Model Card: Fine-tuned Phi3-mini for Mental Health FAQs

Model Details

Model Name: Phi3-mini-MentalHealth
Base Model: Phi3-mini
Model Type: Language Model
Framework: MLX
Fine-tuning Dataset: Mental Health FAQ data from Kaggle
Format: GGUF (GPT Quantized)

Intended Use

This model is designed to provide empathetic and informative responses to frequently asked questions about mental health. It can be used in applications such as:

Mental health chatbots
Educational resources about mental health
Preliminary mental health screening tools (with appropriate disclaimers)

Training Data

The model was fine-tuned on a dataset of Mental Health FAQs from Kaggle. This dataset likely includes:

Common questions about mental health conditions
Basic information about symptoms and treatments
General mental health and wellness advice

Note: The exact contents and size of the dataset should be verified and added here.

Fine-tuning Process

The Phi3-mini model was fine-tuned using the MLX framework. Specific details about the fine-tuning process, such as number of epochs, learning rate, and other hyperparameters, should be added here.

Performance and Limitations

Improved Empathy: The fine-tuned model demonstrates increased empathy in its responses compared to the base model.
Domain Specificity: The model's knowledge is focused on mental health topics covered in the FAQ dataset.
Limitations:
- The model's knowledge is limited to the scope of the training data.
- It should not be used as a substitute for professional mental health advice or diagnosis.
- The model may not be up-to-date with the latest mental health research or treatments.

Ethical Considerations

Privacy: The model should be used in ways that protect user privacy, especially given the sensitive nature of mental health discussions.
Bias: While efforts have been made to improve empathy, the model may still exhibit biases present in the training data.
Misuse Potential: Care should be taken to prevent the model from being used to provide medical advice it's not qualified to give.

Additional Information

License: Apache 2.0
Developer: Dattaraj Rao - Persistent
Date Created: 16-Jul-2024
Version: 1.0

For more information or to report issues, please contact: https://www.linkedin.com/in/dattarajrao/