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Model: aJupyter/EmoLLM_Qwen2-7B-Instruct_lora
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<div align="center">
# EmoLLM-心理健康大模型
</div>
<p align="center">
<a href="https://github.com/SmartFlowAI/EmoLLM/">
<img src="assets/EmoLLM_transparent.png" alt="Logo" width="50%">
</a>
<div align="center">
<!-- PROJECT SHIELDS -->
[![Contributors][contributors-shield]][contributors-url]
[![Forks][forks-shield]][forks-url]
[![Issues][issues-shield]][issues-url]
[![OpenXLab_App][OpenXLab_App-image]][OpenXLab_App-url]
[![OpenXLab_Model][OpenXLab_Model-image]][OpenXLab_Model-url]
[![MIT License][license-shield]][license-url]
[![Stargazers][stars-shield]][stars-url]
</div>
<h3 align="center">EmoLLM</h3>
<div align="center">
简体中文| <a href="README_EN.md" >English</a> | <a href="README_JP.md" >日本語</a>
<br />
<br />
<a href="https://github.com/SmartFlowAI/EmoLLM"><strong>探索本项目的文档 »</strong></a>
<br />
<br />
<a href="https://openxlab.org.cn/apps/detail/Farewell1/EmoLLMV2.0">体验EmoLLM 2.0</a>
·
<a href="https://github.com/SmartFlowAI/EmoLLM/issues">报告Bug</a>
·
<a href="https://github.com/SmartFlowAI/EmoLLM/issues">提出新特性</a>
</div>
<!-- 本篇README.md面向开发者 -->
**EmoLLM** 是一系列能够支持 **理解用户-支持用户-帮助用户** 心理健康辅导链路的心理健康大模型,由 `LLM`指令微调而来,欢迎大家star~⭐⭐。目前已经开源的 `LLM` 微调配置如下:
<div align="center">
| 模型 | 类型 | 链接 | 模型链接 |
| :-------------------: | :------: | :------------------------------------------------------------------------------------------------------: |:------: |
| InternLM2_5_7B_chat | 全量微调 | [internlm2_5_chat_7b_full.py](./xtuner_config/internlm2_5_chat_7b_full.py) | [OpenXLab](https://openxlab.org.cn/models/detail/chg0901/EmoLLM_V3.0), [ModelScope](https://modelscope.cn/models/chg0901/EmoLLMV3.0) |
| InternLM2_5_7B_chat | QLoRA | [internlm2_5_chat_7b_qlora_oasst1_e3.py](./xtuner_config/internlm2_5_chat_7b_qlora_oasst1_e3.py) |[ModelScope](https://www.modelscope.cn/models/z342994309/emollm_interlm2_5/) |
| InternLM2_7B_chat | QLoRA | [internlm2_7b_chat_qlora_e3.py](./xtuner_config/internlm2_7b_chat_qlora_e3.py) | [ModelScope](https://modelscope.cn/models/aJupyter/EmoLLM/files) |
| InternLM2_7B_chat | 全量微调 | [internlm2_chat_7b_full.py](./xtuner_config/internlm2_chat_7b_full.py) | [OpenXLab](https://openxlab.org.cn/models/detail/ajupyter/EmoLLM_internlm2_7b_full) |
| InternLM2_7B_base | QLoRA | [internlm2_7b_base_qlora_e10_M_1e4_32_64.py](./xtuner_config/internlm2_7b_base_qlora_e10_M_1e4_32_64.py) |[OpenXLab](https://openxlab.org.cn/models/detail/chg0901/EmoLLM-InternLM7B-base-10e), [ModelScope](https://www.modelscope.cn/models/chg0901/EmoLLM-InternLM7B-base-10e/summary) |
