# EmoLLM - Large Languge Model for Mental Health [![Contributors][contributors-shield]][contributors-url] [![Forks][forks-shield]][forks-url] [![Issues][issues-shield]][issues-url] [![MIT License][license-shield]][license-url] [![Stargazers][stars-shield]][stars-url]

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EmoLLM


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**EmoLLM** is a large language model designed to understand, support and help customers in mental health counseling. It is fine-tuned from the LLM instructions. We really appreciate it if you can give it a star~⭐⭐. The open-sourced configuration is as follows: | model | type | | :-------------------: | :------: | | InternLM2_7B_chat | qlora | | InternLM2_1_8B_chat | full finetuning | | Qwen_7b_chat | qlora | | Qwen1_5-0_5B-Chat | full finetuning | | Baichuan2_13B_chat | qlora | | ChatGLM3_6B | lora | | DeepSeek MoE_16B_chat | qlora | | Mixtral 8x7B_instruct | qlora | | …… | …… | Everyone is welcome to contribute to this project ~ --- The Model is aimed at fully understanding and promoting the mental health of individuals, groups, and society. This model typically includes the following key components: - Cognitive factors: Involving an individual's thought patterns, belief systems, cognitive biases, and problem-solving abilities. Cognitive factors significantly impact mental health as they affect how individuals interpret and respond to life events. - Emotional factors: Including emotion regulation, emotional expression, and emotional experiences. Emotional health is a crucial part of mental health, involving how individuals manage and express their emotions and how they recover from negative emotions. - Behavioral factors: Concerning an individual's behavior patterns, habits, and coping strategies. This includes stress management skills, social skills, and self-efficacy, which is the confidence in one's abilities. - Social environment: Comprising external factors such as family, work, community, and cultural background, which have direct and indirect impacts on an individual's mental health. - Physical health: There is a close relationship between physical and mental health. Good physical health can promote mental health and vice versa. - Psychological resilience: Refers to an individual's ability to recover from adversity and adapt. Those with strong psychological resilience can bounce back from challenges and learn and grow from them. - Prevention and intervention measures: The Mental Health Grand Model also includes strategies for preventing psychological issues and promoting mental health, such as psychological education, counseling, therapy, and social support systems. - Assessment and diagnostic tools: Effective promotion of mental health requires scientific tools to assess individuals' psychological states and diagnose potential psychological issues. ### Recent Updates - 【2024.2.23】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) - 【2024.2.23】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. - 【2024.2.18】 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. - 【2024.2.6】 [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~

模型下载量

View More - 【2024.2.5】 The project has been promoted by the official WeChat account NLP Engineering. Here's the [link](https://mp.weixin.qq.com/s/78lrRl2tlXEKUfElnkVx4A) to the article. Welcome everyone to follow!! 🥳🥳

