diff --git a/.gitignore b/.gitignore index 671e9e2..e687864 100644 --- a/.gitignore +++ b/.gitignore @@ -6,6 +6,8 @@ data/ pdf/ .idea/ logs/ +.vscode/ +work_dirs/ # *.jsonl # *.json diff --git a/README.md b/README.md index 3e3e7c1..85ad355 100644 --- a/README.md +++ b/README.md @@ -59,8 +59,9 @@ | ChatGLM3_6B | LoRA | [chatglm3_6b_lora_alpaca_e3.py](./xtuner_config/chatglm3_6b_lora_alpaca_e3.py) | | | DeepSeek MoE_16B_chat | QLoRA | [deepseek_moe_16b_chat_qlora_oasst1_e3.py](./xtuner_config/deepseek_moe_16b_chat_qlora_oasst1_e3.py) | | | Mixtral 8x7B_instruct | QLoRA | [mixtral_8x7b_instruct_qlora_oasst1_e3.py](./xtuner_config/mixtral_8x7b_instruct_qlora_oasst1_e3.py) | | -| LLaMA3_8b_instruct | QLoRA | [aiwei_llama3_8b_instruct_qlora_e3.py](./xtuner_config/aiwei_llama3_8b_instruct_qlora_e3.py) | [OpenXLab](https://openxlab.org.cn/models/detail/ajupyter/EmoLLM-LLaMA3_8b_instruct_aiwei/tree/main), [ModelScope](https://modelscope.cn/models/aJupyter/EmoLLM-LLaMA3_8b_instruct_aiwei/files) | -| LLaMA3_8b_instruct | QLoRA | [llama3_8b_instruct_qlora_alpaca_e3_M_ruozhi_scM.py](./xtuner_config/llama3_8b_instruct_qlora_alpaca_e3_M_ruozhi_scM.py) |[OpenXLab](https://openxlab.org.cn/models/detail/chg0901/EmoLLM-Llama3-8B-Instruct3.0), [ModelScope](https://modelscope.cn/models/chg0901/EmoLLM-Llama3-8B-Instruct3.0/summary) | +| LLaMA3_8B_instruct | QLoRA | [aiwei_llama3_8b_instruct_qlora_e3.py](./xtuner_config/aiwei_llama3_8b_instruct_qlora_e3.py) | [OpenXLab](https://openxlab.org.cn/models/detail/ajupyter/EmoLLM-LLaMA3_8b_instruct_aiwei/tree/main), [ModelScope](https://modelscope.cn/models/aJupyter/EmoLLM-LLaMA3_8b_instruct_aiwei/files) | +| LLaMA3_8B_instruct | QLoRA | [llama3_8b_instruct_qlora_alpaca_e3_M_ruozhi_scM.py](./xtuner_config/llama3_8b_instruct_qlora_alpaca_e3_M_ruozhi_scM.py) |[OpenXLab](https://openxlab.org.cn/models/detail/chg0901/EmoLLM-Llama3-8B-Instruct3.0), [ModelScope](https://modelscope.cn/models/chg0901/EmoLLM-Llama3-8B-Instruct3.0/summary) | +| Qwen2-7B-Instruct | LoRA | [Qwen2-7B-Instruct_lora.py](./xtuner_config/Qwen2-7B-Instruct_lora.py) |[ModelScope](https://www.modelscope.cn/models/aJupyter/EmoLLM_Qwen2-7B-Instruct_lora/) | | …… | …… | …… | …… | @@ -100,6 +101,8 @@ ## 🎇最近更新 +- 【2024.09.14】基于Qwen2-7B-Instruct模型的Lora微调模型开源，微调配置文件地址：[Qwen2-7B-Instruct_lora.py](./xtuner_config/Qwen2-7B-Instruct_lora.py) ，模型权重链接：[ModelScope](https://www.modelscope.cn/models/aJupyter/EmoLLM_Qwen2-7B-Instruct_lora/) +- 【2024.08】基于GLM4-9B-chat微调Lora模型开源（基于LLaMA-Factory），详情见[微调教程](./doc/GLM-4-9B-chat%20Lora%20微调（llama-factory）.md) ，模型权重链接：[ModelScope](https://www.modelscope.cn/models/wwewwt/EmoLLM-glm-4-9b-chat/summary) - 【2024.07.16】欢迎大家体验 EmoLLM V3.0 ，该模型是基于InternLM2.5-7B-Chat模型的全量微调，微调配置文件地址：[internlm2_5_chat_7b_full.py](./xtuner_config/internlm2_5_chat_7b_full.py) ，模型权重链接：[OpenXLab](https://openxlab.org.cn/models/detail/chg0901/EmoLLM_V3.0), [ModelScope](https://modelscope.cn/models/chg0901/EmoLLMV3.0) ，WebDemo地址： [OpenXLab apps](https://openxlab.org.cn/apps/detail/chg0901/EmoLLMV3.0), [配套全量微调知乎教程](https://zhuanlan.zhihu.com/p/708931911)。 - 【2024.07】欢迎大家使用稳定版 EmoLLM V2.0 进行日常使用和学术研究，模型权重链接：[OpenXLab](https://openxlab.org.cn/models/detail/ajupyter/EmoLLM_internlm2_7b_full/tree/main)。 - 【2024.07】新增基于InternLM2_5_7B_chat[微调配置](./xtuner_config/internlm2_5_chat_7b_qlora_oasst1_e3.py)、模型文件发布在 [ModelScope](https://www.modelscope.cn/models/z342994309/emollm_interlm2_5/)。 @@ -117,15 +120,14 @@ - 【2024.03.11】 **EmoLLM V2.0 相比 EmoLLM V1.0 全面提升，已超越 Role-playing ChatGPT 在心理咨询任务上的能力！**[点击体验EmoLLM V2.0](https://openxlab.org.cn/apps/detail/Farewell1/EmoLLMV2.0)，更新[数据集统计及详细信息](./datasets/)、[路线图](./assets/Roadmap_ZH.png) - 【2024.03.09】 新增并发功能加速 [QA 对生成](./scripts/qa_generation/)、[RAG pipeline](./rag/) - 【2024.03.03】 [基于InternLM2-7B-chat全量微调版本EmoLLM V2.0开源](https://openxlab.org.cn/models/detail/ajupyter/EmoLLM_internlm2_7b_full)，需要两块A100*80G，更新专业评估，详见[evaluate](./evaluate/)，更新基于PaddleOCR的PDF转txt工具脚本，详见[scripts](./scripts/) + +
+查看更多 - 【2024.02.29】更新客观评估计算，详见[evaluate](./evaluate/)，更新一系列数据集，详见[datasets](./datasets/) - 【2024.02.27】更新英文readme和一系列数据集（舔狗和单轮对话） - 【2024.02.23】推出基于InternLM2_7B_chat_qlora的 `温柔御姐心理医生艾薇`，[点击获取模型权重](https://openxlab.org.cn/models/detail/ajupyter/EmoLLM_aiwei)，[配置文件](xtuner_config/aiwei-internlm2_chat_7b_qlora.py)，[在线体验链接](https://openxlab.org.cn/apps/detail/ajupyter/EmoLLM-aiwei) - 【2024.02.23】更新[若干微调配置](/xtuner_config/)，新增 [data_pro.json](/datasets/data_pro.json)（数量更多、场景更全、更丰富）和 [aiwei.json](/datasets/aiwei.json)（温柔御姐角色扮演专用，带有Emoji表情），即将推出 `温柔御姐心理医生艾薇` - 【2024.02.18】 [基于Qwen1_5-0_5B-Chat全量微调版本开源](https://www.modelscope.cn/models/aJupyter/EmoLLM_Qwen1_5-0_5B-Chat_full_sft/summary)，算力有限的道友可以玩起来~ - -
-查看更多 - - 【2024.02.06】 EmoLLM在[**Openxlab** ](https://openxlab.org.cn/models/detail/jujimeizuo/EmoLLM_Model) 平台下载量高达18.7k，欢迎大家体验！

@@ -185,7 +187,7 @@ - [使用指南](#使用指南) - [🍪快速体验](#快速体验) - [📌数据构建](#数据构建) - - [🎨微调指南](#微调指南) + - [🎨增量预训练、微调指南](#增量预训练微调指南) - [🔧部署指南](#部署指南) - [⚙RAG(检索增强生成)](#rag检索增强生成) - [🎓评测指南](#评测指南) @@ -204,6 +206,7 @@ ###### 开发前的配置要求 - 硬件：A100 40G（仅针对InternLM2_7B_chat+qlora微调+deepspeed zero2优化） +- todo：发布更多硬件消耗细节 ###### 使用指南 @@ -216,7 +219,7 @@ git clone https://github.com/SmartFlowAI/EmoLLM.git 2. 依次阅读或者选择感兴趣的部分阅读： - [快速体验](#快速体验) - [数据构建](#数据构建) - - [微调指南](#微调指南) + - [增量预训练、微调指南](#增量预训练微调指南) - [部署指南](#部署指南) - [RAG](#rag检索增强生成) - [评测指南](#评测指南) @@ -230,19 +233,21 @@ git clone https://github.com/SmartFlowAI/EmoLLM.git ### 📌数据构建 - - 请阅读[数据构建指南](generate_data/tutorial.md)查阅 - - 微调用到的数据集见[datasets](datasets/data.json) -### 🎨微调指南 - -详见[微调指南](xtuner_config/README.md) +### 🎨增量预训练、微调指南 +- 增量预训练详见[增量预训练指南](./xtuner_config/pt/README.md) +- 【基于xtuner】全量、LoRA、QLoRA微调详见[微调指南](./xtuner_config/README.md) +- 【基于ms-swift】全量、LoRA、QLoRA微调详见[微调指南](./swift/README.md) +- 【基于LLaMA-Factory】全量、LoRA、QLoRA微调详见[微调指南](./doc/GLM-4-9B-chat%20Lora%20微调（llama-factory）.md) +- todo：待更新DPO训练 ### 🔧部署指南 - Demo部署：详见[部署指南](demo/README.md) - 基于[LMDeploy](https://github.com/InternLM/lmdeploy/)的量化部署：详见[deploy](./deploy/lmdeploy.md) +- todo: 基于VLLM部署指南 ### ⚙RAG(检索增强生成) @@ -257,13 +262,14 @@ git clone https://github.com/SmartFlowAI/EmoLLM.git ### 使用到的框架 -- [Xtuner](https://github.com/InternLM/xtuner)：用于微调 +- [xtuner](https://github.com/InternLM/xtuner)：用于微调 - [Transformers](https://github.com/huggingface/transformers) - [Pytorch](https://pytorch.org/) - [LMDeploy](https://github.com/InternLM/lmdeploy/)：用于量化部署 - [Stremlit](https://streamlit.io/)：用于构建Demo - [DeepSpeed](https://github.com/microsoft/DeepSpeed)：并行训练 -- … +- [LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory/blob/main)：训练框架 +- [ms-swift](https://github.com/modelscope/ms-swift)：训练框架 #### 如何参与本项目 diff --git a/README_EN.md b/README_EN.md index 75af904..ddca556 100644 --- a/README_EN.md +++ b/README_EN.md @@ -63,6 +63,7 @@ | Mixtral 8x7B_instruct | QLoRA | [mixtral_8x7b_instruct_qlora_oasst1_e3.py](./xtuner_config/mixtral_8x7b_instruct_qlora_oasst1_e3.py) | | | LLaMA3_8b_instruct | QLoRA | [aiwei_llama3_8b_instruct_qlora_e3.py](./xtuner_config/aiwei_llama3_8b_instruct_qlora_e3.py) | [OpenXLab](https://openxlab.org.cn/models/detail/ajupyter/EmoLLM-LLaMA3_8b_instruct_aiwei/tree/main), [ModelScope](https://modelscope.cn/models/aJupyter/EmoLLM-LLaMA3_8b_instruct_aiwei/files) | | LLaMA3_8b_instruct | QLoRA | [llama3_8b_instruct_qlora_alpaca_e3_M_ruozhi_scM.py](./xtuner_config/llama3_8b_instruct_qlora_alpaca_e3_M_ruozhi_scM.py) |[OpenXLab](https://openxlab.org.cn/models/detail/chg0901/EmoLLM-Llama3-8B-Instruct3.0), [ModelScope](https://modelscope.cn/models/chg0901/EmoLLM-Llama3-8B-Instruct3.0/summary) | +| Qwen2-7B-Instruct | LoRA | [Qwen2-7B-Instruct_lora.py](./xtuner_config/Qwen2-7B-Instruct_lora.py) |[ModelScope](https://www.modelscope.cn/models/aJupyter/EmoLLM_Qwen2-7B-Instruct_lora/) | | …… | …… | …… | …… | @@ -104,11 +105,13 @@ The Model aims to fully understand and promote the mental health of individuals, ## Recent Updates -- 【2024.07.16】 Welcome everyone to experience EmoLLM V3.0. This model is a fully fine-tuned version based on the InternLM2.5-7B-Chat model. The fine-tuning configuration file can be found at: [internlm2_5_chat_7b_full.py](./xtuner_config/internlm2_5_chat_7b_full.py). Model weights are available at: [OpenXLab](https://openxlab.org.cn/models/detail/chg0901/EmoLLM_V3.0), [ModelScope](https://modelscope.cn/models/chg0901/EmoLLMV3.0). WebDemo is available at: [OpenXLab apps](https://openxlab.org.cn/apps/detail/chg0901/EmoLLMV3.0), [Full fine-tuning tutorial on Zhihu](https://zhuanlan.zhihu.com/p/708931911). -- 【2024.07】Welcome to use the stable version of EmoLLM V2.0 for daily use and academic research. Model weight link: [OpenXLab](https://openxlab.org.cn/models/detail/ajupyter/EmoLLM_internlm2_7b_full/tree/main). -- 【2024.07】Added InternLM2_5_7B_chat[fine-tuning configuration](./xtuner_config/internlm2_5_chat_7b_qlora_oasst1_e3.py)、model file [ModelScope](https://www.modelscope.cn/models/z342994309/emollm_interlm2_5/)。 -- 【2024.06】 Added [LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory)[GLM4-9B-chat fine-tuning guide](./doc/GLM-4-9B-chat%20Lora%20微调（llama-factory）.md), added [swift-based fine-tuning guide](./swift/), the paper [ESC-Eval: Evaluating Emotion Support Conversations in Large Language Models](https://arxiv.org/abs/2406.14952) cited EmoLLM and EmoLLM achieved good results. -- 【2024.05.28】The multi-turn dialogue dataset **CPsyCunD** and **professional evaluation method** used by EmoLLM have been released. For details, please see the 2024 ACL findings[《CPsyCoun: A Report-based Multi-turn Dialogue Reconstruction and Evaluation Framework for Chinese Psychological Counseling》](https://arxiv.org/abs/2405.16433)! +- [2024.09.14] The Lora fine-tuned model based on the Qwen2-7B-Instruct