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EmoLLM - Large Language Model for Mental Health

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Contributors Forks Issues OpenXLab_App OpenXLab_Model MIT License Stargazers

EmoLLM

简体中文 | English | 日本語

Explore the documentation of this project »

EmoLLM 2.0 Demo · Report a Bug · Propose a New Feature

EmoLLM is a series of large language models 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 could give it a star~⭐⭐. The open-sourced configuration is as follows:

Model Type File Links Model Links
InternLM2_5_7B_chat full fine-tuning internlm2_5_chat_7b_full.py OpenXLab, ModelScope
InternLM2_5_7B_chat QLoRA internlm2_5_chat_7b_qlora_oasst1_e3.py ModelScope
InternLM2_7B_chat QLoRA internlm2_7b_chat_qlora_e3.py ModelScope
InternLM2_7B_chat full fine-tuning internlm2_chat_7b_full.py OpenXLab
InternLM2_7B_base QLoRA internlm2_7b_base_qlora_e10_M_1e4_32_64.py OpenXLab, ModelScope
InternLM2_1_8B_chat full fine-tuning internlm2_1_8b_full_alpaca_e3.py OpenXLab, ModelScope
InternLM2_20B_chat LoRA internlm2_20b_chat_lora_alpaca_e3.py
Qwen_7b_chat QLoRA qwen_7b_chat_qlora_e3.py
Qwen1_5-0_5B-Chat full fine-tuning qwen1_5_0_5_B_full.py ModelScope
Baichuan2_13B_chat QLoRA baichuan2_13b_chat_qlora_alpaca_e3.py
ChatGLM3_6B LoRA chatglm3_6b_lora_alpaca_e3.py
DeepSeek MoE_16B_chat QLoRA deepseek_moe_16b_chat_qlora_oasst1_e3.py
Mixtral 8x7B_instruct QLoRA mixtral_8x7b_instruct_qlora_oasst1_e3.py
LLaMA3_8b_instruct QLoRA aiwei_llama3_8b_instruct_qlora_e3.py OpenXLab, ModelScope
LLaMA3_8b_instruct QLoRA llama3_8b_instruct_qlora_alpaca_e3_M_ruozhi_scM.py OpenXLab, ModelScope
Qwen2-7B-Instruct LoRA Qwen2-7B-Instruct_lora.py ModelScope
…… …… …… ……

Everyone is welcome to contribute to this project ~

The Model aims to fully understand and promote 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.
占位图 占位图
占位图 占位图

Recent Updates

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模型下载量

  • [2024.02.05] The project has been promoted by the official WeChat account NLP Engineering. Here's the link to the article. Welcome everyone to follow!! 🥳🥳

公众号二维码

Honors

  • The project won the the Innovation and Creativity Award in the 2024 Puyuan Large Model Series Challenge Spring Competition held by the Shanghai Artificial Intelligence Laboratory

Challenge Innovation and Creativity Award

Roadmap

Roadmap_EN

Contents

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
  1. Clone the repo
git clone https://github.com/SmartFlowAI/EmoLLM.git
  1. Read in sequence or read sections you're interested in：

🍪Quick start

📌Data Construction

🎨Incremental Pre-training and Fine-tuning Guide

🔧Deployment Guide

⚙RAG (Retrieval Augmented Generation)

🎓Evaluation Guide

  • The model evaluation is divided into General Metrics Evaluation and Professional Metrics Evaluation，Please read the evaluation guide for reference.
Additional Details

Frameworks Used

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 currently available versions in the repository.

Authors (in no particular order)

Username School/Organization Remarks Contributions
aJupyter Nankai University, Master's student DataWhale member Project initiator
MING-ZCH Huazhong University of Science and Technology, Undergraduate student LLM X Mental health researcher Project co-leader
chg0901 Ph.D Student of Kwangwoon University in South Korea MiniSora Project co-leader
jujimeizuo Jiangnan University, Master's student
Smiling-Weeping-zhr Harbin Institute of Technology (Weihai), Undergraduate student
8baby8 PaddlePaddle Pilot Team Regional Director Wenxin Large Model core developer
zxazys Nankai University, Master's student
JasonLLLLLLLLLLL SWUFE (Southwestern University of Finance and Economics)
MrCatAI AI Mover
ZeyuBa Institute of Automation, Master's student
aiyinyuedejustin University of Pennsylvania, Master's student
Nobody-ML China University of Petroleum (East China), Undergraduate student
Mxoder Beihang University, Undergraduate student
Anooyman Nanjing University of Science and Technology, Master's student
Vicky-3021 Xidian University, Master's student (Research Year 0)
SantiagoTOP Taiyuan University of Technology, Master's student Data cleansing, document management, Baby EmoLLM maintenance
zealot52099 Individual developer Data Processing, LLM finetuning and RAG
wwwyfff FuDan University, Master's student
jkhumor Nankai University, Master's student RAG
lll997150986 Nankai University, Master's student Fine Tuning
nln-maker Nankai University, Master's student Front-end and back-end development
dream00001 Nankai University, Master's student Front-end and back-end development
王几行XING Peking University, Master's graduate Data Processing, LLM finetuning, Front-end and back-end development
[思在] Peking University, Master's graduate (Microsoft) LLM finetuning, Front-end and back-end development
TingWei University Of Electronic Science And Technology Of China,Master's graduate LLM finetuning
PengYu Shihezi University, Master's student LLM finetuning

Copyright Notice

The project is licensed under the MIT License. Please refer to the details LICENSE

Acknowledgments

Related Projects

People

Star History

Star History Chart

🌟 Contributors

EmoLLM contributors

Communication group

  • If it fails, go to the Issue section.

EmoLLM official communication group