Merge branch 'main' into release/2.0

Author: Jintao-Huang

README.md

@@ -39,6 +39,7 @@ 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
+- 2024.04.24: 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.
 - 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.
@@ -433,7 +434,7 @@ CUDA_VISIBLE_DEVICES=0 swift deploy \
 ```
 
 ### Supported Models
-The complete list of supported models and datasets can be found at [Supported Models and Datasets List](https://idealab.alibaba-inc.com/docs/source/LLM/Supported-Models-and-Datasets.md).
+The complete list of supported models and datasets can be found at [Supported Models and Datasets List](docs/source_en/LLM/Supported-models-datasets.md).
 
 #### LLMs
 
@@ -464,7 +465,7 @@ The complete list of supported models and datasets can be found at [Supported Mo
 | SUS                                            | [Southern University of Science and Technology model fine-tuned on YI](https://github.com/SUSTech-IDEA/SUS-Chat) | Chinese<br>English | 34B                              | chat model                                 |
 | Tongyi-Finance                                 | [Tongyi finance series models](https://github.com/QwenLM/Qwen)          | Chinese<br>English    | 14B                                    | base model<br>chat model<br>financial model        |
 | CodeFuse-CodeLLaMA<br>CodeFuse-Codegeex2<br>CodeFuse-Qwen | [Ant CodeFuse series models](https://github.com/codefuse-ai)        | Chinese<br>English    | 6B-34B                                 | chat model<br>code model                      |
-| phi2                                           | Microsoft's PHI2 model                                                 | English            | 3B                                     | base model<br>code model                          |
+| phi2/phi3                                | Microsoft's PHI series models    | English            | 3B/4B                               | base model<br>instruct model<br>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<br>English | 7B-12B | chat model |
 | dbrx | [databricks](https://github.com/databricks/dbrx) | English | 132B | base model<br>chat model  |

README_CN.md

@@ -40,6 +40,7 @@ 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
+- 2024.04.24: 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.
 - 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！
@@ -461,7 +462,7 @@ CUDA_VISIBLE_DEVICES=0 swift deploy \
 | SUS                                                 | [南方科技大学基于YI Fine-Tune的模型](https://github.com/SUSTech-IDEA/SUS-Chat) | 中文<br>英文 | 34B                       | chat模型                                    |
 | Tongyi-Finance                                      | [通义金融系列模型](https://github.com/QwenLM/Qwen)           | 中文<br>英文 | 14B                       | base模型<br>chat模型<br>金融模型                  |
 | CodeFuse-CodeLLaMA<br>CodeFuse-Codegeex2<br>CodeFuse-Qwen | [蚂蚁CodeFuse系列模型](https://github.com/codefuse-ai)       | 中文<br>英文 | 6B-34B                    | chat模型<br>代码模型                            |
-| phi2                           | 微软PHI2模型                                                 | 英文       | 3B                        | base模型<br>代码模型                            |
+| phi2/phi3                         | 微软PHI2模型                                                 | 英文       | 3B/4B                 | base模型<br>指令模型<br>代码模型               |
