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‎README.md‎

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@@ -47,7 +47,7 @@ SWIFT has rich documentations for users, please check [here](https://github.com/
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SWIFT web-ui is available both on [Huggingface space](https://huggingface.co/spaces/tastelikefeet/swift) and [ModelScope studio](https://www.modelscope.cn/studios/iic/Scalable-lightWeight-Infrastructure-for-Fine-Tuning/summary), please feel free to try!
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## 🎉 News
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- 🔥2024.06.05: Support for **glm4** series LLM and glm4v-9b-chat MLLM. You can refer to [glm4v best practice](docs/source/Multi-Modal/glm4v最佳实践.md).
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- 🔥2024.06.05: Support for **glm4** series LLM and glm4v-9b-chat MLLM. You can refer to [glm4v best practice](docs/source_en/Multi-Modal/glm4v-best-practice.md).
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- 🔥2024.06.01: Supoprts **SimPO** training! See [document](https://github.com/modelscope/swift/blob/main/docs/source_en/LLM/SimPO.md) to start training!
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- 🔥2024.06.01: Support for deploying large multimodal models, please refer to the [Multimodal Deployment Documentation](docs/source_en/Multi-Modal/mutlimodal-deployment.md) for more information.
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- 2024.05.31: Supports Mini-Internvl model, Use model_type `mini-internvl-chat-2b-v1_5` and `mini-internvl-chat-4b-v1_5`to train.
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- 2024.04.29: Supports inference and fine-tuning of InternVL-Chat-V1.5 model. For best practice, you can refer to [here](https://github.com/modelscope/swift/tree/main/docs/source_en/Multi-Modal/internvl-best-practice.md).
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- 🔥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)!
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- 🔥2024.04.26: Support the fine-tuning and inference of Qwen1.5-110B and Qwen1.5-110B-Chat model, use [this script](https://github.com/modelscope/swift/blob/main/examples/pytorch/llm/scripts/qwen1half_110b_chat/lora_ddp_ds/sft.sh) to start training!
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<details><summary>More</summary>
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- 2024.04.24: Support for inference and fine-tuning of Phi3 series models. Including: [phi3-4b-4k-instruct](examples/pytorch/llm/scripts/phi3_4b_4k_instruct/lora), phi3-4b-128k-instruct.
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- 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.
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- 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.
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- 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](https://github.com/modelscope/swift/blob/main/examples/pytorch/llm/scripts/atom_7b_chat/lora/sft.sh) to train.
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- 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](docs/source_en/LLM/NPU-best-practice.md).
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- 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.
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<details><summary>More</summary>
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- 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.
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- 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).
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- 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.
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| Model Type | Model Introduction | Language | Model Size | Model Type |
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|------------------------------------------------|------------------------------------------------------------------------|--------------------|----------------------------------------|------------------------------------------- |
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| Qwen<br>Qwen1.5 | [Tongyi Qwen 1.0 and 1.5 series models](https://github.com/QwenLM) | Chinese<br>English | 0.5B-110B<br>including quantized versions | base model<br>chat model<br>MoE model<br>code model |
