README.md · 熊文逸/swift

# SWIFT (Scalable lightWeight Infrastructure for Fine-Tuning)

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<a href="https://modelscope.cn/home">ModelScope Community Website</a>
 
 <a href="README_CN.md">中文</a> &nbsp ｜ &nbsp English &nbsp

## 📖 Table of Contents
- [Introduction](#-introduction)
- [News](#-news)
- [Installation](#%EF%B8%8F-installation)
- [Getting Started](#-getting-started)
- [Documentation](#-documentation)
- [License](#-License)
- [Citation](#-citation)
- [Contact Us](#-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](https://github.com/huggingface/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
- 2024.04.19: Support for single-card, DDP, ZeRO2, and ZeRO3 training and inference with NPU, please refer to [NPU Inference and Fine-tuning Best Practices](docs/source_en/LLM/NPU-best-practice.md).
- 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](https://github.com/modelscope/swift/blob/main/examples/pytorch/llm/scripts/llama3_8b_instruct/lora/sft.sh) 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](https://github.com/modelscope/swift/tree/main/docs/source_en/LLM/Compat-HF.md).
- 2024.04.17: Support the evaluation for OpenAI standard interfaces. Check the [parameter documentation](docs/source_en/LLM/Command-line-parameters.md#eval-parameters) 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](https://github.com/modelscope/swift/blob/main/examples/pytorch/llm/scripts/codeqwen1half_7b_chat/lora/sft.sh) to train.
- 2024.04.16: Supports inference and fine-tuning of llava-v1.6-34b model. For best practice, you can refer to [here](https://github.com/modelscope/swift/tree/main/docs/source_en/Multi-Modal/llava-best-practice.md).
- 2024.04.13: Support the fine-tuning and inference of Mixtral-8x22B-v0.1 model, use [this script](https://github.com/modelscope/swift/blob/main/examples/pytorch/llm/scripts/mixtral_moe_8x22b_v1/lora_ddp_ds/sft.sh) 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](https://github.com/modelscope/swift/blob/main/examples/pytorch/llm/scripts/minicpm_moe_8x2b/lora_ddp/sft.sh) 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](docs/source_en/LLM/LLM-eval.md) for details. Meanwhile, we support a trick way to do multiple ablation experiments, check [this documentation](docs/source_en/LLM/LLM-exp.md) to use.
- 🔥2024.04.11: Support **c4ai-command-r** series: c4ai-command-r-plus, c4ai-command-r-v01, use [this script](https://github.com/modelscope/swift/blob/main/examples/pytorch/llm/scripts/c4ai_command_r_plus/lora_mp/sft.sh) 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](https://github.com/modelscope/modelscope-agent) for best practices, which can be found [here](https://github.com/modelscope/swift/tree/main/docs/source_en/LLM/Agent-best-practice.md#Usage-with-Modelscope_Agent).
- 🔥2024.04.09: Support ruozhiba dataset. Search `ruozhiba` in [this documentation](docs/source_en/LLM/Supported-models-datasets.md) to begin training!
- 2024.04.08: Support the fine-tuning and inference of XVERSE-MoE-A4.2B model, use [this script](https://github.com/modelscope/swift/blob/main/examples/pytorch/llm/scripts/xverse_moe_a4_2b/lora/sft.sh) to start training!
- 2024.04.04: Support **QLoRA+FSDP** to train a 70B model with two 24G memory GPUs, use [this script](https://github.com/modelscope/swift/blob/main/examples/pytorch/llm/scripts/llama2_70b_chat/qlora_fsdp/sft.sh) 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](https://github.com/modelscope/swift/blob/main/examples/pytorch/llm/scripts/qwen1half_32b_chat/lora_mp/sft.sh) to start training!
- 🔥2024.04.02: Support the fine-tuning and inference of Mengzi3-13B-Base model, use [this script](https://github.com/modelscope/swift/blob/main/examples/pytorch/llm/scripts/mengzi3_13b_base/lora_ddp_ds/sft.sh) to start training!
- 🔥2024.04.01: Support **dbrx** series: dbrx-base and dbrx-instruct, use [this script](https://github.com/modelscope/swift/blob/main/examples/pytorch/llm/scripts/dbrx-instruct/lora_mp/sft.sh) 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](https://github.com/modelscope/swift/tree/main/docs/source_en/LLM/Grok-1-best-practice.md).
- 🔥2024.03.25: Supports inference and fine-tuning of TeleChat-7b and TeleChat-12b model, use [this script](https://github.com/modelscope/swift/blob/main/examples/pytorch/llm/scripts/telechat_12b/lora/sft.sh) to start training!
- 🔥2024.03.20: Supports inference and fine-tuning for the **llava** series. For best practice, you can refer to [here](https://github.com/modelscope/swift/tree/main/docs/source_en/Multi-Modal/llava-best-practice.md).
<details><summary>More</summary>

