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| SWIFT (Scalable lightWeight Infrastructure for Fine-Tuning)
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SWIFT supports training, inference, evaluation and deployment of 300+ LLMs and 50+ 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 2024.07.06: Support for the llava-next-video series models: llava-next-video-7b-instruct, llava-next-video-7b-32k-instruct, llava-next-video-7b-dpo-instruct, llava-next-video-34b-instruct. You can refer to llava-video best practice for more information. 2024.07.06: Support internvl2 series: internvl2-2b, internvl2-4b, internvl2-8b, internvl2-26b. 2024.07.06: Support codegeex4-9b-chat. 2024.07.04: Support internlm2_5-7b series: internlm2_5-7b, internlm2_5-7b-chat, internlm2_5-7b-chat-1m. 2024.07.02: Support for using vLLM for accelerating inference and deployment of multimodal large models such as the llava series and phi3-vision models. You can refer to the Multimodal & vLLM Inference Acceleration Documentation for more information. 2024.07.02: Support for llava1_6-vicuna-7b-instruct, llava1_6-vicuna-13b-instruct and other llava-hf models. For best practices, refer to here. 🔥2024.06.29: Support eval-scope&open-compass for evaluation! Now we have supported over 50 eval datasets like BoolQ, ocnli, humaneval, math, ceval, mmlu, gsk8k, ARC_e, please check our Eval Doc to begin! Next sprint we will support Multi-modal and Agent evaluation, remember to follow us : ) 🔥2024.06.28: Support for Florence series model! See document 🔥2024.06.28: Support for Gemma2 series models: gemma2-9b, gemma2-9b-instruct, gemma2-27b, gemma2-27b-instruct. 🔥2024.06.18: Supports DeepSeek-Coder-v2 series model! Use model_type deepseek-coder-v2-instruct and deepseek-coder-v2-lite-instruct to begin. 🔥2024.06.16: Supports KTO and CPO training! See document to start training! 2024.06.11: Support for tool-calling agent deployment that conform to the OpenAI interface.You can refer to Agent deployment best practice 🔥2024.06.07: Support Qwen2 series LLM, including Base and Instruct models of 0.5B, 1.5B, 7B, and 72B, as well as corresponding quantized versions gptq-int4, gptq-int8, and awq-int4. The best practice for self-cognition fine-tuning, inference and deployment of Qwen2-72B-Instruct using dual-card 80GiB A100 can be found here. 🔥2024.06.05: Support for glm4 series LLM and glm4v-9b-chat MLLM. You can refer to glm4v best practice. 🔥2024.06.01: Supports 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. More 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! 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. 🛠️ InstallationSWIFT 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 StartedThis section introduces basic usage, see the Documentation section for more ways to use. Web-UIWeb-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) Supported Training Processes Training Process Training Method Pretraining Text Generation Fine-tuning Single-turn/Multi-turnAgent Training/Self-cognitionMulti-modal Vision/Multi-modal Speech Human Alignment DPOORPOSimPO Text-to-Image DreamBooth, etc. Text-to-Video - Single GPU TrainingStart 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 TrainingDeepspeed 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 # If multiple machines share a disk, please additionally specify `--save_on_each_node false`. # 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_type 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_type qwen1half-32b-chat \ --sft_type full \ --dataset blossom-math-zh \ --output_dir output \ --deepspeed default-zero3 \ AliYun-DLC multi-node trainingIn 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_type qwen1half-32b-chat \ --sft_type full \ --dataset blossom-math-zh \ --output_dir output \ --deepspeed default-zero3 InferenceOriginal 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 8192 EvaluationOriginal model: # We recommend using vLLM for acceleration (arc evaluated in half a minute) CUDA_VISIBLE_DEVICES=0 swift eval --model_type qwen1half-7b-chat \ --eval_dataset ARC_e --infer_backend vllmLoRA fine-tuned: CUDA_VISIBLE_DEVICES=0 swift eval --ckpt_dir xxx/checkpoint-xxx \ --eval_dataset ARC_e --infer_backend vllm \ --merge_lora true \ QuantizationOriginal 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 \ DeploymentThe 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 