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ms-swift: Use PEFT or Full-parameter to finetune 250+ LLMs or 35+ MLLMs. (Qwen, GLM4, Internlm2, Yi, Llama3, Llava, Deepseek, ...)

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SWIFT (Scalable lightWeight Infrastructure for Fine-Tuning)



ModelScope Community Website
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modelscope%2Fswift | Trendshift

📖 Table of Contents

📝 Introduction

SWIFT supports training, inference, evaluation and deployment of nearly 200 LLMs and MLLMs (multimodal large models). Developers can directly apply our framework to their own research and production environments to realize the complete workflow from model training and evaluation to application. In addition to supporting the lightweight training solutions provided by PEFT, we also provide a complete Adapters library to support the latest training techniques such as NEFTune, LoRA+, LLaMA-PRO, etc. This adapter library can be used directly in your own custom workflow without our training scripts.

To facilitate use by users unfamiliar with deep learning, we provide a Gradio web-ui for controlling training and inference, as well as accompanying deep learning courses and best practices for beginners.

Additionally, we are expanding capabilities for other modalities. Currently, we support full-parameter training and LoRA training for AnimateDiff.

SWIFT has rich documentations for users, please check here.

SWIFT web-ui is available both on Huggingface space and ModelScope studio, please feel free to try!

🎉 News

  • 🔥2024.06.05: Support for glm4 series LLM and glm4v-9b-chat MLLM. You can refer to glm4v best practice.
  • 🔥2024.06.01: Supoprts SimPO training! See document to start training!
  • 🔥2024.06.01: Support for deploying large multimodal models, please refer to the Multimodal Deployment Documentation for more information.
  • 2024.05.31: Supports Mini-Internvl model, Use model_type mini-internvl-chat-2b-v1_5 and mini-internvl-chat-4b-v1_5to train.
  • 2024.05.24: Supports Phi3-vision model, Use model_type phi3-vision-128k-instruct to train.
  • 2024.05.22: Supports DeepSeek-V2-Lite series models, model_type are deepseek-v2-lite and deepseek-v2-lite-chat
  • 2024.05.22: Supports TeleChat-12B-v2 model with quantized version, model_type are telechat-12b-v2 and telechat-12b-v2-gptq-int4
  • 🔥2024.05.21: Inference and fine-tuning support for MiniCPM-Llama3-V-2_5 are now available. For more details, please refer to minicpm-v-2.5 Best Practice.
  • 🔥2024.05.20: Support for inferencing and fine-tuning cogvlm2-llama3-chinese-chat-19B, cogvlm2-llama3-chat-19B. you can refer to cogvlm2 Best Practice.
  • 🔥2024.05.17: Support peft=0.11.0. Meanwhile support 3 new tuners: BOFT, Vera and Pissa. use --sft_type boft/vera to use BOFT or Vera, use --init_lora_weights pissa with --sft_type lora to use Pissa.
  • 2024.05.16: Supports Llava-Next (Stronger) series models. For best practice, you can refer to here.
  • 🔥2024.05.13: Support Yi-1.5 series models，use --model_type yi-1_5-9b-chat to begin!
  • 2024.05.11: Support for qlora training and quantized inference using hqq and eetq. For more information, see the LLM Quantization Documentation.
  • 2024.05.10: Support split a sequence to multiple GPUs to reduce memory usage. Use this feature by pip install .[seq_parallel], then add --sequence_parallel_size n to your DDP script to begin!
  • 2024.05.08: Support DeepSeek-V2-Chat model, you can refer to this script.Support InternVL-Chat-V1.5-Int8 model, for best practice, you can refer to here.
  • 🔥2024.05.07: Supoprts ORPO training! See document to start training!
  • 2024.05.07: Supports Llava-Llama3 model from xtuner，model_type is llava-llama-3-8b-v1_1.
  • 2024.04.29: Supports inference and fine-tuning of InternVL-Chat-V1.5 model. For best practice, you can refer to here.
  • 🔥2024.04.26: Support LISA and unsloth training! Specify --lisa_activated_layers=2 to use LISA(to reduce the memory cost to 30 percent!), specify --tuner_backend unsloth to use unsloth to train a huge model(full or lora) with lesser memory(30 percent or lesser) and faster speed(5x)!