- 🔥2024.03.12: Support inference and fine-tuning for **deepseek-vl** series. Best practices can be found [here](docs/source_en/Multi-Modal/deepseek-vl-best-practice.md).
- 🔥2024.03.11: Support [GaLore](https://arxiv.org/abs/2403.03507) for effectively reducing memory usage to 1/2 of the original in full-parameter training.
- 🔥2024.03.10: [End-to-end best practices](docs/source_en/LLM/Qwen1.5-best-practice.md) 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](https://github.com/modelscope/swift/blob/main/examples/pytorch/llm/scripts/mamba-1.4b/lora/sft.sh) to start training!
- 2024.03.09: Support training and inference of AQLM quantized model, use [this script](https://github.com/modelscope/swift/blob/main/examples/pytorch/llm/scripts/llama2_7b_aqlm_2bit_1x16/lora/sft.sh) to start training!
- 2024.03.06: Support training and inference of AWQ quantized model, use [this Qwen1.5-AWQ model script](https://github.com/modelscope/swift/blob/main/examples/pytorch/llm/scripts/qwen1half_7b_chat_awq/lora/sft.sh) to start training, and support training and inference of [yi-9b](https://github.com/modelscope/swift/blob/main/examples/pytorch/llm/scripts/yi_9b/lora_zero3).
- 🔥2024.02.29: Support [LLaMA PRO](https://arxiv.org/pdf/2401.02415.pdf), simply use [this script](https://github.com/modelscope/swift/blob/main/examples/pytorch/llm/scripts/yi_6b_chat/llamapro/sft.sh) to start training.
- 🔥2024.02.29: Support [LoRA+](https://arxiv.org/pdf/2402.12354.pdf), simply use [this script](https://github.com/modelscope/swift/blob/main/examples/pytorch/llm/scripts/yi_6b_chat/lorap/sft.sh) to start training.
- 2024.02.25: Support `swift export` to quantize models using **AWQ/GPTQ** and push to ModelScope Hub. See documentation: [LLM Quantization](docs/source_en/LLM/LLM-quantization.md).
- 2024.02.22: Support gemma series: gemma-2b, [gemma-2b-instruct](https://github.com/modelscope/swift/tree/main/examples/pytorch/llm/scripts/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](https://github.com/modelscope/swift/blob/main/docs/source/LLM/%E6%94%AF%E6%8C%81%E7%9A%84%E6%A8%A1%E5%9E%8B%E5%92%8C%E6%95%B0%E6%8D%AE%E9%9B%86.md#%E6%A8%A1%E5%9E%8B) for all supported Qwen1.5 models. Provide fine-tuning scripts for [qwen1half-7b-chat](https://github.com/modelscope/swift/tree/main/examples/pytorch/llm/scripts/qwen1half_7b_chat), [qwen1half-7b-chat-int8](https://github.com/modelscope/swift/tree/main/examples/pytorch/llm/scripts/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](https://github.com/modelscope/swift/tree/main/examples/pytorch/sdxl/scripts) for details.
- 2024.02.01: Support minicpm series: [minicpm-2b-sft-chat](https://github.com/modelscope/swift/tree/main/examples/pytorch/llm/scripts/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](https://www.modelscope.cn/datasets/iic/ms_bench/summary).
- 🔥2024.02.01: Support Agent training! Agent training algorithm is derived from this [paper](https://arxiv.org/pdf/2309.00986.pdf). We also added [ms-agent](https://www.modelscope.cn/datasets/iic/ms_agent/summary), a high-quality agent dataset. Use [this script](https://github.com/modelscope/swift/blob/main/examples/pytorch/llm/scripts/qwen_7b_chat/lora/sft.sh) 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](https://github.com/modelscope/swift/tree/main/examples/pytorch/llm/scripts/internlm_xcomposer2_7b_chat).
- 🔥2024.01.30: Support [ZeRO-3](https://github.com/modelscope/swift/tree/main/examples/pytorch/llm/scripts/qwen_14b_chat/full_ddp_zero3/), 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](https://github.com/modelscope/swift/tree/main/examples/pytorch/llm/scripts/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](https://github.com/modelscope/swift/tree/main/examples/pytorch/llm/scripts/orion_14b_chat).
- 2024.01.20: Support [xverse-13b-256k](https://github.com/modelscope/swift/tree/main/examples/pytorch/llm/scripts/xverse_13b_256k), xverse-65b-v2, xverse-65b-chat.
- 🔥2024.01.17: Support internlm2 series: internlm2-7b-base, internlm2-7b, [internlm2-7b-sft-chat](https://github.com/modelscope/swift/tree/main/examples/pytorch/llm/scripts/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](https://github.com/modelscope/swift/tree/main/examples/pytorch/llm/scripts/yuan2_2b_janus_instruct), yuan2-51b-instruct, yuan2-102b-instruct.
- 🔥2024.01.12: Support **deepseek-moe** series: deepseek-moe-16b, [deepseek-moe-16b-chat](https://github.com/modelscope/swift/tree/main/examples/pytorch/llm/scripts/deepseek_moe_16b_chat).
- 🔥2024.01.04: Support **VLLM deployment**, compatible with **OpenAI API** style, see [VLLM Inference Acceleration and Deployment](https://github.com/modelscope/swift/blob/main/docs/source_en/LLM/VLLM-inference-acceleration-and-deployment.md#Deployment) for details.

