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# 🩺 TinyLlama Medical Assistant
A fine-tuned TinyLlama 1.1B model specialized in allopathic medicine, trained using LoRA (Low-Rank Adaptation) on a custom medical dataset.
## 🎯 Features
- **Fine-tuned Model**: Specialized knowledge of 10+ common medicines
- **LoRA Adaptation**: Efficient fine-tuning with only 2.2M trainable parameters
- **4-bit Quantization**: Memory-efficient inference
- **User Authentication**: Role-based access (admin, doctor, student)
- **Medical Disclaimer**: Safety warnings on all responses
- **Interactive UI**: Clean Streamlit interface with adjustable parameters
## 📊 Model Details
- **Base Model**: TinyLlama-1.1B-Chat-v1.0
- **Fine-tuning Method**: LoRA (r=8, alpha=16)
- **Training Data**: 500 medical Q&A pairs
- **Training Accuracy**: 97.83%
- **Medicines Covered**: Paracetamol, Ibuprofen, Amoxicillin, Metformin, Atorvastatin, Amlodipine, Omeprazole, Cetirizine, Azithromycin, Losartan
## 🚀 Quick Start
### Local Installation
```bash
# Clone the repository
git clone https://github.com/yourusername/medical-assistant.git
cd medical-assistant
# Install dependencies
pip install -r requirements.txt
# Run the app
streamlit run app.py
```
### Login Credentials
```
Username: admin Password: admin123
Username: doctor Password: doc123
Username: student Password: student123
```
## 📁 Project Structure
```
medical-assistant/
├── app.py # Main Streamlit application
├── requirements.txt # Python dependencies
├── .streamlit/
│ └── config.toml # Streamlit configuration
├── tinyllama-medical-lora/ # Fine-tuned model weights
│ ├── adapter_config.json
│ ├── adapter_model.safetensors
│ └── tokenizer files...
└── README.md
```
## 💡 Example Queries
- "What is Paracetamol used for?"
- "Tell me about Ibuprofen"
- "What is Metformin?"
- "Uses of Amoxicillin"
- "What is Atorvastatin for?"
## ⚙️ Model Parameters
Adjust these in the sidebar:
- **Temperature** (0.1-1.5): Controls randomness
- **Max Tokens** (32-256): Response length
- **Top-p** (0.1-1.0): Nucleus sampling
## ⚠️ Medical Disclaimer
This AI assistant is for educational purposes only. Always consult a qualified healthcare provider for medical advice.
## 🔧 Technical Stack
- **Framework**: Streamlit
- **Model**: TinyLlama 1.1B + LoRA
- **Libraries**: Transformers, PEFT, BitsAndBytes, PyTorch
- **Quantization**: 4-bit NF4
## 📄 License
MIT License
## 👥 Authors
Your Name - Medical AI Research
## 🙏 Acknowledgments
- TinyLlama team for the base model
- Hugging Face for transformers library
- PEFT library for LoRA implementation
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