Merge branch 'main' into release/2.0

Author: Jintao-Huang

README.md

@@ -39,6 +39,8 @@ To facilitate use by users unfamiliar with deep learning, we provide a Gradio we
 Additionally, we are expanding capabilities for other modalities. Currently, we support full-parameter training and LoRA training for AnimateDiff.
 
 ## 🎉 News
+- 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](https://github.com/modelscope/swift/blob/main/examples/pytorch/llm/scripts/atom_7b_chat/lora/sft.sh) 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 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.
@@ -441,7 +443,7 @@ CUDA_VISIBLE_DEVICES=0 swift deploy \
 | Yuan2                                          | [Langchao Yuan series models](https://github.com/IEIT-Yuan)             | Chinese<br>English    | 2B-102B                                | instruct model                                 |
 | 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                |
 | LLaMA2                                         | [LLaMA2 series models](https://github.com/facebookresearch/llama)       | English            | 7B-70B<br>including quantized versions   | base model<br>chat model                       |
-| LLaMA3                   | [LLaMA3 series models](https://github.com/meta-llama/llama3)  | English       | 8B-70B      | base model<br>chat model              |
+| LLaMA3                   | [LLaMA3 series models](https://github.com/meta-llama/llama3)  | English       | 8B-70B<br>including quantized versions      | base model<br>chat model              |
 | Mistral<br>Mixtral                            | [Mistral series models](https://github.com/mistralai/mistral-src)       | English            | 7B-22B     | base model<br>instruct model<br>MoE model                     |
 | YI                                             | [01AI's YI series models](https://github.com/01-ai)                     | Chinese<br>English    | 6B-34B<br>including quantized             | base model<br>chat model<br>long text model            |
 | InternLM<br>InternLM2<br>InternLM2-Math              | [Pujiang AI Lab InternLM series models](https://github.com/InternLM/InternLM) | Chinese<br>English | 1.8B-20B                            | base model<br>chat model<br>math model            |
@@ -468,6 +470,7 @@ CUDA_VISIBLE_DEVICES=0 swift deploy \
 | 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<br>including quantized versions | chat model<br>MoE model |
 | Atom | [Atom](https://github.com/LlamaFamily/Llama-Chinese) | Chinese | 7B| base model<br>chat model|
+| Chinese-LLaMA-Alpaca-2 | [Chinese-LLaMA-Alpaca-2](https://github.com/ymcui/Chinese-LLaMA-Alpaca-2) | Chinese | 1.3B-13B| base model<br>chat model<br>long text model|
 
 #### MLLMs
 
@@ -564,8 +567,8 @@ make docs
 | 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) |
+| [Customizing New Models and Datasets](docs/source_en/LLM/Customization.md) |
 | [Runtime Speed and Memory Benchmark](docs/source_en/LLM/Benchmark.md) |
 
 

README_CN.md

@@ -40,6 +40,8 @@ SWIFT支持近**200种LLM和MLLM**（多模态大模型）的训练、推理、
 此外，我们也在拓展其他模态的能力，目前我们支持了AnimateDiff的全参数训练和LoRA训练。
 