| InternLM2_1_8B_chat | 全量微调 | [internlm2_1_8b_full_alpaca_e3.py](./xtuner_config/internlm2_1_8b_full_alpaca_e3.py) | [OpenXLab](https://openxlab.org.cn/models/detail/ajupyter/EmoLLM_internlm2_1_8b_full/tree/main), [ModelScope](https://modelscope.cn/models/aJupyter/EmoLLM_PT_InternLM1.8B-chat/files) |
| InternLM2_20B_chat | LoRA |[internlm2_20b_chat_lora_alpaca_e3.py](./xtuner_config/internlm2_20b_chat_lora_alpaca_e3.py)| |
| Qwen_7b_chat | QLoRA | [qwen_7b_chat_qlora_e3.py](./xtuner_config/qwen_7b_chat_qlora_e3.py) | |
| Qwen1_5-0_5B-Chat | 全量微调 | [qwen1_5_0_5_B_full.py](./xtuner_config/qwen1_5_0_5_B_full.py) | [ModelScope](https://www.modelscope.cn/models/aJupyter/EmoLLM_Qwen1_5-0_5B-Chat_full_sft/summary) |
| Baichuan2_13B_chat | QLoRA | [baichuan2_13b_chat_qlora_alpaca_e3.py](./xtuner_config/baichuan2_13b_chat_qlora_alpaca_e3.py) | |
| 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) |
| 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/) |
| …… | …… | …… | …… |
</div>
欢迎大家为本项目做出贡献~
---
心理健康大模型(Mental Health Grand Model)是一个综合性的概念,它旨在全面理解和促进个体、群体乃至整个社会的心理健康状态。这个模型通常包含以下几个关键组成部分:
- 认知因素:涉及个体的思维模式、信念系统、认知偏差以及解决问题的能力。认知因素对心理健康有重要影响,因为它们影响个体如何解释和应对生活中的事件。
- 情感因素:包括情绪调节、情感表达和情感体验。情感健康是心理健康的重要组成部分,涉及个体如何管理和表达自己的情感,以及如何从负面情绪中恢复。
- 行为因素:涉及个体的行为模式、习惯和应对策略。这包括应对压力的技巧、社交技能以及自我效能感,即个体对自己能力的信心。
- 社会环境:包括家庭、工作、社区和文化背景等外部因素,这些因素对个体的心理健康有着直接和间接的影响。
- 生理健康:身体健康与心理健康紧密相关。良好的身体健康可以促进心理健康,反之亦然。
- 心理韧性:指个体在面对逆境时的恢复力和适应能力。心理韧性强的人更能够从挑战中恢复,并从中学习和成长。
- 预防和干预措施:心理健康大模型还包括预防心理问题和促进心理健康的策略,如心理教育、心理咨询、心理治疗和社会支持系统。
- 评估和诊断工具:为了有效促进心理健康,需要有科学的工具来评估个体的心理状态,以及诊断可能存在的心理问题。
<table>
<tr>
<td align="center" style="background-color: transparent">
<img src="assets\aiwei_demo.gif" alt="占位图">
</td>
<td align="center" style="background-color: transparent">
<img src="assets\aiwei_demo2.gif" alt="占位图">
</td>
</tr>
<tr>
<td align="center" style="background-color: transparent">
<img src="assets\aiwei_demo3.gif" alt="占位图">
</td>
<td align="center" style="background-color: transparent">
<img src="assets\aiwei_demo4.gif" alt="占位图">
</td>
</tr>
</table>
## 🎇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/)。
- 【2024.06】新增基于[LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory)[GLM4-9B-chat微调指南](./doc/GLM-4-9B-chat%20Lora%20微调(llama-factory).md)、新增[基于swift的微调指南](./swift/)、论文[ESC-Eval: Evaluating Emotion Support Conversations in Large Language Models](https://arxiv.org/abs/2406.14952)引用了EmoLLM且EmoLLM取得了较好的效果。
- 【2024.05.28】EmoLLM使用的多轮对话数据集CPsyCounD和专业评测方法已公开,详见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**爹系男友阅览体验版**上线 [1. **百度AppBuilder**](https://appbuilder.baidu.com/s/4cLyw) [2. **OpenXLab**](https://openxlab.org.cn/apps/detail/chg0901/EmoLLM3.0_Gradio_Llama3-8B-Instruct3.0), 欢迎点赞收藏
- 【2024.05.07】[增量预训练指南](xtuner_config/pt/README.md)
