thrishala/mental_health_chatbot

TEXT GENERATIONConcurrency Cost:1Model Size:7BQuant:FP8Ctx Length:4kPublished:Sep 17, 2024Architecture:Transformer0.0K Cold

The thrishala/mental_health_chatbot is a 7 billion parameter language model, fine-tuned from NousResearch/Llama-2-7b-chat-hf, specifically designed for virtual therapy and mental health counseling. It leverages QLoRA for efficient training on a personalized dataset of real-world therapy interactions. This model excels at providing empathetic, personalized, and context-aware responses for mental health support, focusing on issues like anxiety, stress, and personal growth.

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Overview

This model is a specialized 7 billion parameter language model, fine-tuned from the Llama 2 base model (NousResearch/Llama-2-7b-chat-hf), with a focus on virtual therapy and mental health support. It was developed by thrishala using the Quantized Low-Rank Adaptation (QLoRA) technique, which allows for efficient fine-tuning on a personalized dataset of real-world therapy interactions.

Key Capabilities

  • Empathetic and Personalized Responses: Designed to mimic real-world therapy interactions, providing context-aware and empathetic replies.
  • Mental Health Support: Addresses issues such as anxiety, stress, relationships, and personal growth.
  • Efficient Fine-tuning: Utilizes QLoRA for optimized training.

Good for

  • Chatbot Applications: Ideal for conversational interfaces seeking to provide therapeutic or emotional support.
  • Virtual Mental Health Tools: Suited for platforms where empathy and personalization are crucial.
  • Further Specialization: Can be fine-tuned for specific mental health tasks like Cognitive Behavioral Therapy (CBT) or mindfulness coaching.

Limitations and Out-of-Scope Use

It is crucial to understand that this model is not intended for medical diagnoses or crisis intervention. It serves as a support tool and should not replace professional therapy. Users must be informed that it is an AI, and monitoring for sensitive or high-risk conversations is recommended due to potential biases from training data and limitations in complex situations.