 ## 🎉 新闻
+- 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.
+- 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.
 - 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！
 - 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).
 - 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！
@@ -438,7 +440,7 @@ CUDA_VISIBLE_DEVICES=0 swift deploy \
 | Yuan2                                               | [浪潮源系列模型](https://github.com/IEIT-Yuan)               | 中文<br>英文 | 2B-102B                   | instruct模型                                |
 | 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             |                |
 | LLaMA2                                              | [LLaMA2系列模型](https://github.com/facebookresearch/llama)  | 英文       | 7B-70B<br>包含量化版本      | base模型<br>chat模型                          |
-| LLaMA3               | [LLaMA3系列模型](https://github.com/meta-llama/llama3)  | 英文       | 8B-70B      | base模型<br>chat模型              |
+| LLaMA3               | [LLaMA3系列模型](https://github.com/meta-llama/llama3)  | 英文       | 8B-70B<br>包含量化版本      | base模型<br>chat模型              |
 | Mistral<br>Mixtral                                 | [Mistral系列模型](https://github.com/mistralai/mistral-src)  | 英文       | 7B-8x22B | base模型<br>instruct模型<br>MoE模型             |
 | YI                                                  | [01AI的YI系列模型](https://github.com/01-ai)                 | 中文<br>英文 | 6B-34B<br>包含量化版本          | base模型<br>chat模型<br>长文本模型                 |
 | InternLM<br>InternLM2<br>InternLM2-Math                   | [浦江实验室书生浦语系列模型](https://github.com/InternLM/InternLM) | 中文<br>英文 | 1.8B-20B                  | base模型<br>chat模型<br>数学模型                  |
@@ -465,6 +467,7 @@ CUDA_VISIBLE_DEVICES=0 swift deploy \
 | c4ai-command-r | [c4ai](https://cohere.com/command) | 多语种 | 35B-104B | chat模型  |
 | WizardLM2 | [WizardLM2系列模型](https://github.com/nlpxucan/WizardLM) | 多语种 | 7B-8x22B<br>包含量化版本 | chat模型<br>MoE模型 |
 | Atom | [Atom](https://github.com/LlamaFamily/Llama-Chinese) | 中文 | 7B| base模型<br>chat模型|
+| Chinese-LLaMA-Alpaca-2 | [Chinese-LLaMA-Alpaca-2](https://github.com/ymcui/Chinese-LLaMA-Alpaca-2) | 中文 | 1.3B-13B| base模型<br>chat模型<br>长文本模型|
 
 
 #### 多模态大模型
@@ -564,8 +567,8 @@ make docs
 | 文档名称                                                     |
 | ------------------------------------------------------------ |
 | [命令行参数](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) |
-| [自定义新模型和数据集](https://github.com/modelscope/swift/blob/main/docs/source/LLM/%E8%87%AA%E5%AE%9A%E4%B9%89%E4%B8%8E%E6%8B%93%E5%B1%95.md) |
 | [支持的模型和数据集列表](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) |
+| [自定义新模型和数据集](https://github.com/modelscope/swift/blob/main/docs/source/LLM/%E8%87%AA%E5%AE%9A%E4%B9%89%E4%B8%8E%E6%8B%93%E5%B1%95.md) |
 | [运行速度与显存Benchmark](https://github.com/modelscope/swift/blob/main/docs/source/LLM/Benchmark.md) |
 | [HuggingFace生态兼容](https://github.com/modelscope/swift/blob/main/docs/source/LLM/HuggingFace%E7%94%9F%E6%80%81%E5%85%BC%E5%AE%B9.md) |
 

docs/source/LLM/Agent微调最佳实践.md

@@ -440,7 +440,7 @@ print()
 
 
 ### 微调
-将`dataset`换为`ms-agent-for-agentfabric`和`ms-agent-for-agentfabric-default`
+将`dataset`换为`ms-agent-for-agentfabric-default`和`ms-agent-for-agentfabric-addition`
 ```shell
 # Experimental environment: 8GPU
 nproc_per_node=8

docs/source/LLM/LLM量化文档.md

@@ -14,7 +14,9 @@ GPU设备: A10, 3090, V100, A100均可.
 # 设置pip全局镜像 (加速下载)
 pip config set global.index-url https://mirrors.aliyun.com/pypi/simple/
 # 安装ms-swift
-pip install 'ms-swift[llm]' -U
+git clone https://github.com/modelscope/swift.git
+cd swift
+pip install -e '.[llm]'
 
 # 使用awq量化:
 # autoawq和cuda版本有对应关系，请按照`https://github.com/casper-hansen/AutoAWQ`选择版本
@@ -209,6 +211,14 @@ curl http://localhost:8000/v1/chat/completions \
 假设你使用lora微调了qwen1half-4b-chat, 模型权重目录为: `output/qwen1half-4b-chat/vx-xxx/checkpoint-xxx`.
 
 ```shell
+# 推送原始量化模型
+CUDA_VISIBLE_DEVICES=0 swift export \
+    --model_type qwen1half-7b-chat \
+    --model_id_or_path qwen1half-7b-chat-gptq-int4 \
+    --push_to_hub true \
+    --hub_model_id qwen1half-7b-chat-gptq-int4 \
+    --hub_token '<your-sdk-token>'
+
 # 推送lora增量模型
 CUDA_VISIBLE_DEVICES=0 swift export \
     --ckpt_dir output/qwen1half-4b-chat/vx-xxx/checkpoint-xxx \