- 【2024.05.04】基于LLaMA3_8b_instruct的[EmoLLM3.0 OpenXLab Demo](https://st-app-center-006861-9746-jlroxvg.openxlab.space/)上线([重启链接](https://openxlab.org.cn/apps/detail/chg0901/EmoLLM-Llama3-8B-Instruct3.0)), [**LLAMA3微调指南**](xtuner_config/README_llama3_8b_instruct_qlora_alpaca_e3_M.md)**更新**,在[**OpenXLab**](https://openxlab.org.cn/models/detail/chg0901/EmoLLM-Llama3-8B-Instruct3.0)和[**ModelScope**](https://modelscope.cn/models/chg0901/EmoLLM-Llama3-8B-Instruct3.0/summary)平台发布**LLaMA3_8b_instruct-8B QLoRA微调模型 EmoLLM3.0权重**
- 【2024.04.20】[LLAMA3微调指南](xtuner_config/README_llama3_8b_instruct_qlora_alpaca_e3_M.md)及基于[LLaMA3_8b_instruct的艾薇](https://openxlab.org.cn/models/detail/ajupyter/EmoLLM-LLaMA3_8b_instruct_aiwei)开源
- 【2023.04.14】新增[快速开始](docs/quick_start.md)和保姆级教程[BabyEmoLLM](Baby_EmoLLM.ipynb)
- 【2024.04.02】在 Huggingface 上传[老母亲心理咨询师](https://huggingface.co/brycewang2018/EmoLLM-mother/tree/main)
- 【2024.03.25】在百度飞桨平台发布[爹系男友心理咨询师](https://aistudio.baidu.com/community/app/68787)
- 【2024.03.24】在**OpenXLab**和**ModelScope**平台发布**InternLM2-Base-7B QLoRA微调模型**, 具体请查看[**InternLM2-Base-7B QLoRA**](./xtuner_config/README_internlm2_7b_base_qlora.md)
- 【2024.03.12】在百度飞桨平台发布[艾薇](https://aistudio.baidu.com/community/app/63335)
- 【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/)
<details>
<summary>查看更多</summary>
- 【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~
- 【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!
<p align="center">
<img src="https://github.com/SmartFlowAI/EmoLLM/assets/62385492/7e931682-c54d-4ded-bc67-79130c68d744" alt="模型下载量">
</p>
- 【2024.02.05】 项目荣获公众号**NLP工程化**推文宣传[推文链接](https://mp.weixin.qq.com/s/78lrRl2tlXEKUfElnkVx4A),为博主推广一波,欢迎大家关注!!🥳🥳
<p align="center">
<img src="https://github.com/SmartFlowAI/EmoLLM/assets/62385492/47868d6a-2e91-4aa9-a630-e594c14295b4" alt="公众号二维码">
</p>
- 【2024.02.03】 [项目宣传视频](https://www.bilibili.com/video/BV1N7421N76X/)完成 😊
- 【2024.01.27】 完善数据构建文档、微调指南、部署指南、Readme等相关文档 👏
- 【2024.01.25】 EmoLLM V1.0 已部署上线 https://openxlab.org.cn/apps/detail/jujimeizuo/EmoLLM 😀
</details>
## 🏆荣誉栏
- 项目荣获上海人工智能实验室举办的**2024浦源大模型系列挑战赛春季赛*****创新创意奖***
<p align="center">
<a href="https://github.com/SmartFlowAI/EmoLLM/">
<img src="assets/Shusheng.png" alt="浦语挑战赛创新创意奖">
</p>
- 荣获[AI 赋能大学计划“全国高校行”](https://mp.weixin.qq.com/s/yyaulQ1wBzKq5cXaGl2Wag)一等奖
- 🎉感谢以下媒体及公众号朋友对本项目的报道和支持(以下排名不分先后! 若有遗漏、十分抱歉, 一并感激! 欢迎补充!): [NLP工程化](https://mp.weixin.qq.com/s/78lrRl2tlXEKUfElnkVx4A), [机智流](https://mp.weixin.qq.com/s/_wMCmssRMGd0Oz5OVVkjAA), [爱可可爱生活](https://mp.weixin.qq.com/s/4WaCg4OpkCWXEuWHuV4r3w), [阿郎小哥](https://mp.weixin.qq.com/s/_MSMeL1XHP0v5lDi3YaPVw), [大模型日知路](https://mp.weixin.qq.com/s/FYYibsCXtfU6FFM9TuKILA), [AI Code](https://mp.weixin.qq.com/s/yDWGY3S4CwCi6U_irsFmqA) 等!