- 2024.01.04: Update [Benchmark](https://github.com/modelscope/swift/blob/main/docs/source/LLM/Benchmark.md) 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](https://github.com/modelscope/swift/blob/main/docs/source/LLM/LLM%E4%BA%BA%E7%B1%BB%E5%AF%B9%E9%BD%90%E8%AE%AD%E7%BB%83%E6%96%87%E6%A1%A3.md) 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](https://github.com/modelscope/swift/tree/main/examples/pytorch/llm/scripts/codegeex2_6b).
- 2023.12.19: Support [phi2-3b](https://github.com/modelscope/swift/tree/main/examples/pytorch/llm/scripts/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](https://github.com/modelscope/swift/tree/main/examples/pytorch/llm/scripts/mixtral_7b_moe), [mixtral-moe-7b-instruct](https://github.com/modelscope/swift/tree/main/examples/pytorch/llm/scripts/mixtral_7b_moe_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](https://github.com/modelscope/swift/tree/main/examples/pytorch/llm/scripts/qwen_7b_chat/full_freeze_ddp). Support `disable_tqdm`, `lazy_tokenize`, `preprocess_num_proc` parameters, see [command line arguments](https://github.com/modelscope/swift/blob/main/docs/source/LLM/%E5%91%BD%E4%BB%A4%E8%A1%8C%E5%8F%82%E6%95%B0.md) for details.
- 2023.12.08: Support [sus-34b-chat](https://github.com/modelscope/swift/tree/main/examples/pytorch/llm/scripts/sus_34b_chat), support yi-6b-200k, yi-34b-200k.
- 2023.12.07: Support [Multi-Node DDP training](https://github.com/modelscope/swift/blob/main/docs/source/LLM/LLM%E5%BE%AE%E8%B0%83%E6%96%87%E6%A1%A3.md#%E4%BD%BF%E7%94%A8cli).
- 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](docs/source_en/LLM/Self-cognition-best-practice.md), **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](https://github.com/modelscope/swift/tree/main/examples/pytorch/llm/scripts/qwen_1_8b_chat), [qwen_72b_chat](https://github.com/modelscope/swift/tree/main/examples/pytorch/llm/scripts/qwen_72b_chat), [qwen_audio_chat](https://github.com/modelscope/swift/tree/main/examples/pytorch/llm/scripts/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](https://github.com/modelscope/swift/tree/main/examples/pytorch/llm/scripts/yi_34b_chat), [codefuse_codellama_34b_chat](https://github.com/modelscope/swift/tree/main/examples/pytorch/llm/scripts/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](https://github.com/modelscope/swift/tree/main/examples/pytorch/llm/scripts/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](https://github.com/modelscope/swift/tree/main/examples/pytorch/llm/scripts/bluelm_7b_chat).
- 🔥2023.11.08: Support training and inference of **xverse-65b** model, script at [xverse_65b](https://github.com/modelscope/swift/tree/main/examples/pytorch/llm/scripts/xverse_65b).
- 🔥2023.11.07: Support training and inference of **yi-6b**, **yi-34b** models, scripts at [yi_6b](https://github.com/modelscope/swift/tree/main/examples/pytorch/llm/scripts/yi_6b), [yi_34b](https://github.com/modelscope/swift/tree/main/examples/pytorch/llm/scripts/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](https://github.com/modelscope/swift/tree/main/examples/pytorch/llm/scripts/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](https://github.com/modelscope/swift/tree/main/examples/pytorch/llm/scripts/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.
</details>