 | Grok | [X-ai](https://github.com/xai-org/grok-1) | 英文       | 300B | base模型                                    |
 | TeleChat | [Tele-AI](https://github.com/Tele-AI/Telechat) | 中文<br>英文 | 7B-12B | chat模型                                    |
 | dbrx | [databricks](https://github.com/databricks/dbrx) | 英文 | 132B | base模型<br>chat模型  |

docs/source/LLM/NPU推理与微调最佳实践.md

@@ -1,4 +1,5 @@
 # NPU训练最佳实践
+作者: [chuanzhubin](https://github.com/chuanzhubin), [jintao](https://github.com/Jintao-Huang)
 
 ## 目录
 - [环境准备](#环境准备)

docs/source/LLM/支持的模型和数据集.md

@@ -93,14 +93,14 @@
 |llama2-7b-aqlm-2bit-1x16|[AI-ModelScope/Llama-2-7b-AQLM-2Bit-1x16-hf](https://modelscope.cn/models/AI-ModelScope/Llama-2-7b-AQLM-2Bit-1x16-hf/summary)|q_proj, k_proj, v_proj|default-generation-bos|✔|✘|transformers>=4.38, aqlm, torch>=2.2.0|-|[ISTA-DASLab/Llama-2-7b-AQLM-2Bit-1x16-hf](https://huggingface.co/ISTA-DASLab/Llama-2-7b-AQLM-2Bit-1x16-hf)|
 |llama3-8b|[LLM-Research/Meta-Llama-3-8B](https://modelscope.cn/models/LLM-Research/Meta-Llama-3-8B/summary)|q_proj, k_proj, v_proj|default-generation|✔|✔||-|[meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B)|
 |llama3-8b-instruct|[LLM-Research/Meta-Llama-3-8B-Instruct](https://modelscope.cn/models/LLM-Research/Meta-Llama-3-8B-Instruct/summary)|q_proj, k_proj, v_proj|llama3|✔|✔||-|[meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct)|
-|llama3-8b-instruct-int4|[huangjintao/Meta-Llama-3-8B-Instruct-GPTQ-Int4](https://modelscope.cn/models/huangjintao/Meta-Llama-3-8B-Instruct-GPTQ-Int4/summary)|q_proj, k_proj, v_proj|llama3|✔|✔|auto_gptq|-|-|
-|llama3-8b-instruct-int8|[huangjintao/Meta-Llama-3-8B-Instruct-GPTQ-Int8](https://modelscope.cn/models/huangjintao/Meta-Llama-3-8B-Instruct-GPTQ-Int8/summary)|q_proj, k_proj, v_proj|llama3|✔|✔|auto_gptq|-|-|
-|llama3-8b-instruct-awq|[huangjintao/Meta-Llama-3-8B-Instruct-AWQ](https://modelscope.cn/models/huangjintao/Meta-Llama-3-8B-Instruct-AWQ/summary)|q_proj, k_proj, v_proj|llama3|✔|✔|autoawq|-|-|
+|llama3-8b-instruct-int4|[huangjintao/Meta-Llama-3-8B-Instruct-GPTQ-Int4](https://modelscope.cn/models/huangjintao/Meta-Llama-3-8B-Instruct-GPTQ-Int4/summary)|q_proj, k_proj, v_proj|llama3|✔|✔|auto_gptq|-|[study-hjt/Meta-Llama-3-8B-Instruct-GPTQ-Int4](https://huggingface.co/study-hjt/Meta-Llama-3-8B-Instruct-GPTQ-Int4)|
+|llama3-8b-instruct-int8|[huangjintao/Meta-Llama-3-8B-Instruct-GPTQ-Int8](https://modelscope.cn/models/huangjintao/Meta-Llama-3-8B-Instruct-GPTQ-Int8/summary)|q_proj, k_proj, v_proj|llama3|✔|✔|auto_gptq|-|[study-hjt/Meta-Llama-3-8B-Instruct-GPTQ-Int8](https://huggingface.co/study-hjt/Meta-Llama-3-8B-Instruct-GPTQ-Int8)|
+|llama3-8b-instruct-awq|[huangjintao/Meta-Llama-3-8B-Instruct-AWQ](https://modelscope.cn/models/huangjintao/Meta-Llama-3-8B-Instruct-AWQ/summary)|q_proj, k_proj, v_proj|llama3|✔|✔|autoawq|-|[study-hjt/Meta-Llama-3-8B-Instruct-AWQ](https://huggingface.co/study-hjt/Meta-Llama-3-8B-Instruct-AWQ)|
 |llama3-70b|[LLM-Research/Meta-Llama-3-70B](https://modelscope.cn/models/LLM-Research/Meta-Llama-3-70B/summary)|q_proj, k_proj, v_proj|default-generation|✔|✔||-|[meta-llama/Meta-Llama-3-70B](https://huggingface.co/meta-llama/Meta-Llama-3-70B)|
 |llama3-70b-instruct|[LLM-Research/Meta-Llama-3-70B-Instruct](https://modelscope.cn/models/LLM-Research/Meta-Llama-3-70B-Instruct/summary)|q_proj, k_proj, v_proj|llama3|✔|✔||-|[meta-llama/Meta-Llama-3-70B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct)|