公众号二维码

- 【2024.2.3】 [Project Vedio](https://www.bilibili.com/video/BV1N7421N76X/) at bilibili 😊 - 【2024.1.27】 Complete data construction documentation, fine-tuning guide, deployment guide, Readme, and other related documents 👏 - 【2024.1.25】 Complete the first version of EmoLLM and deploy it online https://openxlab.org.cn/apps/detail/jujimeizuo/EmoLLM 😀
## Contents - [EmoLLM - Large Languge Model for Mental Health](#emollm---large-languge-model-for-mental-health) - [Everyone is welcome to contribute to this project ~](#everyone-is-welcome-to-contribute-to-this-project-) - [Recent Updates](#recent-updates) - [Contents](#contents) - [Pre-development Configuration Requirements.](#pre-development-configuration-requirements) - [**User Guide**](#user-guide) - [File Directory Explanation](#file-directory-explanation) - [Data Construction](#data-construction) - [Fine-tuning Guide](#fine-tuning-guide) - [Deployment Guide](#deployment-guide) - [Frameworks Used](#frameworks-used) - [How to participate in this project](#how-to-participate-in-this-project) - [Version control](#version-control) - [Authors (in no particular order)](#authors-in-no-particular-order) - [Copyright Notice](#copyright-notice) - [Acknowledgments](#acknowledgments) - [Star History](#star-history) - [🌟 Contributors](#-contributors) ###### Pre-development Configuration Requirements. - A100 40G (specifically for InternLM2_7B_chat + qlora fine-tuning + deepspeed zero2 optimization) ###### **User Guide** 1. Clone the repo ```sh git clone https://github.com/aJupyter/EmoLLM.git ``` 1. Read in sequence or read sections you're interested in： - [File Directory Explanation](#文件目录说明) - [Data Construction](#数据构建) - [Fine-tuning Guide](#微调指南) - [Deployment Guide](#部署指南) - View More Details
Additional Details ### File Directory Explanation ``` ├─assets：Image Resources ├─datasets：Dataset ├─demo：demo scripts ├─generate_data：Data Generation Guide │ └─xinghuo ├─scripts：Some Available Tools └─xtuner_config：Fine-tuning Guide └─images ``` ### Data Construction Please read the [Data Construction Guide ](generate_data/tutorial.md)for reference. The dataset used for this fine-tuning can be found at[datasets](datasets/data.json) ### Fine-tuning Guide For details, see the[微调指南](xtuner_config/README.md) ### Deployment Guide For details, see the[部署指南](demo/README.md) ### Frameworks Used - [Xtuner](https://github.com/InternLM/xtuner) - [Transformers](https://github.com/huggingface/transformers) - [Pytorch](https://pytorch.org/) - … #### How to participate in this project Contributions make the open-source community an excellent place for learning, inspiration, and creation. Any contribution you make is greatly appreciated. 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 ### Version control This project uses Git for version control. You can see the current available versions in the repository.
### Authors (in no particular order) [aJupyter](https://github.com/aJupyter)@Datawhale member, Master's student at Nankai University [jujimeizuo](https://github.com/jujimeizuo)@Master's student at Jiangnan University [Smiling&Weeping](https://github.com/Smiling-Weeping-zhr)@Undergraduate student at Harbin Institute of Technology (Weihai) [Farewell](https://github.com/8baby8)@ [ZhouXinAo](https://github.com/zxazys)@Master's student at Nankai University [MING_X](https://github.com/MING-ZCH) @Undergraduate at Huazhong University of Science and Technology. ### Copyright Notice The project is licensed under the MIT License. Please refer to the details [LICENSE](https://github.com/aJupyter/EmoLLM/blob/master/LICENSE) ### Acknowledgments - [Sanbu](https://github.com/sanbuphy) - [Shanghai Artificial Intelligence Laboratory](https://www.shlab.org.cn/) - [Vanin](https://github.com/vansin) - [扫地升(WeChat Official Account Promotion)](https://mp.weixin.qq.com/s/78lrRl2tlXEKUfElnkVx4A) ## Star History [![Star History Chart](https://api.star-history.com/svg?repos=aJupyter/EmoLLM&type=Date)](https://star-history.com/#aJupyter/EmoLLM&Date) ## 🌟 Contributors [![EmoLLM contributors](https://contrib.rocks/image?repo=aJupyter/EmoLLM&max=50)](https://github.com/aJupyter/EmoLLM/graphs/contributors) [your-project-path]: aJupyter/EmoLLM [contributors-shield]: https://img.shields.io/github/contributors/aJupyter/EmoLLM.svg?style=flat-square [contributors-url]: https://github.com/aJupyter/EmoLLM/graphs/contributors [forks-shield]: https://img.shields.io/github/forks/aJupyter/EmoLLM.svg?style=flat-square [forks-url]: https://github.com/aJupyter/EmoLLM/network/members [stars-shield]: https://img.shields.io/github/stars/aJupyter/EmoLLM.svg?style=flat-square [stars-url]: https://github.com/aJupyter/EmoLLM/stargazers [issues-shield]: https://img.shields.io/github/issues/aJupyter/EmoLLM.svg?style=flat-square [issues-url]: https://img.shields.io/github/issues/aJupyter/EmoLLM.svg [license-shield]: https://img.shields.io/github/license/aJupyter/EmoLLM.svg?style=flat-square [license-url]: https://github.com/aJupyter/EmoLLM/blob/main/LICENSE