## 🛠️ Installation

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

- Method 1: Install SWIFT using pip command:

```shell
# 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:

```shell
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

```shell
# 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](#-documentation) section for more ways to use.

### Web-UI

```shell
swift web-ui
```

### Training

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

- full: [qwen1half-7b-chat](https://github.com/modelscope/swift/tree/main/examples/pytorch/llm/scripts/qwen1half_7b_chat/full) (A100), [qwen-7b-chat](https://github.com/modelscope/swift/tree/main/examples/pytorch/llm/scripts/qwen_7b_chat/full_mp) (2\*A100)
- full+ddp+zero2: [qwen-7b-chat](https://github.com/modelscope/swift/tree/main/examples/pytorch/llm/scripts/qwen_7b_chat/full_ddp_zero2) (4\*A100)
- full+ddp+zero3: [qwen-14b-chat](https://github.com/modelscope/swift/tree/main/examples/pytorch/llm/scripts/qwen_14b_chat/full_ddp_zero3) (4\*A100)
- lora: [chatglm3-6b](https://github.com/modelscope/swift/tree/main/examples/pytorch/llm/scripts/chatglm3_6b/lora) (3090), [baichuan2-13b-chat](https://github.com/modelscope/swift/tree/main/examples/pytorch/llm/scripts/baichuan2_13b_chat/lora_mp) (2\*3090), [yi-34b-chat](https://github.com/modelscope/swift/tree/main/examples/pytorch/llm/scripts/yi_34b_chat/lora) (A100), [qwen-72b-chat](https://github.com/modelscope/swift/tree/main/examples/pytorch/llm/scripts/qwen_72b_chat/lora_mp) (2\*A100)
- lora+ddp: [chatglm3-6b](https://github.com/modelscope/swift/tree/main/examples/pytorch/llm/scripts/chatglm3_6b/lora_ddp) (2\*3090)
- lora+ddp+zero3: [qwen-14b-chat](https://github.com/modelscope/swift/tree/main/examples/pytorch/llm/scripts/qwen_14b_chat/lora_ddp_zero3) (4\*3090), [qwen-72b-chat](https://github.com/modelscope/swift/tree/main/examples/pytorch/llm/scripts/qwen_72b_chat/lora_ddp_zero3) (4\*A100)
- qlora(gptq-int4): [qwen-7b-chat-int4](https://github.com/modelscope/swift/tree/main/examples/pytorch/llm/scripts/qwen_7b_chat_int4/qlora) (3090)
- qlora(gptq-int8): [qwen1half-7b-chat-int8](https://github.com/modelscope/swift/tree/main/examples/pytorch/llm/scripts/qwen1half_7b_chat_int8/qlora) (3090)
- qlora(bnb-int4): [qwen-7b-chat](https://github.com/modelscope/swift/tree/main/examples/pytorch/llm/scripts/qwen_7b_chat/qlora) (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:
```shell
# 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:
```shell
# 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