-|llama3-70b-instruct-int4|[huangjintao/Meta-Llama-3-70B-Instruct-GPTQ-Int4](https://modelscope.cn/models/huangjintao/Meta-Llama-3-70B-Instruct-GPTQ-Int4/summary)|q_proj, k_proj, v_proj|llama3|✔|✔|auto_gptq|-|-|
-|llama3-70b-instruct-int8|[huangjintao/Meta-Llama-3-70b-Instruct-GPTQ-Int8](https://modelscope.cn/models/huangjintao/Meta-Llama-3-70b-Instruct-GPTQ-Int8/summary)|q_proj, k_proj, v_proj|llama3|✔|✔|auto_gptq|-|-|
-|llama3-70b-instruct-awq|[huangjintao/Meta-Llama-3-70B-Instruct-AWQ](https://modelscope.cn/models/huangjintao/Meta-Llama-3-70B-Instruct-AWQ/summary)|q_proj, k_proj, v_proj|llama3|✔|✔|autoawq|-|-|
+|llama3-70b-instruct-int4|[huangjintao/Meta-Llama-3-70B-Instruct-GPTQ-Int4](https://modelscope.cn/models/huangjintao/Meta-Llama-3-70B-Instruct-GPTQ-Int4/summary)|q_proj, k_proj, v_proj|llama3|✔|✔|auto_gptq|-|[study-hjt/Meta-Llama-3-70B-Instruct-GPTQ-Int4](https://huggingface.co/study-hjt/Meta-Llama-3-70B-Instruct-GPTQ-Int4)|
+|llama3-70b-instruct-int8|[huangjintao/Meta-Llama-3-70b-Instruct-GPTQ-Int8](https://modelscope.cn/models/huangjintao/Meta-Llama-3-70b-Instruct-GPTQ-Int8/summary)|q_proj, k_proj, v_proj|llama3|✔|✔|auto_gptq|-|[study-hjt/Meta-Llama-3-70B-Instruct-GPTQ-Int8](https://huggingface.co/study-hjt/Meta-Llama-3-70B-Instruct-GPTQ-Int8)|
+|llama3-70b-instruct-awq|[huangjintao/Meta-Llama-3-70B-Instruct-AWQ](https://modelscope.cn/models/huangjintao/Meta-Llama-3-70B-Instruct-AWQ/summary)|q_proj, k_proj, v_proj|llama3|✔|✔|autoawq|-|[study-hjt/Meta-Llama-3-70B-Instruct-AWQ](https://huggingface.co/study-hjt/Meta-Llama-3-70B-Instruct-AWQ)|
 |chinese-llama-2-1_3b|[AI-ModelScope/chinese-llama-2-1.3b](https://modelscope.cn/models/AI-ModelScope/chinese-llama-2-1.3b/summary)|q_proj, k_proj, v_proj|default-generation|✔|✔||-|[hfl/chinese-llama-2-1.3b](https://huggingface.co/hfl/chinese-llama-2-1.3b)|
 |chinese-llama-2-7b|[AI-ModelScope/chinese-llama-2-7b](https://modelscope.cn/models/AI-ModelScope/chinese-llama-2-7b/summary)|q_proj, k_proj, v_proj|default-generation|✔|✔||-|[hfl/chinese-llama-2-7b](https://huggingface.co/hfl/chinese-llama-2-7b)|
 |chinese-llama-2-7b-16k|[AI-ModelScope/chinese-llama-2-7b-16k](https://modelscope.cn/models/AI-ModelScope/chinese-llama-2-7b-16k/summary)|q_proj, k_proj, v_proj|default-generation|✔|✔||-|[hfl/chinese-llama-2-7b-16k](https://huggingface.co/hfl/chinese-llama-2-7b-16k)|
@@ -243,6 +243,8 @@
 |codefuse-codegeex2-6b-chat|[codefuse-ai/CodeFuse-CodeGeeX2-6B](https://modelscope.cn/models/codefuse-ai/CodeFuse-CodeGeeX2-6B/summary)|query_key_value|codefuse|&#x2718;|&#x2714;|transformers<4.34|coding|[codefuse-ai/CodeFuse-CodeGeeX2-6B](https://huggingface.co/codefuse-ai/CodeFuse-CodeGeeX2-6B)|
 |codefuse-qwen-14b-chat|[codefuse-ai/CodeFuse-QWen-14B](https://modelscope.cn/models/codefuse-ai/CodeFuse-QWen-14B/summary)|c_attn|codefuse|&#x2714;|&#x2714;||coding|[codefuse-ai/CodeFuse-QWen-14B](https://huggingface.co/codefuse-ai/CodeFuse-QWen-14B)|
 |phi2-3b|[AI-ModelScope/phi-2](https://modelscope.cn/models/AI-ModelScope/phi-2/summary)|Wqkv|default-generation|&#x2714;|&#x2714;||coding|[microsoft/phi-2](https://huggingface.co/microsoft/phi-2)|