  • 🔥2024.04.26: Support the fine-tuning and inference of Qwen1.5-110B and Qwen1.5-110B-Chat model, use this script to start training!
More
  • 2024.04.24: Support for inference and fine-tuning of Phi3 series models. Including: phi3-4b-4k-instruct, phi3-4b-128k-instruct.
  • 2024.04.22: Support for inference, fine-tuning, and deployment of chinese-llama-alpaca-2 series models. This includes：chinese-llama-2-1.3b, chinese-llama-2-7b, chinese-llama-2-13b, chinese-alpaca-2-1.3b, chinese-alpaca-2-7b and chinese-alpaca-2-13b along with their corresponding 16k and 64k long text versions.
  • 2024.04.22: Support for inference and fine-tuning of Llama3 GPTQ-Int4, GPTQ-Int8, and AWQ series models. Support for inference and fine-tuning of chatglm3-6b-128k, Openbuddy-Llama3.
  • 2024.04.20: Support for inference, fine-tuning, and deployment of Atom series models. This includes: Atom-7B and Atom-7B-Chat. use this script to train.
  • 2024.04.19: Support for single-card, DDP, ZeRO2, and ZeRO3 training and inference with NPU, please refer to NPU Inference and Fine-tuning Best Practice.
  • 2024.04.19: Support for inference, fine-tuning, and deployment of Llama3 series models. This includes: Llama-3-8B, Llama-3-8B-Instruct, Llama-3-70B, and Llama-3-70B-Instruct. use this script to train.
  • 2024.04.18: Supported models: wizardlm2-7b-awq, wizardlm2-8x22b, yi-6b-chat-awq, yi-6b-chat-int8, yi-34b-chat-awq, yi-34b-chat-int8. Supported --deepspeed zero3-offload and provided default zero3-offload configuration file for zero3+cpu offload usage.
  • 2024.04.18: Supported compatibility with HuggingFace ecosystem using the environment variable USE_HF, switching to use models and datasets from HF. Please refer to the HuggingFace ecosystem compatibility documentation.
  • 2024.04.17: Support the evaluation for OpenAI standard interfaces. Check the parameter documentation for details.
  • 🔥2024.04.17: Support CodeQwen1.5-7B series: CodeQwen1.5-7B, CodeQwen1.5-7B-Chat,CodeQwen1.5-7B-Chat-AWQ, use this script to train.
  • 2024.04.16: Supports inference and fine-tuning of llava-v1.6-34b model. For best practice, you can refer to here.
  • 2024.04.13: Support the fine-tuning and inference of Mixtral-8x22B-v0.1 model, use this script to start training!
  • 2024.04.13: Support the newly launched MiniCPM series: MiniCPM-V-2.0、MiniCPM-2B-128k、MiniCPM-MoE-8x2B and MiniCPM-1B.use this script to start training!
  • 🔥2024.04.11: Support Model Evaluation with MMLU/ARC/CEval datasets(also user custom eval datasets) with one command! Check this documentation for details. Meanwhile, we support a trick way to do multiple ablation experiments, check this documentation to use.
  • 🔥2024.04.11: Support c4ai-command-r series: c4ai-command-r-plus, c4ai-command-r-v01, use this script to train.
  • 2024.04.10: Use SWIFT to fine-tune the qwen-7b-chat model to enhance its function call capabilities, and combine it with Modelscope-Agent for best practices, which can be found here.
  • 🔥2024.04.09: Support ruozhiba dataset. Search ruozhiba in this documentation to begin training!
  • 2024.04.08: Support the fine-tuning and inference of XVERSE-MoE-A4.2B model, use this script to start training!
  • 2024.04.04: Support QLoRA+FSDP to train a 70B model with two 24G memory GPUs, use this script to train.
  • 🔥2024.04.03: Support Qwen1.5-32B series: Qwen1.5-32B, Qwen1.5-32B-Chat, Qwen1.5-32B-Chat-GPTQ-Int4.use this script to start training!
  • 🔥2024.04.02: Support the fine-tuning and inference of Mengzi3-13B-Base model, use this script to start training!
  • 🔥2024.04.01: Support dbrx series: dbrx-base and dbrx-instruct, use this script to start training!
  • 🔥2024.03.29: Support Qwen1.5-MoE series: Qwen1.5-MoE-A2.7B, Qwen1.5-MoE-A2.7B-Chat, Qwen1.5-MoE-A2.7B-Chat-GPTQ-Int4.
  • 🔥2024.03.29: Support the fine-tuning and inference of Grok-1 300B MoE, please view details here.
  • 🔥2024.03.25: Supports inference and fine-tuning of TeleChat-7b and TeleChat-12b model, use this script to start training!
