SWIFT (Scalable lightWeight Infrastructure for Fine-Tuning)

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📖 Table of Contents

• Introduction
• News
• Installation
• Getting Started
• Documentation
• License
• Citation
• Contact Us

📝 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.

🎉 News

• 🔥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.

More

• 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

swift web-ui

Training

Training Scripts

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

• full: qwen1half-7b-chat (A100), qwen-7b-chat (2*A100)
• full+ddp+zero2: qwen-7b-chat (4*A100)
• full+ddp+zero3: qwen-14b-chat (4*A100)
• lora: chatglm3-6b (3090), baichuan2-13b-chat (2*3090), yi-34b-chat (A100), qwen-72b-chat (2*A100)
• lora+ddp: chatglm3-6b (2*3090)
• lora+ddp+zero3: qwen-14b-chat (4*3090), qwen-72b-chat (4*A100)
• qlora(gptq-int4): qwen-7b-chat-int4 (3090)
• qlora(gptq-int8): qwen1half-7b-chat-int8 (3090)
• qlora(bnb-int4): qwen-7b-chat (3090)

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
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

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 \

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

# Debugging, on line soon:>
CUDA_VISIBLE_DEVICES=0 swift eval --model_type qwen1half-7b-chat --eval_dataset mmlu ceval

Export

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

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

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-72B including quantized versions	base model chat model
ChatGLM2 ChatGLM3 Codegeex2	Zhipu ChatGLM series models	Chinese English	6B	base model chat model code 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
LLaMA2	LLaMA2 series models	English	7B-70B including quantized versions	base model chat model
Mistral Mixtral	Mistral series models	English	7B	base model instruct model MoE model
YI	01AI's YI series models	Chinese English	6B-34B	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 series models	Chinese English	1.3B-67B	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
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	Microsoft's PHI2 model	English	3B	base model code 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	OpenBmB MiniCPM vision model	Chinese English	3B	chat model
CogVLM CogAgent	Zhipu ChatGLM visual QA and Agent model	English	17B-18B	chat model
Llava	Llava series models	English	7B	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	🔥ms-bench, 🔥ms-bench-mini, 🔥alpaca-en(gpt4), 🔥alpaca-zh(gpt4), multi-alpaca-all, instinwild-en, instinwild-zh, cot-en, cot-zh, firefly-all-zh, instruct-en, gpt4all-en, sharegpt-en, sharegpt-zh, tulu-v2-sft-mixture, wikipedia-zh, open-orca, open-orca-gpt4, sharegpt-gpt4, 🔥sharegpt-gpt4-mini.
Agent	Fine-tuning	🔥ms-agent, damo-mini-agent-zh, damo-agent-zh, agent-instruct-all-en.
General	Human Alignment	🔥hh-rlhf-cn, stack-exchange-paired, hh-rlhf-harmless-base, hh-rlhf-helpful-base, hh-rlhf-helpful-online, hh-rlhf-helpful-rejection-sampled, hh-rlhf-red-team-attempts, hh-rlhf-cn-harmless-base-cn, hh-rlhf-cn-helpful-base-cn, hh-rlhf-cn-harmless-base-en, hh-rlhf-cn-helpful-base-en.
Code	Fine-tuning	code-alpaca-en, 🔥leetcode-python-en, 🔥codefuse-python-en, 🔥codefuse-evol-instruction-zh.
Medical	Fine-tuning	medical-en, medical-zh, medical-mini-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, 🔥cmnli-mini-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-mini-en, coco-mini-en-2, capcha-images.
Audio	Fine-tuning	aishell1-zh, 🔥aishell1-mini-zh.

Supported Technologies

Technology Name
🔥LoRA: LORA: LOW-RANK ADAPTATION OF LARGE LANGUAGE MODELS
🔥LoRA+: LoRA+: Efficient Low Rank Adaptation of Large Models
🔥LLaMA PRO: LLAMA PRO: Progressive LLaMA with Block Expansion
🔥SCEdit: SCEdit: Efficient and Controllable Image Diffusion Generation via Skip Connection Editing < arXiv | Project Page >
🔥NEFTune: Noisy Embeddings Improve Instruction Finetuning
QA-LoRA:Quantization-Aware Low-Rank Adaptation of Large Language Models
LongLoRA: Efficient Fine-tuning of Long-Context Large Language Models
ROME: Rank-One Editing of Encoder-Decoder Models
Adapter: Parameter-Efficient Transfer Learning for NLP
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 | Project Page | Usage >
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 Fine-tuning
LLM Inference
LLM Quantization
LLM Deployment
DPO Human Alignment Training
AnimateDiff Training

Reference Documentation

Document Name
Command Line Arguments
Customizing New Models and Datasets
Supported Models and Datasets List
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

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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