model is open-sourced. Fine-tuning configuration file address: [Qwen2-7B-Instruct_lora.py](./xtuner_config/Qwen2-7B-Instruct_lora.py), model weight link: [ModelScope](https://www.modelscope.cn/models/aJupyter/EmoLLM_Qwen2-7B-Instruct_lora/) +- [2024.08] The Lora fine-tuned model based on GLM4-9B-chat is open-sourced (based on Llama-factory). For details, see [Fine-tuning Tutorial](./doc/GLM-4-9B-chat%20Lora%20微调（llama-factory）.md), model weight link: [ModelScope](https://www.modelscope.cn/models/wwewwt/EmoLLM-glm-4-9b-chat/summary) +- [2024.07.16] Welcome everyone to experience EmoLLM V3.0. This model is a fully fine-tuned version based on the InternLM2.5-7B-Chat model. The fine-tuning configuration file can be found at: [internlm2_5_chat_7b_full.py](./xtuner_config/internlm2_5_chat_7b_full.py). Model weights are available at: [OpenXLab](https://openxlab.org.cn/models/detail/chg0901/EmoLLM_V3.0), [ModelScope](https://modelscope.cn/models/chg0901/EmoLLMV3.0). WebDemo is available at: [OpenXLab apps](https://openxlab.org.cn/apps/detail/chg0901/EmoLLMV3.0), [Full fine-tuning tutorial on Zhihu](https://zhuanlan.zhihu.com/p/708931911). +- [2024.07] Welcome to use the stable version of EmoLLM V2.0 for daily use and academic research. Model weight link: [OpenXLab](https://openxlab.org.cn/models/detail/ajupyter/EmoLLM_internlm2_7b_full/tree/main). +- [2024.07] Added InternLM2_5_7B_chat[fine-tuning configuration](./xtuner_config/internlm2_5_chat_7b_qlora_oasst1_e3.py)、model file [ModelScope](https://www.modelscope.cn/models/z342994309/emollm_interlm2_5/)。 +- [2024.06] Added [LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory)[GLM4-9B-chat fine-tuning guide](./doc/GLM-4-9B-chat%20Lora%20微调（llama-factory）.md), added [swift-based fine-tuning guide](./swift/), the paper [ESC-Eval: Evaluating Emotion Support Conversations in Large Language Models](https://arxiv.org/abs/2406.14952) cited EmoLLM and EmoLLM achieved good results. +- [2024.05.28] The multi-turn dialogue dataset **CPsyCunD** and **professional evaluation method** used by EmoLLM have been released. For details, please see the 2024 ACL findings[《CPsyCoun: A Report-based Multi-turn Dialogue Reconstruction and Evaluation Framework for Chinese Psychological Counseling》](https://arxiv.org/abs/2405.16433)! - [2024.05.08] EmoLLM**Daddy-like BF V0.1** is public now in [1. **Baidu AppBuilder**](https://appbuilder.baidu.com/s/4cLyw) and [2. **OpenXLab**](https://openxlab.org.cn/apps/detail/chg0901/EmoLLM3.0_Gradio_Llama3-8B-Instruct3.0), welcome to like and add it to your collections! - [2024.05.07] [Incremental Pre-training Guide](xtuner_config/pt/README.md) - [2024.05.04] [EmoLLM3.0 OpenXLab Demo](https://st-app-center-006861-9746-jlroxvg.openxlab.space/) based on LLaMA3_8b_instruct is available now ([restart link]((https://openxlab.org.cn/apps/detail/chg0901/EmoLLM-Llama3-8B-Instruct3.0))), [LLAMA3 fine-tuning guide](xtuner_config/README_llama3_8b_instruct_qlora_alpaca_e3_M.md) is updated, LLaMA3_8b_instruct-8B QLoRA fine-tuning model EmoLLM3.0 weights are released on [**OpenXLab**](https://openxlab.org.cn/models/detail/chg0901/EmoLLM-Llama3-8B-Instruct3.0) and [**ModelScope**](https://modelscope.cn/models/chg0901/EmoLLM-Llama3-8B-Instruct3.0/summary) platforms @@ -122,6 +125,10 @@ The Model aims to fully understand and promote the mental health of individuals, - [2024.03.11] **EmoLLM V2.0 is greatly improved in all scores compared to EmoLLM V1.0. Surpasses the performance of Role-playing ChatGPT on counseling tasks!