```shell
# 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

```shell
# 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:
```shell
# 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:
```shell
# 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:
```shell
# 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:
```shell
# 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 \
```

### Inference
Original model:
```shell
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:
```shell
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

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

### Export

Original model:
```shell
CUDA_VISIBLE_DEVICES=0 swift export --model_type qwen1half-7b-chat \
    --quant_bits 4 --quant_method awq
```

LoRA fine-tuned:
```shell
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:
```shell
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:
```shell
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](https://github.com/QwenLM) | Chinese English | 0.5B-72B including quantized versions | base model chat model MoE model code model |
| ChatGLM2 ChatGLM3 Codegeex2 | [Zhipu ChatGLM series models](https://github.com/THUDM) | Chinese English | 6B | base model chat model code model |
| Baichuan/Baichuan2 | [Baichuan 1 and Baichuan 2](https://github.com/baichuan-inc) | Chinese English | 7B-13B including quantized versions | base model chat model |
| Yuan2 | [Langchao Yuan series models](https://github.com/IEIT-Yuan) | Chinese English | 2B-102B | instruct model |
| XVerse | [XVerse series models](https://github.com/xverse-ai) | Chinese English | 7B-65B | base model chat model long text model MoE model |
| LLaMA2 | [LLaMA2 series models](https://github.com/facebookresearch/llama) | English | 7B-70B including quantized versions | base model chat model |
| LLaMA3 | [LLaMA3 series models](https://github.com/meta-llama/llama3) | English | 8B-70B | base model chat model |
| Mistral Mixtral | [Mistral series models](https://github.com/mistralai/mistral-src) | English | 7B-22B | base model instruct model MoE model |
| YI | [01AI's YI series models](https://github.com/01-ai) | Chinese English | 6B-34B including quantized | base model chat model long text model |
| InternLM InternLM2 InternLM2-Math | [Pujiang AI Lab InternLM series models](https://github.com/InternLM/InternLM) | Chinese English | 1.8B-20B | base model chat model math model |
| DeepSeek DeepSeek-MoE DeepSeek-Coder DeepSeek-Math | [DeepSeek series models](https://github.com/deepseek-ai) | Chinese English | 1.3B-67B | base model chat model MoE model code model math model |
| MAMBA | [MAMBA temporal convolution model](https://github.com/state-spaces/mamba) | English | 130M-2.8B | base model |
| Gemma | [Google Gemma series models](https://github.com/google/gemma_pytorch) | English | 2B-7B | base model instruct model |
| MiniCPM | [OpenBmB MiniCPM series models](https://github.com/OpenBMB/MiniCPM) | Chinese English | 2B-3B | chat model MoE model |
| OpenBuddy | [OpenBuddy series models](https://github.com/OpenBuddy/OpenBuddy) | Chinese English | 7B-67B | base model chat model |
| Orion | [OrionStar AI series models](https://github.com/OrionStarAI) | Chinese English | 14B | base model chat model |
| BlueLM | [VIVO BlueLM large model](https://github.com/vivo-ai-lab/BlueLM) | Chinese English | 7B | base model chat model |
