---
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

[![Hugging Face](https://img.shields.io/badge/Model-Hugging%20Face-blue)](https://huggingface.co/LOHAMEIT/BITShyd)

### 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](https://huggingface.co/datasets/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

1. **Install Hugging Face Transformers and Required Libraries**:
   ```bash
   pip install transformers torch
   ```

2. **Load the Model and Tokenizer**
   ```python
   from transformers import AutoTokenizer, AutoModelForCausalLM

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

3. **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.

4. **Generate a Response**
   ```python
   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
```python
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).

```python
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](LICENSE.md) file in the repository.

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### Explore the Model

Interact with the model here: [LOHAMEIT/BITShyd](https://huggingface.co/LOHAMEIT/BITShyd) 

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

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