EmoLLM - Large Language Model for Mental Health

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EmoLLM

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<a href="https://github.com/SmartFlowAI/EmoLLM"><strong>Explore the documentation of this project »</strong></a>






<a href="https://openxlab.org.cn/apps/detail/Farewell1/EmoLLMV2.0">EmoLLM 2.0 Demo</a>
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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:

🎉 Everyone is welcome to contribute to this project!

🔍 Those who are interested in the principles and underlying implementations of LLMs can follow ThinkLLM, which focuses on building various components of large models from scratch.

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.

Recent Updates

• [2025.5] AI Psychological Assistant-Deep Thinking Version (Caryou): EmoLLM-Psychological Digital Person sub project (currently completed deep thinking, Rag, web search, and tts functions) has been merged into EmoLLM. Welcome everyone to participate in the optimization and improvement of the project!
• [2025.5] deepwiki-EmoLLM: can be used for more intelligent project & document understanding based on this project.
• [2025.4] ThinkLLM is a lightweight and efficient implementation repository for large language models, providing a BPE training guide (supporting EmoLLM).
• [2025.3] Based on the full fine-tuning of InternLM2.5-7B-chat, EmoLLM (in GGUF format, fp16 precision) has been released. Updates on how to operate it will be provided later. @Rycen7822 @Slipstream-Max
• [2025.2] Updated the first mental health R1 distillation dataset @Kedreamix
• [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, model weight link: ModelScope
• [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, model weight link: ModelScope
• [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. Model weights are available at: OpenXLab, ModelScope. WebDemo is available at: OpenXLab apps, Full fine-tuning tutorial on Zhihu.
• [2024.07] Welcome to use the stable version of EmoLLM V2.0 for daily use and academic research. Model weight link: OpenXLab.
• [2024.07] Added InternLM2_5_7B_chatfine-tuning configuration、model file ModelScope。
• [2024.06] Added LLaMA-FactoryGLM4-9B-chat fine-tuning guide, added swift-based fine-tuning guide, the paper ESC-Eval: Evaluating Emotion Support Conversations in Large Language Models 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》!
• [2024.05.08] EmoLLMDaddy-like BF V0.1 is public now in 1. Baidu AppBuilder and 2. OpenXLab, welcome to like and add it to your collections!
• [2024.05.07] Incremental Pre-training Guide
• [2024.05.04] EmoLLM3.0 OpenXLab Demo based on LLaMA3_8b_instruct is available now (restart link), LLAMA3 fine-tuning guide is updated, LLaMA3_8b_instruct-8B QLoRA fine-tuning model EmoLLM3.0 weights are released on OpenXLab and ModelScope platforms
• [2024.04.20] LLAMA3 fine-tuning guide and based on LLaMA3_8b_instruct's aiwei open source
• [2023.04.14] Added Quick Start and Nanny level tutorial BabyEmoLLM
• [2024.04.02] Uploaded at Huggingface Old Mother Counsellor
• [2024.03.25] [Mother-like Therapist] is released on Huggingface (https://huggingface.co/brycewang2018/EmoLLM-mother/tree/main)
• [2024.03.25] [Daddy-like Boy-Friend] is released on Baidu Paddle-Paddle AI Studio Platform (https://aistudio.baidu.com/community/app/68787)

View More

• [2024.03.24] The InternLM2-Base-7B QLoRA fine-tuned model has been released on the OpenXLab and **