| Ziya2 | [Fengshenbang series models](https://github.com/IDEA-CCNL/Fengshenbang-LM) | Chinese English | 13B | base model chat model |
| Skywork | [Skywork series models](https://github.com/SkyworkAI/Skywork) | 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](https://github.com/DAMO-NLP-MT/PolyLM) | Multilingual | 13B | base model |
| SeqGPT | [Tongyi Lab self-developed text understanding model for information extraction and text classification](https://github.com/Alibaba-NLP/SeqGPT) | Chinese | 560M | semantic understanding model |
| SUS | [Southern University of Science and Technology model fine-tuned on YI](https://github.com/SUSTech-IDEA/SUS-Chat) | Chinese English | 34B | chat model |
| Tongyi-Finance | [Tongyi finance series models](https://github.com/QwenLM/Qwen) | Chinese English | 14B | base model chat model financial model |
| CodeFuse-CodeLLaMA CodeFuse-Codegeex2 CodeFuse-Qwen | [Ant CodeFuse series models](https://github.com/codefuse-ai) | Chinese English | 6B-34B | chat model code model |
| phi2 | Microsoft's PHI2 model | English | 3B | base model code model |
| Grok | [X-ai](https://github.com/xai-org/grok-1) | English | 300B | base model |
| TeleChat | [Tele-AI](https://github.com/Tele-AI/Telechat) | Chinese English | 7B-12B | chat model |
| dbrx | [databricks](https://github.com/databricks/dbrx) | English | 132B | base model chat model |
| mengzi3 | [Langboat](https://github.com/Langboat/Mengzi3) | Chinese English | 13B | base model |
| c4ai-command-r | [c4ai](https://cohere.com/command) | Multilingual | 35B-104B | chat model |
| WizardLM2 | [WizardLM2 series models](https://github.com/nlpxucan/WizardLM) | English | 7B-8x22B including quantized versions | chat model MoE model |

#### MLLMs

| Model Type | Model Introduction | Language | Model Size | Model Type |
|------------------|------------------------------------------------------------------------|--------------------|-------------------|------------------- |
| Qwen-VL | [Tongyi Qwen vision model](https://github.com/QwenLM) | Chinese English | 7B including quantized versions | base model chat model |
| Qwen-Audio | [Tongyi Qwen speech model](https://github.com/QwenLM) | Chinese English | 7B | base model chat model |
| YI-VL | [01AI's YI series vision models](https://github.com/01-ai) | Chinese English | 6B-34B | chat model |
| XComposer2 | [Pujiang AI Lab InternLM vision model](https://github.com/InternLM/InternLM) | Chinese English | 7B | chat model |
| DeepSeek-VL | [DeepSeek series vision models](https://github.com/deepseek-ai) | Chinese English | 1.3B-7B | chat model |
| MiniCPM-V | [OpenBmB MiniCPM vision model](https://github.com/OpenBMB/MiniCPM) | Chinese English | 3B | chat model |
| CogVLM CogAgent | [Zhipu ChatGLM visual QA and Agent model](https://github.com/THUDM/) | English | 17B-18B | chat model |
| Llava | [Llava series models](https://github.com/haotian-liu/LLaVA) | English | 7B-34B | chat model |
| mPLUG-Owl | [mPLUG-Owl series models](https://github.com/X-PLUG/mPLUG-Owl) | English | 11B | chat model |

#### Diffusion Models

| Model Type          | Model Introduction                                                    | Language | Model Type        |
|---------------------|----------------------------------------------------------------------|----------|------------------ |
| AnimateDiff         | [AnimateDiff animation model](https://github.com/guoyww/AnimateDiff) | English  | text-to-video     |