  • 🔥2024.03.20: Supports inference and fine-tuning for the llava series. For best practice, you can refer to here.
  • 🔥2024.03.12: Support inference and fine-tuning for deepseek-vl series. Best practices can be found here.
  • 🔥2024.03.11: Support GaLore for effectively reducing memory usage to 1/2 of the original in full-parameter training.
  • 🔥2024.03.10: End-to-end best practices from fine-tuning to deployment for Qwen1.5-7B-Chat and Qwen1.5-72B-Chat.
  • 🔥2024.03.09: Support training and inference of MAMBA model, use this script to start training!
  • 2024.03.09: Support training and inference of AQLM quantized model, use this script to start training!
  • 2024.03.06: Support training and inference of AWQ quantized model, use this Qwen1.5-AWQ model script to start training, and support training and inference of yi-9b.
  • 🔥2024.02.29: Support LLaMA PRO, simply use this script to start training.
  • 🔥2024.02.29: Support LoRA+, simply use this script to start training.
  • 2024.02.25: Support swift export to quantize models using AWQ/GPTQ and push to ModelScope Hub. See documentation: LLM Quantization.
  • 2024.02.22: Support gemma series: gemma-2b, gemma-2b-instruct, gemma-7b, gemma-7b-instruct.
  • 2024.02.16: Support deepseek-math series: deepseek-math-7b, deepseek-math-7b-instruct, deepseek-math-7b-chat.
  • 🔥2024.02.05: Support Qwen1.5 series models, see model list for all supported Qwen1.5 models. Provide fine-tuning scripts for qwen1half-7b-chat, qwen1half-7b-chat-int8.
  • 2024.02.05: Support training of diffusion models such as SDXL, SD, ControlNet, as well as DreamBooth training. See corresponding training scripts for details.
  • 2024.02.01: Support minicpm series: minicpm-2b-sft-chat, minicpm-2b-chat.
  • 🔥2024.02.01: Support dataset mixing to reduce catastrophic forgetting. Use --train_dataset_mix_ratio 2.0 to enable training! We also open sourced the general knowledge dataset ms-bench.
  • 🔥2024.02.01: Support Agent training! Agent training algorithm is derived from this paper. We also added ms-agent, a high-quality agent dataset. Use this script to start Agent training!
  • 🔥2024.02.01: Support adding SFT loss in DPO training to reduce repetitive generation caused by KL divergence loss.
  • 2024.02.01: Support using AdaLoRA and IA3 adapters in training.
  • 2024.02.01: Support --merge_lora parameter in AnimateDiff training.
  • 2024.01.30: Support internlm-xcomposer2-7b-chat.
  • 🔥2024.01.30: Support ZeRO-3, simply specify --deepspeed default-zero3.
  • 2024.01.29: Support internlm2-math series: internlm2-math-7b, internlm2-math-7b-chat, internlm2-math-20b, internlm2-math-20b-chat.
  • 🔥2024.01.26: Support yi-vl-6b-chat, yi-vl-34b-chat.
  • 2024.01.24: Support codefuse-codegeex2-6b-chat, codefuse-qwen-14b-chat.
  • 2024.01.23: Support orion series: orion-14b, orion-14b-chat.
  • 2024.01.20: Support xverse-13b-256k, xverse-65b-v2, xverse-65b-chat.
  • 🔥2024.01.17: Support internlm2 series: internlm2-7b-base, internlm2-7b, internlm2-7b-sft-chat, internlm2-7b-chat, internlm2-20b-base, internlm2-20b, internlm2-20b-sft-chat, internlm2-20b-chat.
  • 2024.01.15: Support yuan series: yuan2-2b-instruct, yuan2-2b-janus-instruct, yuan2-51b-instruct, yuan2-102b-instruct.
  • 🔥2024.01.12: Support deepseek-moe series: deepseek-moe-16b, deepseek-moe-16b-chat.
  • 🔥2024.01.04: Support VLLM deployment, compatible with OpenAI API style, see VLLM Inference Acceleration and Deployment for details.
  • 2024.01.04: Update Benchmark for convenient viewing of training speed and memory usage of different models.
  • 🔥2023.12.29: Support web-ui for sft training and inference, use swift web-ui after installing ms-swift to start.
  • 🔥2023.12.29: Support DPO RLHF (Reinforcement Learning from Human Feedback) and three datasets for this task: AI-ModelScope/stack-exchange-paired, AI-ModelScope/hh-rlhf and AI-ModelScope/hh_rlhf_cn. See documentation to start training!
  • 🔥2023.12.28: Support SCEdit! This tuner can significantly reduce memory usage in U-Net and support low-memory controllable image generation (replacing ControlNet), read the section below to learn more.
