readme.md

🩺 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

# 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