- 项目宣传视频 [EmoLLM](https://www.bilibili.com/video/BV1N7421N76X/) 已发布,欢迎大家围观 😀
## 🎯路线图
<p align="center">
<a href="https://github.com/SmartFlowAI/EmoLLM/">
<img src="assets/Roadmap_ZH.png" alt="Roadmap_ZH">
</a>
## 🔗框架图
<p align="center">
<a href="https://github.com/SmartFlowAI/EmoLLM/">
<img src="assets/框架图.png" alt="Framework_ZH">
</a>
## 目录
- [EmoLLM-心理健康大模型](#emollm-心理健康大模型)
- [🎇最近更新](#最近更新)
- [🏆荣誉栏](#荣誉栏)
- [🎯路线图](#路线图)
- [🔗框架图](#框架图)
- [目录](#目录)
- [开发前的配置要求](#开发前的配置要求)
- [使用指南](#使用指南)
- [🍪快速体验](#快速体验)
- [📌数据构建](#数据构建)
- [🎨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)
- [使用到的框架](#使用到的框架)
- [如何参与本项目](#如何参与本项目)
- [作者(排名不分先后)](#作者排名不分先后)
- [版权说明](#版权说明)
- [引用](#引用)
- [特别鸣谢](#特别鸣谢)
- [相关项目](#相关项目)
- [人员](#人员)
- [Star History](#star-history)
- [🌟 Contributors](#-contributors)
- [交流群](#交流群)
###### 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
1. Clone the repo
```sh
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)
- [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)
- 查看更多详情
### 🍪快速体验
- 请阅读[快速体验](quick_start/quick_start.md)查阅
- 快速上手:[Baby EmoLLM](quick_start/Baby_EmoLLM.ipynb)
### 📌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)
### 🎨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)
- 详见[RAG](rag/README.md)
### 🎓评测指南
- 本模型评测分为通用评测和专业评测,请阅读[评测指南](evaluate/README.md)查阅
<details>
<summary>更多详情</summary>
### 使用到的框架
- [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
贡献使开源社区成为一个学习、激励和创造的绝佳场所。你所作的任何贡献都是**非常感谢**的。
1. Fork the Project
2. Create your Feature Branch (`git checkout -b feature/AmazingFeature`)
3. Commit your Changes (`git commit -m 'Add some AmazingFeature'`)
4. Push to the Branch (`git push origin feature/AmazingFeature`)
5. Open a Pull Request
</details>
### 作者(排名不分先后)
| 用户名 | 学校/组织 | 备注 | 贡献 |
| :----------------------------------------------------------: | :------------------------------------------------: | :----------------------------------------------------------: | :-------------------------------------------: |
| [aJupyter](https://github.com/aJupyter) | 南开大学在读硕士 | DataWhale成员 | 项目发起人 |
| [MING-ZCH](https://github.com/MING-ZCH) | 华中科技大学在读本科生 | LLM x Mental health 研究者 | 项目联合负责人 |
| [chg0901](https://github.com/chg0901) | 韩国光云大学在读博士 [MiniSora](https://github.com/mini-sora/minisora/) | DataWhale意向成员 DataWhale鲸英助教团成员 | 项目联合负责人 |
| [jujimeizuo](https://github.com/jujimeizuo) | 江南大学在读硕士 | | |
| [Smiling-Weeping-zhr](https://github.com/Smiling-Weeping-zhr) | 哈尔滨工业大学(威海)在读本科生 | | |
| [8baby8](https://github.com/8baby8) | 飞桨领航团区域主管 | 文心大模型核心开发者 | |
| [zxazys](https://github.com/zxazys) | 南开大学在读硕士 | | |
| [JasonLLLLLLLLLLL](https://github.com/JasonLLLLLLLLLLL) | swufe | | |
| [MrCatAI](https://github.com/MrCatAI) | AI搬用工 | | |
| [ZeyuBa](https://github.com/ZeyuBa) | 自动化所在读硕士 | | |
| [aiyinyuedejustin](https://github.com/aiyinyuedejustin) | 宾夕法尼亚大学在读硕士 | | |
| [Nobody-ML](https://github.com/Nobody-ML) | 中国石油大学(华东)在读本科生 | | |
| [Mxoder](https://github.com/Mxoder) | 北京航空航天大学在读本科生 | | |
| [Anooyman](https://github.com/Anooyman) | 南京理工大学硕士 | | |
| [Vicky-3021](https://github.com/Vicky-3021) | 西安电子科技大学硕士(研0) | | |
| [SantiagoTOP](https://github.com/santiagoTOP) | 太原理工大学在读硕士 | | 数据清洗,文档管理、Baby EmoLLM维护 |
| [zealot52099](https://github.com/zealot52099) | 个人开发者 | | 清洗数据、LLM微调、RAG |
| [wwwyfff](https://github.com/wwwyfff) | 复旦大学在读硕士 | | |