  • 2023.12.23: Support codegeex2-6b.
  • 2023.12.19: Support phi2-3b.
  • 2023.12.18: Support VLLM for inference acceleration.
  • 2023.12.15: Support deepseek, deepseek-coder series: deepseek-7b, deepseek-7b-chat, deepseek-67b, deepseek-67b-chat, openbuddy-deepseek-67b-chat, deepseek-coder-1_3b, deepseek-coder-1_3b-instruct, deepseek-coder-6_7b, deepseek-coder-6_7b-instruct, deepseek-coder-33b, deepseek-coder-33b-instruct.
  • 2023.12.13: Support mistral-7b-instruct-v2, mixtral-moe-7b, mixtral-moe-7b-instruct.
  • 2023.12.09: Support freeze_parameters parameter as a compromise between lora and full-parameter training. Corresponding sh can be found in full_freeze_ddp. Support disable_tqdm, lazy_tokenize, preprocess_num_proc parameters, see command line arguments for details.
  • 2023.12.08: Support sus-34b-chat, support yi-6b-200k, yi-34b-200k.
  • 2023.12.07: Support Multi-Node DDP training.
  • 2023.12.05: Support models: zephyr-7b-beta-chat, openbuddy-zephyr-7b-chat. Support datasets: hc3-zh, hc3-en.
  • 🔥2023.12.02: Self-cognition fine-tuning best practices, 10 minutes to fine-tune a large model for self-cognition, create your own unique large model.
  • 🔥2023.11.30: Support training and inference of qwen-1_8b, qwen-72b, qwen-audio series models. Corresponding sh scripts can be found in qwen_1_8b_chat, qwen_72b_chat, qwen_audio_chat
  • 🔥2023.11.29: Support training and inference of AnimateDiff
  • 🔥2023.11.24: Support yi-34b-chat, codefuse-codellama-34b-chat models. Corresponding sh scripts can be found in yi_34b_chat, codefuse_codellama_34b_chat.
  • 🔥2023.11.18: Support tongyi-finance-14b series models: tongyi-finance-14b, tongyi-finance-14b-chat, tongyi-finance-14b-chat-int4. Corresponding sh scripts can be found in tongyi_finance_14b_chat_int4.
  • 2023.11.16: Support flash attn for more models: qwen series, qwen-vl series, llama series, openbuddy series, mistral series, yi series, ziya series. Please use use_flash_attn parameter.
  • 🔥2023.11.11: Support NEFTune, simply use Swift.prepare_model(model, NEFTuneConfig()) to enable.
  • 🔥2023.11.11: Support training and inference by command line and inference by Web-UI, see Usage with Swift CLI section below for details.
  • 🔥2023.11.10: Support bluelm series models: bluelm-7b, bluelm-7b-chat, bluelm-7b-32k, bluelm-7b-chat-32k. Corresponding sh scripts can be found in bluelm_7b_chat.
  • 🔥2023.11.08: Support training and inference of xverse-65b model, script at xverse_65b.
  • 🔥2023.11.07: Support training and inference of yi-6b, yi-34b models, scripts at yi_6b, yi_34b.
  • 🔥2023.10.30: Support two new tuners: QA-LoRA and LongLoRA.
  • 🔥2023.10.30: Support editing models using ROME (Rank One Model Editing) to infuse new knowledge into models without training!
  • 2023.10.30: Support skywork-13b series models: skywork-13b, skywork-13b-chat. Corresponding sh scripts can be found in skywork_13b.
  • 🔥2023.10.27: Support chatglm3 series models: chatglm3-6b-base, chatglm3-6b, chatglm3-6b-32k. Corresponding sh scripts can be found in chatglm3_6b.
  • 🔥2023.10.17: Support SFT of int4, int8 models: qwen-7b-chat-int4, qwen-14b-chat-int4, qwen-vl-chat-int4, baichuan2-7b-chat-int4, baichuan2-13b-chat-int4, qwen-7b-chat-int8, qwen-14b-chat-int8.
  • 2023.10.15: Support ziya2-13b series models: ziya2-13b, ziya2-13b-chat.
  • 2023.10.12: Support mistral-7b series models: openbuddy-mistral-7b-chat, mistral-7b, mistral-7b-instruct.
  • 🔥2023.10.07: Support DeepSpeed ZeRO-2, enabling lora (not just qlora) to run DDP on dual A10 cards.
  • 2023.10.04: Support more math, law, SQL, code domain datasets: blossom-math-zh, school-math-zh, text2sql-en, sql-create-context-en, lawyer-llama-zh, tigerbot-law-zh, leetcode-python-en.
  • 🔥2023.09.25: Support qwen-14b series: qwen-14b, qwen-14b-chat.
  • 2023.09.18: Support internlm-20b series: internlm-20b, internlm-20b-chat.
  • 2023.09.12: Support MP+DDP to accelerate full-parameter training.
  • 2023.09.05: Support openbuddy-llama2-70b-chat.
  • 2023.09.03: Support baichuan2 series: baichuan2-7b, baichuan2-7b-chat, baichuan2-13b, baichuan2-13b-chat.

