

Emollm
Overview :
EmoLLM is a mental health large language model fine-tuned from LLM instructions, designed to comprehensively understand and promote the mental health status of individuals, groups, and even entire societies. It encompasses key components such as cognitive factors, emotional factors, behavioral factors, social environment, physical health, psychological resilience, preventative and intervention measures, and assessment and diagnostic tools. Through fine-tuning configurations, EmoLLM can provide support in mental health counseling tasks, helping users better understand and cope with psychological issues.
Target Users :
EmoLLM is suitable for mental health professionals, researchers, and the general public interested in mental health. Professionals and researchers can leverage it to provide more specialized mental health counseling services, while the general public can utilize it to gain knowledge about mental health and self-regulation techniques.
Use Cases
Mental health professionals use EmoLLM to provide online mental health counseling services to patients.
Researchers utilize EmoLLM to assess and research mental health status.
The general public uses EmoLLM to learn about mental health and improve their self-regulation abilities.
Features
Supports understanding user needs and providing support throughout the user's mental health counseling journey
Fine-tuned from LLM instructions, providing support for mental health counseling tasks
Encompasses cognitive, emotional, and behavioral aspects of mental health
Provides multi-round dialogue datasets CPsyCounD and professional evaluation methods
Supports incremental pre-training and various fine-tuning configurations, such as InternLM2_5_7B_chat
Released on multiple platforms like ModelScope and OpenXLab, making it accessible for users
Received multiple awards and media coverage, demonstrating its value and impact in the mental health field
How to Use
Visit EmoLLM's GitHub page to learn about the project background and details.
Choose a suitable fine-tuning configuration based on your needs, such as InternLM2_5_7B_chat.
Read the quick start guide to learn how to quickly get started using EmoLLM.
Refer to the data preparation guide to prepare and organize the required mental health dataset.
Fine-tune the model according to the fine-tuning guide to adapt it to specific mental health counseling tasks.
Deploy the fine-tuned model to servers or cloud platforms using the deployment guide.
Evaluate the model's performance using the evaluation guide to ensure its effectiveness in practical applications.
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