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| ChatGLM2<br>ChatGLM3<br>Codegeex2 | [Zhipu ChatGLM series models](https://github.com/THUDM) | Chinese<br>English | 6B | base model<br>chat model<br>code model<br>long text model |
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| Qwen<br>Qwen1.5 | [Tongyi Qwen 1.0 and 1.5 series models](https://github.com/QwenLM) | Chinese<br>English | 0.5B-110B<br>including quantized versions | base model<br>chat model<br>MoE model<br>code model |
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| ChatGLM2<br>ChatGLM3<br>Codegeex2<br>GLM4 | [Zhipu ChatGLM series models](https://github.com/THUDM) | Chinese<br>English | 6B-9B | base model<br>chat model<br>code model<br>long text model |
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| Baichuan/Baichuan2 | [Baichuan 1 and Baichuan 2](https://github.com/baichuan-inc) | Chinese<br>English | 7B-13B<br>including quantized versions | base model<br>chat model |
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| Yuan2 | [Langchao Yuan series models](https://github.com/IEIT-Yuan) | Chinese<br>English | 2B-102B | instruct model |
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| XVerse | [XVerse series models](https://github.com/xverse-ai) | Chinese<br>English | 7B-65B | base model<br>chat model<br>long text model<br>MoE model |
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| XComposer2 | [Pujiang AI Lab InternLM vision model](https://github.com/InternLM/InternLM) | Chinese<br>English | 7B | chat model |
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| DeepSeek-VL | [DeepSeek series vision models](https://github.com/deepseek-ai) | Chinese<br>English | 1.3B-7B | chat model |
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| MiniCPM-V<br>MiniCPM-V-2<br>MiniCPM-V-2_5 | [OpenBmB MiniCPM vision model](https://github.com/OpenBMB/MiniCPM) | Chinese<br>English | 3B-9B | chat model |
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| CogVLM<br>CogVLM2<br>CogAgent | [Zhipu ChatGLM visual QA and Agent model](https://github.com/THUDM/) | Chinese<br>English | 17B-19B | chat model |
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| CogVLM<br>CogVLM2<br>CogAgent<br>GLM4V | [Zhipu ChatGLM visual QA and Agent model](https://github.com/THUDM/) | Chinese<br>English | 9B-19B | chat model |
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| Llava | [Llava series models](https://github.com/haotian-liu/LLaVA) | English | 7B-34B | chat model |
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| Llava-Next | [Llava-Next series models](https://github.com/LLaVA-VL/LLaVA-NeXT) | Chinese<br>English | 8B-110B | chat model |
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| mPLUG-Owl | [mPLUG-Owl series models](https://github.com/X-PLUG/mPLUG-Owl) | English | 11B | chat model |

‎README_CN.md‎

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- 2024.04.29: 支持InternVL-Chat-V1.5的推理与微调, 最佳实践可以查看[这里](https://github.com/modelscope/swift/tree/main/docs/source/Multi-Modal/internvl最佳实践.md).
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- 🔥2024.04.26: 支持**LISA** 和 **unsloth**训练！指定 `--lisa_activated_layers=2` 来开启LISA（显存使用降低至全参训练的30%），指定 `--tuner_backend unsloth` 来使用unsloth，用更少的显存（30%或更少）更快的速度（5x）训练一个超大模型！
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- 🔥2024.04.26: 支持Qwen1.5-110B和Qwen1.5-110B-Chat模型的推理与微调, 使用[这个脚本](https://github.com/modelscope/swift/blob/main/examples/pytorch/llm/scripts/qwen1half_110b_chat/lora_ddp_ds/sft.sh)来开始训练！
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<details><summary>更多</summary>
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- 2024.04.24: 支持Phi3系列模型的推理与微调. 包括: [phi3-4b-4k-instruct](examples/pytorch/llm/scripts/phi3_4b_4k_instruct/lora), phi3-4b-128k-instruct.
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- 2024.04.22: 支持**chinese-llama-alpaca-2**系列模型的推理与微调和部署等. 包括：chinese-llama-2-1.3b, chinese-llama-2-7b, chinese-llama-2-13b, chinese-alpaca-2-1.3b, chinese-alpaca-2-7b和chinese-alpaca-2-13b以及对应的16k和64k长文本模型.
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- 2024.04.22: 支持Llama3 GPTQ-Int4, GPTQ-Int8, AWQ系列模型的推理与微调. 支持chatglm3-6b-128k, Openbuddy-llama3的推理与微调.
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- 2024.04.20: 支持**Atom**系列模型的推理, 微调和部署等. 包括: Atom-7B and Atom-7B-Chat. 使用[这个脚本](https://github.com/modelscope/swift/blob/main/examples/pytorch/llm/scripts/atom_7b_chat/lora/sft.sh)来开始训练！
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- 2024.04.19: 支持NPU的单卡、DDP、ZeRO2和ZeRO3的训练与推理, 可以查看[NPU推理与微调最佳实践](docs/source/LLM/NPU推理与微调最佳实践.md).