🛠️ Installation

SWIFT runs in the Python environment. Please ensure your Python version is higher than 3.8.

  • Method 1: Install SWIFT using pip command:
# Full capabilities
pip install 'ms-swift[all]' -U
# LLM only
pip install 'ms-swift[llm]' -U
# AIGC only
pip install 'ms-swift[aigc]' -U
# Adapters only
pip install ms-swift -U
  • Method 2: Install SWIFT through source code (convenient for running training and inference scripts), please run the following commands:
git clone https://github.com/modelscope/swift.git
cd swift
pip install -e '.[llm]'

SWIFT depends on torch>=1.13, recommend torch>=2.0.0.

  • Method 3: Use SWIFT in our Docker image
# China-Hangzhou image
docker pull registry.cn-hangzhou.aliyuncs.com/modelscope-repo/modelscope:ubuntu22.04-cuda12.1.0-py310-torch2.1.2-tf2.14.0-1.13.1
# US-west image
docker pull registry.us-west-1.aliyuncs.com/modelscope-repo/modelscope:ubuntu22.04-cuda12.1.0-py310-torch2.1.2-tf2.14.0-1.13.1

🚀 Getting Started

This section introduces basic usage, see the Documentation section for more ways to use.

Web-UI

Web-UI is a gradio-based interface for zero-threshold training and deployment. It is easy to use and perfectly supports multi-GPU training and deployment:

SWIFT_UI_LANG=en swift web-ui

image.png

Training

Training Scripts

You can refer to the following scripts to customize your own training script.

Supported Training Processes

Training Process Training Method
Pretraining Text Generation
Fine-tuning Single-turn/Multi-turn
Agent Training/Self-cognition
Multi-modal Vision/Multi-modal Speech
Human Alignment DPO
ORPO
SimPO
Text-to-Image DreamBooth, etc.
Text-to-Video -

Single GPU Training

Start single GPU fine-tuning with the following command:

LoRA:

# Experimental Environment: A100
# GPU Memory Requirement: 20GB
# Runtime: 3.1 hours
CUDA_VISIBLE_DEVICES=0 \
swift sft \
    --model_type qwen1half-7b-chat \
    --dataset blossom-math-zh \
    --num_train_epochs 5 \
    --sft_type lora \
    --output_dir output \
    --eval_steps 200 \

Full-parameter:

# Experimental Environment: A100
# GPU Memory Requirement: 80GB
# Runtime: 2.5 hours
CUDA_VISIBLE_DEVICES=0 \
swift sft \
    --model_type qwen1half-7b-chat \
    --dataset blossom-math-zh \
    --num_train_epochs 5 \
    --sft_type full \
    --output_dir output \
    --eval_steps 500 \

Model Parallel Training

# Experimental Environment: 2 * A100
# GPU Memory Requirement: 10GB + 13GB
# Runtime: 3.4 hours
CUDA_VISIBLE_DEVICES=0,1 \
swift sft \
    --model_type qwen1half-7b-chat \
    --dataset blossom-math-zh \
    --num_train_epochs 5 \
    --sft_type lora \
    --output_dir output \

Data Parallel Training

# Experimental Environment: 4 * A100
# GPU Memory Requirement: 4 * 30GB
# Runtime: 0.8 hours
NPROC_PER_NODE=4 \
CUDA_VISIBLE_DEVICES=0,1,2,3 \
swift sft \
    --model_type qwen1half-7b-chat \
    --dataset blossom-math-zh \
    --num_train_epochs 5 \
    --sft_type lora \
    --output_dir output \

Combining Model Parallelism and Data Parallelism:

# Experimental Environment: 4 * A100
# GPU Memory Requirement: 2*14GB + 2*18GB
# Runtime: 1.7 hours
NPROC_PER_NODE=2 \
CUDA_VISIBLE_DEVICES=0,1,2,3 \
swift sft \
    --model_type qwen1half-7b-chat \
    --dataset blossom-math-zh \
    --num_train_epochs 5 \
    --sft_type lora \
    --output_dir output \

Deepspeed Training

Deepspeed supports training of quantized GPTQ and AWQ models.

ZeRO2:

# Experimental Environment: 4 * A100
# GPU Memory Requirement: 4 * 21GB
# Runtime: 0.9 hours
NPROC_PER_NODE=4 \
CUDA_VISIBLE_DEVICES=0,1,2,3 \
swift sft \
    --model_type qwen1half-7b-chat \
    --dataset blossom-math-zh \
    --num_train_epochs 5 \
    --sft_type lora \
    --output_dir output \
    --deepspeed default-zero2 \

ZeRO3:

# Experimental Environment: 4 * A100
# GPU Memory Requirement: 4 * 19GB
# Runtime: 3.2 hours
NPROC_PER_NODE=4 \
CUDA_VISIBLE_DEVICES=0,1,2,3 \
swift sft \
    --model_type qwen1half-7b-chat \
    --dataset blossom-math-zh \
    --num_train_epochs 5 \
    --sft_type lora \
    --output_dir output \
    --deepspeed default-zero3 \

ZeRO3-Offload:

# Experimental Environment: 4 * A100
# GPU Memory Requirement: 4 * 12GB
# Runtime: 60 hours
NPROC_PER_NODE=4 \
CUDA_VISIBLE_DEVICES=0,1,2,3 \
swift sft \
    --model_id_or_path AI-ModelScope/WizardLM-2-8x22B \
    --dataset blossom-math-zh \
    --num_train_epochs 5 \
    --sft_type lora \
    --output_dir output \
    --deepspeed zero3-offload \

Multi-node Multi-GPU

# node0
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
NNODES=2 \
NODE_RANK=0 \
MASTER_ADDR=127.0.0.1 \
NPROC_PER_NODE=8 \
swift sft \
    --model_id_or_path qwen1half-32b-chat \
    --sft_type full \
    --dataset blossom-math-zh \
    --output_dir output \
    --deepspeed default-zero3 \

# node1
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
NNODES=2 \
NODE_RANK=1 \
MASTER_ADDR=xxx.xxx.xxx.xxx \
NPROC_PER_NODE=8 \
swift sft \
    --model_id_or_path qwen1half-32b-chat \
    --sft_type full \
    --dataset blossom-math-zh \
    --output_dir output \
    --deepspeed default-zero3 \
AliYun-DLC multi-node training

In DLC product, WORLD_SIZE is the node number, RANK is the node index, this is different from the definition of torchrun.

NNODES=$WORLD_SIZE \
NODE_RANK=$RANK \
swift sft \
    --model_id_or_path qwen1half-32b-chat \
    --sft_type full \
    --dataset blossom-math-zh \
    --output_dir output \
    --deepspeed default-zero3

Inference

Original model:

CUDA_VISIBLE_DEVICES=0 swift infer --model_type qwen1half-7b-chat
# use VLLM
CUDA_VISIBLE_DEVICES=0 swift infer --model_type qwen1half-7b-chat \
    --infer_backend vllm --max_model_len 8192

LoRA fine-tuned:

CUDA_VISIBLE_DEVICES=0 swift infer --ckpt_dir xxx/checkpoint-xxx --load_dataset_config true
# use VLLM
CUDA_VISIBLE_DEVICES=0 swift infer \
    --ckpt_dir xxx/checkpoint-xxx --load_dataset_config true \
    --merge_lora true --infer_backend vllm --max_model_len 8192

Evaluation

CUDA_VISIBLE_DEVICES=0 swift eval --model_type qwen1half-7b-chat --eval_dataset mmlu ceval

Quantization

Original model:

CUDA_VISIBLE_DEVICES=0 swift export --model_type qwen1half-7b-chat \
    --quant_bits 4 --quant_method awq

LoRA fine-tuned:

CUDA_VISIBLE_DEVICES=0 swift export \
    --ckpt_dir xxx/checkpoint-xxx --load_dataset_config true \
    --quant_method awq --quant_bits 4 \
    --merge_lora true \

Deployment

The client uses the OpenAI API for invocation, for details refer to the LLM deployment documentation.

Original model:

CUDA_VISIBLE_DEVICES=0 swift deploy --model_type qwen1half-7b-chat
# 使用VLLM加速
CUDA_VISIBLE_DEVICES=0 swift deploy --model_type qwen1half-7b-chat \
    --infer_backend vllm --max_model_len 8192

LoRA fine-tuned:

CUDA_VISIBLE_DEVICES=0 swift deploy --ckpt_dir xxx/checkpoint-xxx
# 使用VLLM加速
CUDA_VISIBLE_DEVICES=0 swift deploy \
    --ckpt_dir xxx/checkpoint-xxx --merge_lora true \
    --infer_backend vllm --max_model_len 8192

Supported Models

The complete list of supported models and datasets can be found at Supported Models and Datasets List.

LLMs

Model Type Model Introduction Language Model Size Model Type
Qwen
Qwen1.5		 Tongyi Qwen 1.0 and 1.5 series models Chinese
English		 0.5B-110B
including quantized versions		 base model
chat model
MoE model
code model
ChatGLM2
ChatGLM3
Codegeex2
GLM4		 Zhipu ChatGLM series models Chinese
English		 6B-9B base model
chat model
code model
long text model
Baichuan/Baichuan2 Baichuan 1 and Baichuan 2 Chinese
English		 7B-13B
including quantized versions		 base model
chat model
Yuan2 Langchao Yuan series models Chinese
English		 2B-102B instruct model
XVerse XVerse series models Chinese
English		 7B-65B base model
chat model
long text model
MoE model
LLaMA2 LLaMA2 series models English 7B-70B
including quantized versions		 base model
chat model
LLaMA3 LLaMA3 series models English 8B-70B
including quantized versions		 base model
chat model
Mistral
Mixtral		 Mistral series models English 7B-22B base model
instruct model
MoE model
Yi
Yi1.5		 01AI's YI series models Chinese
English		 6B-34B
including quantized		 base model
chat model
long text model
InternLM
InternLM2
InternLM2-Math		 Pujiang AI Lab InternLM series models Chinese
English		 1.8B-20B base model
chat model
math model
DeepSeek
DeepSeek-MoE
DeepSeek-Coder
DeepSeek-Math
DeepSeek-V2		 DeepSeek series models Chinese
English		 1.3B-236B base model
chat model
MoE model
code model
math model
MAMBA MAMBA temporal convolution model English 130M-2.8B base model
Gemma Google Gemma series models English 2B-7B base model
instruct model
MiniCPM OpenBmB MiniCPM series models Chinese
English		 2B-3B chat model
MoE model
OpenBuddy OpenBuddy series models Chinese
English		 7B-67B base model
chat model
Orion OrionStar AI series models Chinese
English		 14B base model
chat model
BlueLM VIVO BlueLM large model Chinese
English		 7B base model
chat model
Ziya2 Fengshenbang series models Chinese
English		 13B base model
chat model
Skywork Skywork series models Chinese
English		 13B base model
chat model
Zephyr Zephyr series models based on Mistral English 7B chat model
PolyLM Tongyi Lab self-developed PolyLM series models Multilingual 13B base model
SeqGPT Tongyi Lab self-developed text understanding model for information extraction and text classification Chinese 560M semantic understanding model
SUS Southern University of Science and Technology model fine-tuned on YI Chinese
English		 34B chat model
Tongyi-Finance Tongyi finance series models Chinese
English		 14B base model
chat model
financial model
CodeFuse-CodeLLaMA
CodeFuse-Codegeex2
CodeFuse-Qwen		 Ant CodeFuse series models Chinese
English		 6B-34B chat model
code model
phi2/phi3 Microsoft's PHI series models English 3B/4B base model
instruct model
code model
Grok X-ai English 300B base model
TeleChat Tele-AI Chinese
English		 7B-12B chat model
dbrx databricks English 132B base model
chat model
mengzi3 Langboat Chinese
English		 13B base model
c4ai-command-r c4ai Multilingual 35B-104B chat model
WizardLM2 WizardLM2 series models English 7B-8x22B
including quantized versions		 chat model
MoE model
Atom Atom Chinese 7B base model
chat model
Chinese-LLaMA-Alpaca-2 Chinese-LLaMA-Alpaca-2 Chinese 1.3B-13B base model
chat model
long text model
Chinese-LLaMA-Alpaca-3 Chinese-LLaMA-Alpaca-3 Chinese 8B base model
chat model
ModelScope-Agent ModelScope Agent series models Chinese 7B-14B agent model

MLLMs

Model Type Model Introduction Language Model Size Model Type
Qwen-VL Tongyi Qwen vision model Chinese
English		 7B
including quantized versions		 base model
chat model
Qwen-Audio Tongyi Qwen speech model Chinese
English		 7B base model
chat model
YI-VL 01AI's YI series vision models Chinese
English		 6B-34B chat model
XComposer2 Pujiang AI Lab InternLM vision model Chinese
English		 7B chat model
DeepSeek-VL DeepSeek series vision models Chinese
English		 1.3B-7B chat model
MiniCPM-V
MiniCPM-V-2
MiniCPM-V-2_5		 OpenBmB MiniCPM vision model Chinese
English		 3B-9B chat model
CogVLM
CogVLM2
CogAgent
GLM4V		 Zhipu ChatGLM visual QA and Agent model Chinese
English		 9B-19B chat model
Llava Llava series models English 7B-34B chat model
Llava-Next Llava-Next series models Chinese
English		 8B-110B chat model
mPLUG-Owl mPLUG-Owl series models English 11B chat model
InternVL InternVL Chinese
English		 2B-25.5B
including quantized version		 chat model
Llava-llama3 xtuner English 8B chat model
Phi3-Vision Microsoft English 4B chat model
PaliGemma Google English 3B chat model

Diffusion Models

Model Type Model Introduction Language Model Type
AnimateDiff AnimateDiff animation model English text-to-video
SD1.5/SD2.0/SDXL StabilityAI series diffusion models English text-to-image

