metadata

datasets:
  - Amod/mental_health_counseling_conversations
base_model:
  - meta-llama/Llama-3.1-8B-Instruct
tags:
  - mental_health

BITShyd: Facial Expression and Mental Health Counseling AI

Project Summary

BITShyd is an advanced AI model that combines facial expression recognition with mental health counseling dialogues, designed to offer empathetic responses based on both visual and conversational cues. This project fine-tunes a conversational AI with the Amod/mental_health_counseling_conversations dataset, adapting it specifically for virtual counseling and emotional support applications.

The model leverages LoRA (Low-Rank Adaptation) and Unsloth fine-tuning techniques for efficient adaptation, making it suitable for use on various hardware setups, from personal devices to cloud-based applications.

Model Details

Model Type: Conversational AI with emotional intelligence features
Dataset: Amod/mental_health_counseling_conversations
Fine-Tuning Methods: LoRA & Unsloth for optimized performance and low latency
Primary Applications: Virtual mental health support, empathetic AI assistants, interactive emotional response models

Key Features

Real-Time Facial Expression Recognition: Capable of identifying emotional expressions such as happiness, sadness, anger, surprise, and neutrality.
Empathetic, Contextually Aware Responses: Trained specifically for counseling-based responses, this model interacts in an emotionally supportive way.
Scalable Fine-Tuning Techniques: LoRA and Unsloth allow for efficient, resource-light tuning, making the model adaptable to different devices.

Quickstart Guide

Here’s how to get started with using this model in your own applications.

Installation and Setup

Install Hugging Face Transformers and Required Libraries:
```
pip install transformers torch
```

Load the Model and Tokenizer

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("LOHAMEIT/BITShyd")
model = AutoModelForCausalLM.from_pretrained("LOHAMEIT/BITShyd")

Preparing Input
- Text Input: This model uses text prompts, ideally incorporating facial expression indicators for contextual awareness.
- Image Input (Optional): For real-time interaction, integrate with a facial expression API to enhance response generation based on user expressions.

Generate a Response

input_text = "Hello, I feel anxious today."
inputs = tokenizer(input_text, return_tensors="pt")
output = model.generate(**inputs, max_length=50)
print(tokenizer.decode(output[0], skip_special_tokens=True))

Usage Examples

1. Mental Health Support Assistant

input_text = "I'm feeling overwhelmed and don't know how to manage my stress."
inputs = tokenizer(input_text, return_tensors="pt")
output = model.generate(**inputs, max_length=50)
print(tokenizer.decode(output[0], skip_special_tokens=True))

Expected Response:

"I'm here for you. It sounds like things are challenging right now. Let's take a deep breath together. Would you like to talk more about what's overwhelming you?"

2. Emotionally Responsive AI Assistant

This example integrates with a facial expression API to adjust responses based on detected emotions (like sadness or happiness).

detected_emotion = "sadness"  # Detected through facial expression analysis
input_text = "I've been feeling lonely lately."
inputs = tokenizer(f"{detected_emotion} | {input_text}", return_tensors="pt")
output = model.generate(**inputs, max_length=50)
print(tokenizer.decode(output[0], skip_special_tokens=True))

Expected Response:

"I'm sorry you're feeling this way. Loneliness can be really tough. Sometimes sharing your feelings can help. I'm here to listen if you'd like to talk."

Model Training and Fine-Tuning Details

This model was trained with LoRA and Unsloth:

LoRA (Low-Rank Adaptation): LoRA enables the model to retain core knowledge while adapting efficiently to new data, making it ideal for nuanced tasks like mental health counseling.
Unsloth: Unsloth enhances inference speed, allowing the model to process requests with lower latency, suitable for real-time interaction scenarios.

Training Configurations:

Parameter	Description
Model Size	8 Billion Parameters
Epochs	3
Learning Rate	5e-5
Batch Size	8
Dataset	Amod/mental_health_counseling_conversations
Optimizations	LoRA and Unsloth

Future Work

Advanced Emotion Detection: Plan to integrate a broader range of emotions and body language cues.
Interactive Widgets: Adding Hugging Face widget for real-time interactions directly on this model’s page.
Deployment Options: Explore integration with cloud-based platforms for widespread access.

License

This model is available under the Apache 2.0 License. For detailed terms, refer to the LICENSE file in the repository.

Explore the Model

Interact with the model here: LOHAMEIT/BITShyd

For any feedback or collaboration requests, feel free to reach out on Hugging Face or GitHub!