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- 2024.04.19: 支持**Llama3**系列模型的推理, 微调和部署等. 包括: Llama-3-8B, Llama-3-8B-Instruct, Llama-3-70B, Llama-3-70B-Instruct. 使用[这个脚本](https://github.com/modelscope/swift/blob/main/examples/pytorch/llm/scripts/llama3_8b_instruct/lora/sft.sh)开始训练叭！
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<details><summary>更多</summary>
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- 2024.04.18: 支持模型: wizardlm2-7b-awq, wizardlm2-8x22b, yi-6b-chat-awq, yi-6b-chat-int8, yi-34b-chat-awq, yi-34b-chat-int8. 支持`--deepspeed zero3-offload`, 提供了默认zero3-offload配置文件来使用zero3+cpu offload.
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- 2024.04.18: 支持使用环境变量`USE_HF`兼容HuggingFace生态, 切换成使用HF中的模型和数据集, 可以查看[HuggingFace生态兼容文档](https://github.com/modelscope/swift/tree/main/docs/source/LLM/HuggingFace生态兼容.md).
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- 2024.04.17: 支持OpenAI样式的接口评测, 可以查看[评测参数接口文档](docs/source/LLM/命令行参数.md#eval参数)来查看使用方法.
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| 模型类型 | 模型介绍 | 语言 | 模型大小 | 模型类型 |
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| --------------------------------------------------- | ------------------------------------------------------------ |----------| ------------------------- |-------------------------------------------|
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| Qwen<br>Qwen1.5 | [通义千问1.0和1.5系列模型](https://github.com/QwenLM) | 中文<br>英文 | 0.5B-110B<br>包含量化版本 | base模型<br>chat模型<br>MoE模型<br>代码模型 | |
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| ChatGLM2<br>ChatGLM3<br>Codegeex2 | [智谱ChatGLM系列模型](https://github.com/THUDM/) | 中文<br>英文 | 6B | base模型<br>chat模型<br>代码模型<br>长文本模型 |
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| Qwen<br>Qwen1.5 | [通义千问1.0和1.5系列模型](https://github.com/QwenLM) | 中文<br>英文 | 0.5B-110B<br>包含量化版本 | base模型<br>chat模型<br>MoE模型<br>代码模型 | |
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| ChatGLM2<br>ChatGLM3<br>Codegeex2<br>GLM4 | [智谱ChatGLM系列模型](https://github.com/THUDM/) | 中文<br>英文 | 6B-9B | base模型<br>chat模型<br>代码模型<br>长文本模型 |
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| Baichuan<br>Baichuan2 | [百川1和百川2](https://github.com/baichuan-inc) | 中文<br>英文 | 7B-13B<br>包含量化版本 | base模型<br>chat模型 |
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| Yuan2 | [浪潮源系列模型](https://github.com/IEIT-Yuan) | 中文<br>英文 | 2B-102B | instruct模型 |
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| XVerse | [元象系列模型](https://github.com/xverse-ai) | 中文<br>英文 | 7B-65B | base模型<br>chat模型<br>长文本模型<br>MoE模型 | |
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| XComposer2 | [浦江实验室书生浦语视觉模型](https://github.com/InternLM/InternLM) | 中文<br>英文 | 7B | chat模型 |
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| DeepSeek-VL | [幻方系列视觉模型](https://github.com/deepseek-ai) | 中文<br>英文 | 1.3B-7B | chat模型 |
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| MiniCPM-V<br>MiniCPM-V-2<br>MiniCPM-V-2_5 | [OpenBmB MiniCPM视觉模型](https://github.com/OpenBMB/MiniCPM) | 中文<br>英文 | 3B-9B | chat模型 |
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| CogVLM<br>CogVLM2<br>CogAgent | [智谱ChatGLM视觉问答和Agent模型](https://github.com/THUDM/) | 中文<br>英文 | 17B-19B | chat模型 |
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| CogVLM<br>CogVLM2<br>CogAgent<br>GLM4V | [智谱ChatGLM视觉问答和Agent模型](https://github.com/THUDM/) | 中文<br>英文 | 9B-19B | chat模型 |
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| Llava | [Llava系列模型](https://github.com/haotian-liu/LLaVA) | 英文 | 7B-34B | chat模型 |
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| Llava-Next | [Llava-Next系列模型](https://github.com/LLaVA-VL/LLaVA-NeXT) | 中文<br>英文 | 8B-110B | chat模型 |
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| mPLUG-Owl | [mPLUG-Owl系列模型](https://github.com/X-PLUG/mPLUG-Owl) | 英文 | 11B | chat模型 |

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