+|phi3-4b-4k-instruct|[LLM-Research/Phi-3-mini-4k-instruct](https://modelscope.cn/models/LLM-Research/Phi-3-mini-4k-instruct/summary)|qkv_proj|phi3|&#x2714;|&#x2718;|transformers>=4.36|general|[microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct)|
+|phi3-4b-128k-instruct|[LLM-Research/Phi-3-mini-128k-instruct](https://modelscope.cn/models/LLM-Research/Phi-3-mini-128k-instruct/summary)|qkv_proj|phi3|&#x2714;|&#x2718;|transformers>=4.36|general|[microsoft/Phi-3-mini-128k-instruct](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct)|
 |cogvlm-17b-instruct|[ZhipuAI/cogvlm-chat](https://modelscope.cn/models/ZhipuAI/cogvlm-chat/summary)|vision_expert_query_key_value, vision_expert_dense, language_expert_query_key_value, language_expert_dense|cogvlm-instruct|&#x2718;|&#x2718;||multi-modal, vision|[THUDM/cogvlm-chat-hf](https://huggingface.co/THUDM/cogvlm-chat-hf)|
 |cogagent-18b-chat|[ZhipuAI/cogagent-chat](https://modelscope.cn/models/ZhipuAI/cogagent-chat/summary)|vision_expert_query_key_value, vision_expert_dense, language_expert_query_key_value, language_expert_dense, query, key_value, dense|cogagent-chat|&#x2718;|&#x2718;||multi-modal, vision|[THUDM/cogagent-chat-hf](https://huggingface.co/THUDM/cogagent-chat-hf)|
 |cogagent-18b-instruct|[ZhipuAI/cogagent-vqa](https://modelscope.cn/models/ZhipuAI/cogagent-vqa/summary)|vision_expert_query_key_value, vision_expert_dense, language_expert_query_key_value, language_expert_dense, query, key_value, dense|cogagent-instruct|&#x2718;|&#x2718;||multi-modal, vision|[THUDM/cogagent-vqa-hf](https://huggingface.co/THUDM/cogagent-vqa-hf)|
@@ -291,6 +293,8 @@
 |open-orca-gpt4|[AI-ModelScope/OpenOrca](https://modelscope.cn/datasets/AI-ModelScope/OpenOrca/summary)|994896|0|382.3±417.4, min=31, max=8740|chat, multilingual, general|-|
 |sharegpt-gpt4|[AI-ModelScope/sharegpt_gpt4](https://modelscope.cn/datasets/AI-ModelScope/sharegpt_gpt4/summary)|103063|0|1286.2±2089.4, min=22, max=221080|chat, multilingual, general, multi-round|-|
 |🔥sharegpt-gpt4-mini|[AI-ModelScope/sharegpt_gpt4](https://modelscope.cn/datasets/AI-ModelScope/sharegpt_gpt4/summary)|6205|0|3511.6±6068.5, min=33, max=116018|chat, multilingual, general, multi-round, gpt4|-|
+|deepctrl-sft-zh|[AI-ModelScope/deepctrl-sft-data](https://modelscope.cn/datasets/AI-ModelScope/deepctrl-sft-data/summary)|11381621|0|274.2±311.5, min=21, max=9835|chat, general, sft, multi-round|-|
+|deepctrl-sft-en|[AI-ModelScope/deepctrl-sft-data](https://modelscope.cn/datasets/AI-ModelScope/deepctrl-sft-data/summary)|2767403|0|865.2±1157.6, min=22, max=626237|chat, general, sft, multi-round|-|
 |🔥ms-agent|[iic/ms_agent](https://modelscope.cn/datasets/iic/ms_agent/summary)|30000|0|647.7±217.1, min=199, max=2722|chat, agent, multi-round|-|
 |ms-agent-for-agentfabric-default|[AI-ModelScope/ms_agent_for_agentfabric](https://modelscope.cn/datasets/AI-ModelScope/ms_agent_for_agentfabric/summary)|30000|0|617.8±199.1, min=251, max=2657|chat, agent, multi-round|-|
 |ms-agent-for-agentfabric-addition|[AI-ModelScope/ms_agent_for_agentfabric](https://modelscope.cn/datasets/AI-ModelScope/ms_agent_for_agentfabric/summary)|488|0|2084.9±1514.8, min=489, max=7354|chat, agent, multi-round|-|

docs/source_en/LLM/NPU-best-practice.md

@@ -1,4 +1,5 @@
 # NPU Best Practice
+Authors: [chuanzhubin](https://github.com/chuanzhubin), [jintao](https://github.com/Jintao-Huang)
 
 ## Table of Contents
 - [Environment Preparation](#Environment-Preparation)