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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!
- 🔥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_5andmini-internvl-chat-4b-v1_5to train. - 2024.05.24: Supports Phi3-vision model, Use model_type
phi3-vision-128k-instructto train. - 2024.05.22: Supports DeepSeek-V2-Lite series models, model_type are
deepseek-v2-liteanddeepseek-v2-lite-chat - 2024.05.22: Supports TeleChat-12B-v2 model with quantized version, model_type are
telechat-12b-v2andtelechat-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,VeraandPissa. use--sft_type boft/verato use BOFT or Vera, use--init_lora_weights pissawith--sft_type lorato 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-chatto 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 nto 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=2to use LISA(to reduce the memory cost to 30 percent!), specify--tuner_backend unslothto 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-offloadand 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
ruozhibain 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 exportto 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 2.0to 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_loraparameter 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-uiafter 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_parametersparameter as a compromise between lora and full-parameter training. Corresponding sh can be found in full_freeze_ddp. Supportdisable_tqdm,lazy_tokenize,preprocess_num_procparameters, 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_attnparameter. - 🔥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 CLIsection 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.
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-tf2.14.0-1.13.1This section introduces basic usage, see the Documentation section for more ways to use.
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
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)
| 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 | - |
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 \# 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 \# 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 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 \# 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 \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-zero3Original 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 8192LoRA 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 8192CUDA_VISIBLE_DEVICES=0 swift eval --model_type qwen1half-7b-chat --eval_dataset mmlu cevalOriginal model:
CUDA_VISIBLE_DEVICES=0 swift export --model_type qwen1half-7b-chat \
--quant_bits 4 --quant_method awqLoRA 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 \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 8192LoRA 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 8192The complete list of supported models and datasets can be found at Supported Models and Datasets List.
| 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 |
| 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 | English | 3B | chat model |
| 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 |
| 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. |
| 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. |
| 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 |
make docs
# Check docs/build/html/index.html in web-browser| Document Name |
|---|
| Using Web-UI |
| Using Tuners |
| LLM Inference |
| LLM Fine-tuning |
| LLM Evaluation |
| LLM Quantization |
| LLM Deployment |
| AnimateDiff Training |
| Document Name |
|---|
| Command Line Arguments |
| Supported Models and Datasets List |
| Customizing New Models and Datasets |
| Runtime Speed and Memory Benchmark |
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.
@Misc{swift,
title = {SWIFT:Scalable lightWeight Infrastructure for Fine-Tuning},
author = {The ModelScope Team},
howpublished = {\url{https://github.com/modelscope/swift}},
year = {2024}
}You can contact us and communicate with us by adding our WeChat group:
