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---
datasets:
- Amod/mental_health_counseling_conversations
base_model:
- meta-llama/Llama-3.1-8B-Instruct
tags:
- mental_health
---
Here’s a `README.md` file tailored for your AI project on Hugging Face. This README assumes your model is designed for facial expression recognition with fine-tuning on mental health counseling conversations. Make sure to replace placeholders like `YOUR_USERNAME` with actual details as needed.
---
# Facial Expression and Mental Health Counseling AI Model
[](https://huggingface.co/LOHAMEIT/BITShyd)
### Project Overview
This AI model combines **facial expression recognition** with **mental health counseling-focused dialogue generation**. Fine-tuned on the `Amod/mental_health_counseling_conversations` dataset using **LoRA** (Low-Rank Adaptation) and **Unsloth**, this model is designed to offer empathetic responses based on visual and conversational cues, suitable for virtual counselors or mental health assistants.
Key capabilities:
- **Real-time Emotion Recognition** from facial expressions
- **Contextually Relevant Responses** in a supportive, conversational tone
### Model Summary
- **Model Type**: Conversational AI with facial expression support
- **Training Dataset**: [Amod/mental_health_counseling_conversations](https://huggingface.co/datasets/Amod/mental_health_counseling_conversations)
- **Fine-Tuning Techniques**: LoRA and Unsloth for efficient, optimized adaptation
- **Usage Applications**: Mental health support, virtual assistants, interactive emotional AI
---
## Quick Start
1. **Load the Model**
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("LOHAMEIT/BITShyd")
model = AutoModelForCausalLM.from_pretrained("LOHAMEIT/BITShyd")
```
2. **Prepare the Input**
- Ensure the input text or image follows the required pre-processing steps for facial expression recognition.
- Use `transformers` for text and facial expression embeddings to create a blended emotional context.
3. **Generate a Response**
```python
inputs = tokenizer("User input text here", return_tensors="pt")
output = model.generate(**inputs)
print(tokenizer.decode(output[0], skip_special_tokens=True))
```
---
### Training and Fine-Tuning
This model was fine-tuned with **LoRA** and **Unsloth**:
- **LoRA** enables efficient training with limited resources by reducing the dimensionality of model parameters, while retaining high accuracy.
- **Unsloth** minimizes latency and optimizes response generation, improving the model's suitability for real-time applications.
1. **Install LoRA & Unsloth**:
```bash
pip install lora unsloth
```
2. **Fine-Tune on Custom Dataset** (if desired):
```python
from lora import LoraTrainer
trainer = LoraTrainer(model, dataset="Amod/mental_health_counseling_conversations")
trainer.train()
```
### Model Details
| Parameter | Description |
|-----------------|------------------------------------|
| Model Size | 8 Billion Parameters |
| Fine-Tuning | LoRA + Unsloth |
| Dataset | Amod/mental_health_counseling_conversations |
| Primary Use | Mental Health AI, Virtual Support |
### Example Use Case
The model is designed to recognize and interpret facial expressions alongside counseling conversations. This interaction facilitates emotionally supportive responses, tailored for user needs in mental health applications or personal emotional assistants.
---
## License
This model and dataset are licensed for non-commercial use. For more details, see [LICENSE](LICENSE.md).
---
Explore the model on Hugging Face: [LOHAMEIT/BITShyd](https://huggingface.co/LOHAMEIT/BITShyd)
--- |