8192 Supported ModelsThe 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 QwenQwen1.5Qwen2 Tongyi Qwen 1.0 and 1.5 series models ChineseEnglish 0.5B-110Bincluding quantized versions base modelchat modelMoE modelcode model ChatGLM2ChatGLM3Codegeex2GLM4Codegeex4 Zhipu ChatGLM series models ChineseEnglish 6B-9B base modelchat modelcode modellong text model BaichuanBaichuan2 Baichuan 1 and Baichuan 2 ChineseEnglish 7B-13Bincluding quantized versions base modelchat model Yuan2 Langchao Yuan series models ChineseEnglish 2B-102B instruct model XVerse XVerse series models ChineseEnglish 7B-65B base modelchat modellong text modelMoE model LLaMA2 LLaMA2 series models English 7B-70Bincluding quantized versions base modelchat model LLaMA3 LLaMA3 series models English 8B-70Bincluding quantized versions base modelchat model MistralMixtral Mistral series models English 7B-22B base modelinstruct modelMoE model YiYi1.5 01AI's YI series models ChineseEnglish 6B-34Bincluding quantized base modelchat modellong text model InternLMInternLM2InternLM2-MathInternLM2.5 Pujiang AI Lab InternLM series models ChineseEnglish 1.8B-20B base modelchat modelmath model DeepSeekDeepSeek-MoEDeepSeek-CoderDeepSeek-MathDeepSeek-V2DeepSeek-Coder-V2 DeepSeek series models ChineseEnglish 1.3B-236B base modelchat modelMoE modelcode modelmath model MAMBA MAMBA temporal convolution model English 130M-2.8B base model GemmaGemma2 Google Gemma series models English 2B-27B base modelinstruct model MiniCPM OpenBmB MiniCPM series models ChineseEnglish 2B-3B chat modelMoE model OpenBuddy OpenBuddy series models ChineseEnglish 7B-70B base modelchat model Orion OrionStar AI series models ChineseEnglish 14B base modelchat model BlueLM VIVO BlueLM large model ChineseEnglish 7B base modelchat model Ziya2 Fengshenbang series models ChineseEnglish 13B base modelchat model Skywork Skywork series models ChineseEnglish 13B base modelchat 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 ChineseEnglish 34B chat model Tongyi-Finance Tongyi finance series models ChineseEnglish 14B base modelchat modelfinancial model CodeFuse-CodeLLaMACodeFuse-Codegeex2CodeFuse-Qwen Ant CodeFuse series models ChineseEnglish 6B-34B chat modelcode model phi2/phi3 Microsoft's PHI series models English 3B/4B base modelinstruct modelcode model Grok X-ai English 300B base model TeleChat Tele-AI ChineseEnglish 7B-12B chat model dbrx databricks English 132B base modelchat model mengzi3 Langboat ChineseEnglish 13B base model c4ai-command-r c4ai Multilingual 35B-104B chat model WizardLM2 WizardLM2 series models English 7B-8x22Bincluding quantized versions chat modelMoE model Atom Atom Chinese 7B base modelchat model Chinese-LLaMA-Alpaca-2 Chinese-LLaMA-Alpaca-2 Chinese 1.3B-13B base modelchat modellong text model Chinese-LLaMA-Alpaca-3 Chinese-LLaMA-Alpaca-3 Chinese 8B base modelchat 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 ChineseEnglish 7Bincluding quantized versions base modelchat model Qwen-Audio Tongyi Qwen speech model ChineseEnglish 7B base modelchat model YI-VL 01AI's YI series vision models ChineseEnglish 6B-34B chat model XComposer2 Pujiang AI Lab InternLM vision model ChineseEnglish 7B chat model DeepSeek-VL DeepSeek series vision models ChineseEnglish 1.3B-7B chat model MiniCPM-VMiniCPM-V-2MiniCPM-V-2_5 OpenBmB MiniCPM vision model ChineseEnglish 3B-9B chat model CogVLMCogVLM2CogAgentGLM4V Zhipu ChatGLM visual QA and Agent model ChineseEnglish 9B-19B chat model Llava1.5Llava1.6 Llava series models English 7B-34B chat model Llava-NextLlava-Next-Video Llava-Next series models ChineseEnglish 7B-110B chat model mPLUG-Owl mPLUG-Owl series models English 11B chat model InternVLMini-InternvlInternvl2 InternVL ChineseEnglish 2B-25.5Bincluding quantized version chat model Llava-llama3 xtuner English 8B chat model Phi3-Vision Microsoft English 4B chat model PaliGemma Google English 3B chat model Florence Microsoft English 0.23B-0.77B 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 Human Preference Alignment Training Documentation 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 Agent Deployment 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 🏛 LicenseThis 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} } ☎ Wechat GroupYou can contact us and communicate with us by adding our WeChat group:
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