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datasets: |
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- Amod/mental_health_counseling_conversations |
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base_model: |
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- meta-llama/Llama-3.1-8B-Instruct |
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tags: |
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- mental_health |
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--- |
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--- |
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# BITShyd: Facial Expression and Mental Health Counseling AI |
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[](https://huggingface.co/LOHAMEIT/BITShyd) |
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### Project Summary |
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**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. |
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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. |
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## Model Details |
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- **Model Type**: Conversational AI with emotional intelligence features |
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- **Dataset**: [Amod/mental_health_counseling_conversations](https://huggingface.co/datasets/Amod/mental_health_counseling_conversations) |
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- **Fine-Tuning Methods**: LoRA & Unsloth for optimized performance and low latency |
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- **Primary Applications**: Virtual mental health support, empathetic AI assistants, interactive emotional response models |
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## Key Features |
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- **Real-Time Facial Expression Recognition**: Capable of identifying emotional expressions such as happiness, sadness, anger, surprise, and neutrality. |
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- **Empathetic, Contextually Aware Responses**: Trained specifically for counseling-based responses, this model interacts in an emotionally supportive way. |
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- **Scalable Fine-Tuning Techniques**: LoRA and Unsloth allow for efficient, resource-light tuning, making the model adaptable to different devices. |
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## Quickstart Guide |
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Here’s how to get started with using this model in your own applications. |
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### Installation and Setup |
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1. **Install Hugging Face Transformers and Required Libraries**: |
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```bash |
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pip install transformers torch |
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``` |
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2. **Load the Model and Tokenizer** |
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```python |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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tokenizer = AutoTokenizer.from_pretrained("LOHAMEIT/BITShyd") |
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model = AutoModelForCausalLM.from_pretrained("LOHAMEIT/BITShyd") |
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``` |
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3. **Preparing Input** |
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- **Text Input**: This model uses text prompts, ideally incorporating facial expression indicators for contextual awareness. |
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- **Image Input** (Optional): For real-time interaction, integrate with a facial expression API to enhance response generation based on user expressions. |
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4. **Generate a Response** |
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```python |
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input_text = "Hello, I feel anxious today." |
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inputs = tokenizer(input_text, return_tensors="pt") |
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output = model.generate(**inputs, max_length=50) |
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print(tokenizer.decode(output[0], skip_special_tokens=True)) |
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``` |
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## Usage Examples |
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### 1. Mental Health Support Assistant |
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```python |
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input_text = "I'm feeling overwhelmed and don't know how to manage my stress." |
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inputs = tokenizer(input_text, return_tensors="pt") |
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output = model.generate(**inputs, max_length=50) |
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print(tokenizer.decode(output[0], skip_special_tokens=True)) |
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``` |
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Expected Response: |
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> "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?" |
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### 2. Emotionally Responsive AI Assistant |
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This example integrates with a facial expression API to adjust responses based on detected emotions (like sadness or happiness). |
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```python |
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detected_emotion = "sadness" # Detected through facial expression analysis |
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input_text = "I've been feeling lonely lately." |
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inputs = tokenizer(f"{detected_emotion} | {input_text}", return_tensors="pt") |
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output = model.generate(**inputs, max_length=50) |
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print(tokenizer.decode(output[0], skip_special_tokens=True)) |
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``` |
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Expected Response: |
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> "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." |
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## Model Training and Fine-Tuning Details |
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This model was trained with **LoRA** and **Unsloth**: |
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- **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. |
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- **Unsloth**: Unsloth enhances inference speed, allowing the model to process requests with lower latency, suitable for real-time interaction scenarios. |
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Training Configurations: |
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| Parameter | Description | |
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|-----------------|-------------------------------------| |
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| Model Size | 8 Billion Parameters | |
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| Epochs | 3 | |
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| Learning Rate | 5e-5 | |
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| Batch Size | 8 | |
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| Dataset | Amod/mental_health_counseling_conversations | |
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| Optimizations | LoRA and Unsloth | |
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## Future Work |
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- **Advanced Emotion Detection**: Plan to integrate a broader range of emotions and body language cues. |
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- **Interactive Widgets**: Adding Hugging Face widget for real-time interactions directly on this model’s page. |
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- **Deployment Options**: Explore integration with cloud-based platforms for widespread access. |
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## License |
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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 |
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Interact with the model here: [LOHAMEIT/BITShyd](https://huggingface.co/LOHAMEIT/BITShyd) |
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For any feedback or collaboration requests, feel free to reach out on Hugging Face or GitHub! |
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