** [Click to experience EmoLLM V2.0](https://openxlab.org.cn/apps/detail/Farewell1/EmoLLMV2.0), update [dataset statistics and details](./datasets/), [Roadmap](./assets/Roadmap_ZH.png) - [2024.03.09] Add concurrency acceleration [QA pair generation](./scripts/qa_generation/), [RAG pipeline](./rag/) - [2024.03.03] [Based on InternLM2-7B-chat full fine-tuned version EmoLLM V2.0 open sourced](https://openxlab.org.cn/models/detail/ajupyter/EmoLLM_internlm2_7b_full), need two A100*80G, update professional evaluation, see [evaluate](./evaluate/), update PaddleOCR-based PDF to txt tool scripts, see [scripts](./scripts/). + + +

+View More - [2024.02.29] Updated objective assessment calculations, see [evaluate](./evaluate/) for details. A series of datasets have also been updated, see [datasets](./datasets/) for details. - [2024.02.27] Updated English README and a series of datasets (licking dogs and one-round dialogue) - [2024.02.23]The "Gentle Lady Psychologist Ai Wei" based on InternLM2_7B_chat_qlora was launched. [Click here to obtain the model weights](https://openxlab.org.cn/models/detail/ajupyter/EmoLLM_aiwei), [configuration file](xtuner_config/aiwei-internlm2_chat_7b_qlora.py), [online experience link](https://openxlab.org.cn/apps/detail/ajupyter/EmoLLM-aiwei) @@ -129,11 +136,7 @@ The Model aims to fully understand and promote the mental health of individuals, - [2024.02.23]Updated [several fine-tuning configurations](/xtuner_config/), added [data_pro.json](/datasets/data_pro.json) (more quantity, more comprehensive scenarios, richer content) and [aiwei.json](/datasets/aiwei.json) (dedicated to the gentle lady role-play, featuring Emoji expressions), the "Gentle Lady Psychologist Ai Wei" is coming soon. - [2024.02.18] The full fine-tuned version based on Qwen1_5-0_5B-Chat has been [open-sourced](https://www.modelscope.cn/models/aJupyter/EmoLLM_Qwen1_5-0_5B-Chat_full_sft/summary). Friends with limited computational resources can now dive in and explore it. - - -
-View More - + - [2024.02.06] [Open-sourced based on the Qwen1_5-0_5B-Chat full-scale fine-tuned version](https://www.modelscope.cn/models/aJupyter/EmoLLM_Qwen1_5-0_5B-Chat_full_sft/summary), friends with limited computing power can start experimenting~

@@ -187,7 +190,7 @@ The Model aims to fully understand and promote the mental health of individuals, - [User Guide](#user-guide) - [🍪Quick start](#quick-start) - [📌Data Construction](#data-construction) - - [🎨Fine-tuning Guide](#fine-tuning-guide) + - [🎨Incremental Pre-training and Fine-tuning Guide](#incremental-pre-training-and-fine-tuning-guide) - [🔧Deployment Guide](#deployment-guide) - [⚙RAG (Retrieval Augmented Generation)](#rag-retrieval-augmented-generation) - [🎓Evaluation Guide](#evaluation-guide) @@ -206,6 +209,7 @@ The Model aims to fully understand and promote the mental health of individuals, ###### Pre-development Configuration Requirements. - A100 40G (specifically for InternLM2_7B_chat + qlora fine-tuning + deepspeed zero2 optimization) +- **[TODO]**: Publish more details about hardware consumption. ###### User Guide @@ -218,7 +222,7 @@ git clone https://github.com/SmartFlowAI/EmoLLM.git 1. Read in sequence or read sections you're interested in： - [Quick Start](#quick-start) - [Data Construction](#data-construction) - - [Fine-tuning Guide](#fine-tuning-guide) + - [Fine-tuning Guide](#incremental-pre-training-and-fine-tuning-guide) - [Deployment Guide](#deployment-guide) - [RAG](#rag-retrieval-augmented-generation) - [Evaluation Guide](#evaluation-guide) @@ -230,19 +234,22 @@ git clone https://github.com/SmartFlowAI/EmoLLM.git - Quick coding: [Baby EmoLLM](quick_start/Baby_EmoLLM.ipynb) ### 📌Data Construction - - Please read the [Data Construction Guide ](generate_data/tutorial_EN.md) for reference. - - The dataset used for this fine-tuning can be found at [datasets](datasets/data.json) -### 🎨Fine-tuning Guide +### 🎨Incremental Pre-training and Fine-tuning Guide +- For details on incremental pre-training, see [Incremental Pre-training Guide](./xtuner_config/pt/README.md). +- For full-scale, LoRA, and QLoRA fine-tuning based on **xtuner**, see [Fine-tuning Guide](./xtuner_config/README_EN.md). +- For full-scale, LoRA, and QLoRA fine-tuning based on **ms-swift**, see [Fine-tuning Guide](./swift/README_EN.md). +- For full-scale, LoRA, and QLoRA fine-tuning based on **LLaMA-Factory**, see [Fine-tuning Guide](./doc/GLM-4-9B-chat%20Lora%20微调（llama-factory）.md). +- **[TODO]**: Update DPO training. -For details, see the [fine-tuning guide](xtuner_config/README_EN.md) ### 🔧Deployment Guide - Demo deployment: see [deployment guide](./demo/README_EN.md) for details. - Quantitative deployment based on [LMDeploy](https://github.com/InternLM/lmdeploy/): see [deploy](./deploy/lmdeploy_EN.md) +- **[TODO]**: Deployment Guide for VLLM ### ⚙RAG (Retrieval Augmented Generation) @@ -263,7 +270,8 @@ For details, see the [fine-tuning guide](xtuner_config/README_EN.md) - [LMDeploy](https://github.com/InternLM/lmdeploy/): for quantitative deployment - [Stremlit](https://streamlit.io/): for building demos - [DeepSpeed](https://github.com/microsoft/DeepSpeed): for parallel training -- … +- [LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory/blob/main) +- [ms-swift](https://github.com/modelscope/ms-swift) #### How to participate in this project diff --git a/scripts/upload_modelscope.py b/scripts/upload_modelscope.py index 9aff52b..672b6b3 100644 --- a/scripts/upload_modelscope.py +++ b/scripts/upload_modelscope.py @@ -1,11 +1,11 @@ from modelscope.hub.api import HubApi -YOUR_ACCESS_TOKEN = '' #输入你的modelscope access token +YOUR_ACCESS_TOKEN = '' # 输入你的modelscope access token api = HubApi() api.login(YOUR_ACCESS_TOKEN) api.push_model( - model_id="zealot5209/EmoLLM-Scientist", #your_name/model_id - model_dir="./merged" # 本地模型目录，要求目录中必须包含configuration.json - ) + model_id="zealot5209/EmoLLM-Scientist", # your_name/model_id + model_dir="./merged" # 本地模型目录，要求目录中必须包含configuration.json +) diff --git a/xtuner_config/Qwen2-7B-Instruct_lora.py b/xtuner_config/Qwen2-7B-Instruct_lora.py new file mode 100644 index 0000000..4694900 --- /dev/null +++ b/xtuner_config/Qwen2-7B-Instruct_lora.py @@ -0,0 +1,213 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import torch +from datasets import load_dataset +from mmengine.dataset import DefaultSampler +from mmengine.hooks import (CheckpointHook, DistSamplerSeedHook, IterTimerHook, + LoggerHook, ParamSchedulerHook) +from mmengine.optim import AmpOptimWrapper, CosineAnnealingLR, LinearLR +from peft import LoraConfig +from torch.optim import AdamW +from transformers import (AutoModelForCausalLM, AutoTokenizer, + BitsAndBytesConfig) + +from xtuner.dataset import process_hf_dataset +from xtuner.dataset.collate_fns import default_collate_fn +from xtuner.dataset.map_fns import alpaca_map_fn, template_map_fn_factory +from xtuner.engine.hooks import (DatasetInfoHook, EvaluateChatHook, + VarlenAttnArgsToMessageHubHook) +from xtuner.engine.runner import TrainLoop +from xtuner.model import SupervisedFinetune +from xtuner.parallel.sequence import SequenceParallelSampler +from xtuner.utils import PROMPT_TEMPLATE, SYSTEM_TEMPLATE + +####################################################################### +# PART 1 Settings # +####################################################################### +# Model +pretrained_model_name_or_path = 'Qwen2-7B-Instruct' # your model path +use_varlen_attn = False + +# Data +alpaca_en_path = '../datasets/aiwei.json' +prompt_template = PROMPT_TEMPLATE.qwen_chat +max_length = 1024 +pack_to_max_length = True + +# parallel +sequence_parallel_size = 1 + +# Scheduler & Optimizer +batch_size = 8 # per_device +accumulative_counts = 16 +accumulative_counts *= sequence_parallel_size +dataloader_num_workers = 4 +max_epochs = 3 +optim_type = AdamW +lr = 1e-5 +betas = (0.9, 0.999) +weight_decay = 0 +max_norm = 1 # grad clip +warmup_ratio = 0.03 + +# Save +save_steps = 100 +save_total_limit = 2 # Maximum checkpoints to keep (-1 means unlimited) + +# Evaluate the generation performance during the training +evaluation_freq = 100 +SYSTEM = "现在你是一个心理专家，我有一些心理问题，请你用专业的知识帮我解决。" +evaluation_inputs = [ + '我压力很大', '生活没意思', "非常容易羡慕别人啊" +] + +####################################################################### +# PART 2 Model & Tokenizer # +####################################################################### +tokenizer = dict( + type=AutoTokenizer.from_pretrained, + pretrained_model_name_or_path=pretrained_model_name_or_path, + trust_remote_code=True, + padding_side='right') + +model = dict( + type=SupervisedFinetune, + use_varlen_attn=use_varlen_attn, + llm=dict( + type=AutoModelForCausalLM.from_pretrained, + pretrained_model_name_or_path=pretrained_model_name_or_path, + trust_remote_code=True, + torch_dtype=torch.float16, + ), + lora=dict( + type=LoraConfig, + r=32, + lora_alpha=16, + lora_dropout=0.1, + bias='none', + task_type='CAUSAL_LM')) + +####################################################################### +# PART 3 Dataset & Dataloader # +####################################################################### +alpaca_en = dict( + type=process_hf_dataset, + dataset=dict(type=load_dataset, path='json', + data_files=dict(train=alpaca_en_path)), + tokenizer=tokenizer, + max_length=max_length, + dataset_map_fn=None, + template_map_fn=dict( + type=template_map_fn_factory, template=prompt_template), + remove_unused_columns=True, + shuffle_before_pack=True, + pack_to_max_length=pack_to_max_length, + use_varlen_attn=use_varlen_attn) + +sampler = SequenceParallelSampler \ + if sequence_parallel_size > 1 else DefaultSampler + +train_dataloader = dict( + batch_size=batch_size, + num_workers=dataloader_num_workers, + dataset=alpaca_en, + sampler=dict(type=sampler, shuffle=True), + collate_fn=dict(type=default_collate_fn, use_varlen_attn=use_varlen_attn)) + +####################################################################### +# PART 4 Scheduler & Optimizer # +####################################################################### +# optimizer +optim_wrapper = dict( + type=AmpOptimWrapper, + optimizer=dict( + type=optim_type, lr=lr, betas=betas, weight_decay=weight_decay), + clip_grad=dict(max_norm=max_norm, error_if_nonfinite=False), + accumulative_counts=accumulative_counts, + loss_scale='dynamic', + dtype='float16') + +# learning policy +# More information: https://github.com/open-mmlab/mmengine/blob/main/docs/en/tutorials/param_scheduler.md # noqa: E501 +param_scheduler = [ + dict( + type=LinearLR, + start_factor=1e-5, + by_epoch=True, + begin=0, + end=warmup_ratio * max_epochs, + convert_to_iter_based=True), + dict( + type=CosineAnnealingLR, + eta_min=0.0, + by_epoch=True, + begin=warmup_ratio * max_epochs, + end=max_epochs, + convert_to_iter_based=True) +] + +# train, val, test setting +train_cfg = dict(type=TrainLoop, max_epochs=max_epochs) + +####################################################################### +# PART 5 Runtime # +####################################################################### +# Log the dialogue periodically during the training process, optional +custom_hooks = [ + dict(type=DatasetInfoHook, tokenizer=tokenizer), + dict( + type=EvaluateChatHook, + tokenizer=tokenizer, + every_n_iters=evaluation_freq, + evaluation_inputs=evaluation_inputs, + system=SYSTEM, + prompt_template=prompt_template) +] + +if use_varlen_attn: + custom_hooks += [dict(type=VarlenAttnArgsToMessageHubHook)] + +# configure default hooks +default_hooks = dict( + # record the time of every iteration. + timer=dict(type=IterTimerHook), + # print log every 10 iterations. + logger=dict(type=LoggerHook, log_metric_by_epoch=False, interval=10), + # enable the parameter scheduler. + param_scheduler=dict(type=ParamSchedulerHook), + # save checkpoint per `save_steps`. + checkpoint=dict( + type=CheckpointHook, + by_epoch=False, + interval=save_steps, + max_keep_ckpts=save_total_limit), + # set sampler seed in distributed evrionment. + sampler_seed=dict(type=DistSamplerSeedHook), +) + +# configure environment +env_cfg = dict( + # whether to enable cudnn benchmark + cudnn_benchmark=False, + # set multi process parameters + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0), + # set distributed parameters + dist_cfg=dict(backend='nccl'), +) + +# set visualizer +visualizer = None + +# set log level +log_level = 'INFO' + +# load from which checkpoint +load_from = None + +# whether to resume training from the loaded checkpoint +resume = False + +# Defaults to use random seed and disable `deterministic` +randomness = dict(seed=None, deterministic=False) + +# set log processor +log_processor = dict(by_epoch=False)