Arogya is a fine-tuned version of Gemma 3 (4B) designed to act as a medical question-answering assistant.
It is trained on healthcare-related conversational data to provide general medical guidance.

🧠 Model Details

Base Model: unsloth/gemma-3-4b-it-unsloth-bnb-4bit
Fine-tuning Method: LoRA (Parameter-Efficient Fine-Tuning)
Framework: Unsloth + TRL
Quantization: 4-bit (for memory efficiency)

📊 Training Data

Dataset: lavita/ChatDoctor-HealthCareMagic-100k
Format: Instruction + Input → Response
Converted into chat-style format for Gemma 3

⚙️ Training Configuration

Max Steps: 30 (initial prototype)
Learning Rate: 2e-4
Batch Size: 2 (with gradient accumulation)
Response-only training enabled

💡 Intended Use

Educational purposes
Demonstration of medical chatbot fine-tuning
Research experiments in healthcare AI

⚠️ Limitations & Safety Notice

This model is not a licensed medical professional
Outputs may contain:
- inaccurate or outdated medical advice
- overconfident statements
Do NOT use for real medical decision-making
Always consult a qualified healthcare provider

🚀 Usage Example

python

from unsloth import FastModel

model, tokenizer = FastModel.from_pretrained(
    "your-username/Arogya_fine_tuned",
    load_in_4bit=True,
)

messages = [
    {"role": "user", "content": "I have a headache and fever. What should I do?"}
]

👨‍💻 Author

Name: Thanujaya Tennekoon
Affiliation: University of Ruhuna

📌 Future Improvements

Increase training steps for better accuracy
Add safety-aligned medical instructions
Incorporate Sinhala language support
Filter dataset for higher-quality responses

Thanu10/Arogya_fine_tuned

Arogya Fine-Tuned (Gemma 3 - Medical Chatbot)

📌 Overview