Supported Open Source Datasets

Dataset Type Training Task Documentation
General Fine-tuning 🔥ruozhiba, 🔥ms-bench, 🔥alpaca-en(gpt4), 🔥alpaca-zh(gpt4), multi-alpaca, instinwild, cot-en, cot-zh, firefly-zh, instruct-en, gpt4all-en, sharegpt, tulu-v2-sft-mixture, wikipedia-zh, open-orca, sharegpt-gpt4, deepctrl-sft, coig-cqia.
Agent Fine-tuning 🔥ms-agent, 🔥ms-agent-for-agentfabric, ms-agent-multirole, 🔥toolbench-for-alpha-umi, damo-agent-zh, damo-agent-zh-mini, agent-instruct-all-en.
General Human Alignment hh-rlhf, 🔥hh-rlhf-cn, stack-exchange-paired.
Code Fine-tuning code-alpaca-en, 🔥leetcode-python-en, 🔥codefuse-python-en, 🔥codefuse-evol-instruction-zh.
Medical Fine-tuning medical-en, medical-zh, 🔥disc-med-sft-zh.
Legal Fine-tuning lawyer-llama-zh, tigerbot-law-zh, 🔥disc-law-sft-zh.
Math Fine-tuning 🔥blossom-math-zh, school-math-zh, open-platypus-en.
SQL Fine-tuning text2sql-en, 🔥sql-create-context-en.
Text Generation Fine-tuning 🔥advertise-gen-zh, 🔥dureader-robust-zh.
Classification Fine-tuning cmnli-zh, 🔥jd-sentiment-zh, 🔥hc3-zh, 🔥hc3-en.
Quantization Assist Quantization pileval.
Other Fine-tuning finance-en, poetry-zh, webnovel-zh, generated-chat-zh, cls-fudan-news-zh, ner-jave-zh.
Vision Fine-tuning coco-en, 🔥coco-en-mini, coco-en-2, coco-en-2-mini, capcha-images.
Audio Fine-tuning aishell1-zh, 🔥aishell1-zh-mini.

Supported Technologies

Technology Name
🔥LoRA: LORA: LOW-RANK ADAPTATION OF LARGE LANGUAGE MODELS
🔥LoRA+: LoRA+: Efficient Low Rank Adaptation of Large Models
🔥GaLore:GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection
🔥LISA: LISA: Layerwise Importance Sampling for Memory-Efficient Large Language Model Fine-Tuning
🔥UnSloth: https://github.com/unslothai/unsloth
🔥LLaMA PRO: LLAMA PRO: Progressive LLaMA with Block Expansion
🔥SCEdit: SCEdit: Efficient and Controllable Image Diffusion Generation via Skip Connection Editing < arXiv \
🔥NEFTune: Noisy Embeddings Improve Instruction Finetuning
LongLoRA: Efficient Fine-tuning of Long-Context Large Language Models
Adapter: Parameter-Efficient Transfer Learning for NLP
Vision Prompt Tuning: Visual Prompt Tuning
Side: Side-Tuning: A Baseline for Network Adaptation via Additive Side Networks
Res-Tuning: Res-Tuning: A Flexible and Efficient Tuning Paradigm via Unbinding Tuner from Backbone < arXiv \
Tuners provided by PEFT, such as IA3, AdaLoRA, etc.

Supported Hardware

Hardware Environment Notes
CPU
RTX 20/30/40 series, etc. After 30 series, BF16 and FlashAttn can be used
Computing cards T4/V100, etc. BF16 and FlashAttn not supported
Computing cards A10/A100, etc. Support BF16 and FlashAttn
Huawei Ascend NPU

📃 Documentation

Documentation Compiling

make docs
# Check docs/build/html/index.html in web-browser

User Guide

Document Name
Using Web-UI
Using Tuners
LLM Inference
LLM Fine-tuning
LLM Evaluation
LLM Quantization
LLM Deployment
AnimateDiff Training

Reference Documentation

Document Name
Command Line Arguments
Supported Models and Datasets List
Customizing New Models and Datasets
Runtime Speed and Memory Benchmark

Best Practices

Best Practices Name
Agent Fine-Tuning Best Practice
Self-Cognition Fine-Tuning Best Practice
Qwen1.5 Best Practice
Multi-Modal Model Training Best Practice
NPU Best Practice
DPO Human Alignment Training
ORPO Human Alignment Training
SimPO Human Alignment Training

Deep Learning Tutorials

Tutorial Name
Introduction to Deep Learning
Large Model Basics
Prompt Engineering
Transformer Architecture Introduction
Training Technique Selection
Data Preprocessing
Quantization
Training
Inference
Deployment
Evaluation

🏛 License

This framework is licensed under the Apache License (Version 2.0). For models and datasets, please refer to the original resource page and follow the corresponding License.

📎 Citation

@Misc{swift,
  title = {SWIFT:Scalable lightWeight Infrastructure for Fine-Tuning},
  author = {The ModelScope Team},
  howpublished = {\url{https://github.com/modelscope/swift}},
  year = {2024}
}

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ms-swift: Use PEFT or Full-parameter to finetune 250+ LLMs or 35+ MLLMs. (Qwen, GLM4, Internlm2, Yi, Llama3, Llava, Deepseek, ...)

github.com/modelscope/swift/blob/main/docs/source/LLM/index.md

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