| SD1.5/SD2.0/SDXL    | [StabilityAI series diffusion models](https://github.com/Stability-AI) | English | text-to-image    |

### Supported Open Source Datasets

| Dataset Type | Training Task  | Documentation                                                                                                                                                                                                                                                                                                        |
|--------------|:---------------|--------------------------------------------------------------- |
| General      | Fine-tuning    | 🔥ruozhiba, 🔥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, ms-agent-for-agentfabric-default, ms-agent-for-agentfabric-addition, 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](https://arxiv.org/abs/2106.09685) |
| 🔥LoRA+: [LoRA+: Efficient Low Rank Adaptation of Large Models](https://arxiv.org/pdf/2402.12354.pdf) |
| 🔥LLaMA PRO: [LLAMA PRO: Progressive LLaMA with Block Expansion](https://arxiv.org/pdf/2401.02415.pdf) |
| 🔥SCEdit: [SCEdit: Efficient and Controllable Image Diffusion Generation via Skip Connection Editing](https://arxiv.org/abs/2312.11392) < [arXiv](https://arxiv.org/abs/2312.11392) \| [Project Page](https://scedit.github.io/) > |
| 🔥NEFTune: [Noisy Embeddings Improve Instruction Finetuning](https://arxiv.org/abs/2310.05914) |
| QA-LoRA:[Quantization-Aware Low-Rank Adaptation of Large Language Models](https://arxiv.org/abs/2309.14717) |
| LongLoRA: [Efficient Fine-tuning of Long-Context Large Language Models](https://arxiv.org/abs/2309.12307) |
| ROME: [Rank-One Editing of Encoder-Decoder Models](https://arxiv.org/abs/2211.13317) |
| Adapter: [Parameter-Efficient Transfer Learning for NLP](http://arxiv.org/abs/1902.00751) |
| Prompt Tuning: [Visual Prompt Tuning](https://arxiv.org/abs/2203.12119) |
| Side: [Side-Tuning: A Baseline for Network Adaptation via Additive Side Networks](https://arxiv.org/abs/1912.13503) |
| Res-Tuning: [Res-Tuning: A Flexible and Efficient Tuning Paradigm via Unbinding Tuner from Backbone](https://arxiv.org/abs/2310.19859) < [arXiv](https://arxiv.org/abs/2310.19859) \| [Project Page](https://res-tuning.github.io/) \| [Usage](docs/source/GetStarted/ResTuning.md) > |
| Tuners provided by [PEFT](https://github.com/huggingface/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

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

### User Guide

| Document Name                                                |
| ------------------------------------------------------------ |
| [Using Web-UI](docs/source_en/GetStarted/Web-ui.md)          |
| [Using Tuners](docs/source_en/GetStarted/Tuners.md)          |
| [LLM Inference](docs/source_en/LLM/LLM-inference.md)         |
| [LLM Fine-tuning](docs/source_en/LLM/LLM-fine-tuning.md)     |
| [LLM Evaluation](docs/source_en/LLM/LLM-eval.md)     |
| [LLM Quantization](docs/source_en/LLM/LLM-quantization.md)   |
| [LLM Deployment](docs/source_en/LLM/VLLM-inference-acceleration-and-deployment.md) |
| [DPO Human Alignment Training](docs/source_en/LLM/RLHF.md)   |
| [AnimateDiff Training](docs/source_en/AIGC/AnimateDiff-train-infer.md) |

### Reference Documentation
| Document Name                                                |
