quant_bits 4 --quant_method awq \ --dataset AI-ModelScope/alpaca-gpt4-data-zh \ --output_dir Qwen2.5-7B-Instruct-AWQ ``` ### Push Model ```shell swift export \ --model \ --push_to_hub true \ --hub_model_id '' \ --hub_token '' ``` ## 🏛 License This framework is licensed under the [Apache License (Version 2.0)](https://github.com/modelscope/modelscope/blob/master/LICENSE). For models and datasets, please refer to the original resource page and follow the corresponding License. ## 📎 Citation ```bibtex @misc{zhao2024swiftascalablelightweightinfrastructure, title={SWIFT:A Scalable lightWeight Infrastructure for Fine-Tuning}, author={Yuze Zhao and Jintao Huang and Jinghan Hu and Xingjun Wang and Yunlin Mao and Daoze Zhang and Zeyinzi Jiang and Zhikai Wu and Baole Ai and Ang Wang and Wenmeng Zhou and Yingda Chen}, year={2024}, eprint={2408.05517}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2408.05517}, } ``` ## Star History [![Star History Chart](https://api.star-history.com/svg?repos=modelscope/swift&type=Date)](https://star-history.com/#modelscope/ms-swift&Date)