| [Yicooong](https://github.com/Yicooong) | 南开大学在读硕士 | | |
| [jkhumor](https://github.com/jkhumor) | 南开大学在读硕士 | | RAG |
| [lll997150986](https://github.com/lll997150986) | 南开大学在读硕士 | | 微调 |
| [nln-maker](https://github.com/nln-maker) | 南开大学在读硕士 | | 前后端开发 |
| [dream00001](https://github.com/dream00001) | 南开大学在读硕士 | | 前后端开发 |
| [王几行XING](https://zhihu.com/people/brycewang1898) | 北京大学硕士毕业 | | 清洗数据、LLM微调、前后端开发 |
| [思在] | 北京大学硕士毕业(微软美国) | | LLM微调、前后端开发 |
| [TingWei](https://github.com/wwewwt) | 电子科技大学硕士毕业 | 微信公众号:AI大模型在手 | 微调 |
| [PengYu](https://github.com/hi-pengyu) | 石河子大学在读硕士 | | LLM微调 |
### 版权说明
该项目签署了 MIT 授权许可,详情请参阅 [LICENSE](https://github.com/SmartFlowAI/EmoLLM/blob/main/LICENSE)
### 引用
如果本项目对您的工作有所帮助,请使用以下格式引用:
```bibtex
@misc{EmoLLM,
title={EmoLLM},
author={EmoLLM},
url={https://github.com/SmartFlowAI/EmoLLM/},
year={2024}
}
```
### 特别鸣谢
#### 相关项目
- [CPsyCoun](https://github.com/CAS-SIAT-XinHai/CPsyCoun)
- [Smile](https://github.com/qiuhuachuan/smile)
- [SoulChat](https://github.com/scutcyr/SoulChat)
#### 人员
- [上海人工智能实验室](https://www.shlab.org.cn/)
- [闻星(浦语小助手)](https://github.com/vansin)
- 阿布(北大心理学硕士)
- [Sanbu](https://github.com/sanbuphy)
- [HatBoy](https://github.com/hatboy)
<!-- links -->
<!-- [linkedin-shield]: https://img.shields.io/badge/-LinkedIn-black.svg?style=flat-square&logo=linkedin&colorB=555 -->
<!-- [linkedin-url]: https://linkedin.com/in/aJupyter -->
## Star History
[![Star History Chart](https://api.star-history.com/svg?repos=SmartFlowAI/EmoLLM&type=Date)](https://star-history.com/#SmartFlowAI/EmoLLM&Date)
## 🌟 Contributors
[![EmoLLM contributors](https://contrib.rocks/image?repo=SmartFlowAI/EmoLLM&max=50)](https://github.com/SmartFlowAI/EmoLLM/graphs/contributors)
[your-project-path]: SmartflowAI/EmoLLM
[contributors-shield]: https://img.shields.io/github/contributors/SmartflowAI/EmoLLM.svg?style=flat-square
[contributors-url]: https://github.com/SmartflowAI/EmoLLM/graphs/contributors
[forks-shield]: https://img.shields.io/github/forks/SmartflowAI/EmoLLM.svg?style=flat-square
[forks-url]: https://github.com/SmartflowAI/EmoLLM/network/members
[stars-shield]: https://img.shields.io/github/stars/SmartflowAI/EmoLLM.svg?style=flat-square
[stars-url]: https://github.com/SmartflowAI/EmoLLM/stargazers
[issues-shield]: https://img.shields.io/github/issues/SmartflowAI/EmoLLM.svg?style=flat-square
[issues-url]: https://img.shields.io/github/issues/SmartflowAI/EmoLLM.svg
[license-shield]: https://img.shields.io/github/license/SmartflowAI/EmoLLM.svg?style=flat-square
[license-url]: https://github.com/SmartFlowAI/EmoLLM/blob/main/LICENSE
[OpenXLab_App-image]: https://cdn-static.openxlab.org.cn/app-center/openxlab_app.svg
[OpenXLab_Model-image]: https://cdn-static.openxlab.org.cn/header/openxlab_models.svg
[OpenXLab_App-url]: https://openxlab.org.cn/apps/detail/Farewell1/EmoLLMV2.0
[OpenXLab_Model-url]: https://openxlab.org.cn/models/detail/ajupyter/EmoLLM_internlm2_7b_full
## 交流群
- 如果失效,请移步Issue区
<p align="center">
<img width="30%" src="https://private-user-images.githubusercontent.com/8240984/324394775-c8e83dac-9ed9-4a19-bb7f-b6bbedc109d9.png?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.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.yfBwgthq3zvmWD2givTJl5w3SMm4O5BeEFwidgG1WpY" alt="EmoLLM官方交流群">
</p>

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