| ------------------------------------------------------------ |
| [Command Line Arguments](docs/source_en/LLM/Command-line-parameters.md) |
| [Customizing New Models and Datasets](docs/source_en/LLM/Customization.md) |
| [Supported Models and Datasets List](docs/source_en/LLM/Supported-models-datasets.md) |
| [Runtime Speed and Memory Benchmark](docs/source_en/LLM/Benchmark.md) |

### Best Practices

| Best Practices Name                                                |
| ------------------------------------------------------------ |
| [Agent Fine-Tuning Best Practice](docs/source_en/LLM/Agent-best-practice.md) |
| [Self-Cognition Fine-Tuning Best Practice](docs/source_en/LLM/Self-cognition-best-practice.md) |
|  [Qwen1.5 Best Practice](docs/source_en/LLM/Qwen1.5-best-practice.md) |
|  [Multi-Modal Model Training Best Practice](docs/source_en/Multi-Modal/index.md) |
|  [NPU Best Practice](docs/source_en/LLM/NPU-best-practice.md) |

### Deep Learning Tutorials

| Tutorial Name                                                |
|-------------------------------------------------------------- |
| [Introduction to Deep Learning](https://github.com/modelscope/modelscope-classroom/blob/main/LLM-tutorial/A.%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E5%85%A5%E9%97%A8%E4%BB%8B%E7%BB%8D.md) |
| [Large Model Basics](https://github.com/modelscope/modelscope-classroom/blob/main/LLM-tutorial/B.%E9%AD%94%E6%90%AD%E7%A4%BE%E5%8C%BA%E5%92%8CLLM%E5%A4%A7%E6%A8%A1%E5%9E%8B%E5%9F%BA%E7%A1%80%E7%9F%A5%E8%AF%86.md) |
| [Prompt Engineering](https://github.com/modelscope/modelscope-classroom/blob/main/LLM-tutorial/C.%E6%8F%90%E7%A4%BA%E8%AF%8D%E5%B7%A5%E7%A8%8B-prompt%20engineering.md) |
| [Transformer Architecture Introduction](https://github.com/modelscope/modelscope-classroom/blob/main/LLM-tutorial/D.Transformer%E7%BB%93%E6%9E%84.md) |
| [Training Technique Selection](https://github.com/modelscope/modelscope-classroom/blob/main/LLM-tutorial/E.%E6%8A%80%E6%9C%AF%E9%80%89%E5%9E%8B.md) |
| [Data Preprocessing](https://github.com/modelscope/modelscope-classroom/blob/main/LLM-tutorial/F.%E6%95%B0%E6%8D%AE%E9%A2%84%E5%A4%84%E7%90%86.md) |
| [Quantization](https://github.com/modelscope/modelscope-classroom/blob/main/LLM-tutorial/G.%E9%87%8F%E5%8C%96.md) |
| [Training](https://github.com/modelscope/modelscope-classroom/blob/main/LLM-tutorial/H.%E8%AE%AD%E7%BB%83.md) |
| [Inference](https://github.com/modelscope/modelscope-classroom/blob/main/LLM-tutorial/I.LLM%E5%92%8C%E5%A4%9A%E6%A8%A1%E6%80%81%E6%A8%A1%E5%9E%8B%E9%AB%98%E6%95%88%E6%8E%A8%E7%90%86%E5%AE%9E%E8%B7%B5.md) |
| [Deployment](https://github.com/modelscope/modelscope-classroom/blob/main/LLM-tutorial/J.%E9%83%A8%E7%BD%B2.md) |
| [Evaluation](https://github.com/modelscope/modelscope-classroom/blob/main/LLM-tutorial/K.%E5%A4%A7%E6%A8%A1%E5%9E%8B%E8%87%AA%E5%8A%A8%E8%AF%84%E4%BC%B0%E7%90%86%E8%AE%BA%E5%92%8C%E5%AE%9E%E6%88%98--LLM%20Automatic%20Evaluation.md) |

## 🏛 License

This framework is licensed under the [Apache License (Version 2.0)](https://github.com/modelscope/modelscope/blob/master/LICENSE). For models and datasets, please refer to the original resource page and follow the corresponding License.

## 📎 Citation

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

## ☎ Contact Us

You can contact us and communicate with us by adding our WeChat group:

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