README_Enhanced_MedicalChatBot.md

🩺 MedicalChatBot

MedicalChatBot is a medical domain-focused chatbot fine-tuned using LoRA (Low-Rank Adaptation) on top of mistralai/Mistral-7B-Instruct.
It is designed for health education, medical Q&A, and research use only.

---

📌 Overview

• 🧠 Based on Mistral-7B-Instruct, a powerful instruction-following LLM
• 🔧 Fine-tuned using PEFT + LoRA on a medical dataset
• 📚 Trained on: kberta2014/medical-chat-dataset
• ⚡ Efficient: Only trains adapter layers instead of the full model
• 📦 Deployment-ready: Compatible with Hugging Face transformers, Gradio, and Spaces

---

🧠 Prompt Format

Use the model in the following format:

### Instruction:
<Your question>

### Input:
<Optional additional context>

### Response:

Example:

### Instruction:
What are the symptoms of high blood pressure?

### Input:


### Response:

---

💬 Example Usage

from transformers import pipeline

pipe = pipeline("text-generation", model="kberta2014/MedicalChatBot", tokenizer="kberta2014/MedicalChatBot")

prompt = '''### Instruction:
What are common symptoms of diabetes?

### Input:


### Response:
'''

output = pipe(prompt, max_new_tokens=200, temperature=0.7)
print(output[0]["generated_text"])

---

🤖 Gradio Chatbot Interface

import gradio as gr
from transformers import pipeline

pipe = pipeline("text-generation", model="kberta2014/MedicalChatBot", tokenizer="kberta2014/MedicalChatBot")

def chat(instruction, input_text=""):
    prompt = f"### Instruction:\n{instruction}\n\n### Input:\n{input_text}\n\n### Response:\n"
    return pipe(prompt, max_new_tokens=200, temperature=0.7)[0]["generated_text"]

gr.Interface(fn=chat,
             inputs=["text", "text"],
             outputs="text",
             title="🩺 MedicalChatBot",
             description="Ask medical questions and get responses from a fine-tuned LLM"
).launch()

---

🏋️ Training Configuration

• Model: mistralai/Mistral-7B-Instruct
• Dataset: kberta2014/medical-chat-dataset
• Framework: Hugging Face transformers, peft, datasets
• PEFT Config:
  • r=8, lora_alpha=16, target_modules=["q_proj", "v_proj"]
  • lora_dropout=0.05, bias="none", task_type="CAUSAL_LM"
• Training Time: ~3 epochs on Colab T4
• Batch Size: 2
• Learning Rate: 2e-4
• Precision: bf16 / float16

---

📊 Training Metrics (Sample)

Metric	Value
Training loss	~1.02
Eval loss	~0.94
Perplexity	~2.6
Epochs	3
Trainable params	~7M (LoRA)

---

🧾 Citation

If you use this model in your research or application, please cite:

@misc{medicalchatbot2025,
  title={MedicalChatBot: A LoRA Fine-Tuned Mistral-7B Model for Medical QA},
  author={kberta2014},
  year={2025},
  url={https://huggingface.co/kberta2014/MedicalChatBot},
  note={Hugging Face model repository}
}

---

⚠️ Disclaimer

This model is intended for research and educational purposes only.
It is not a replacement for professional medical advice or diagnosis.
Always consult a licensed healthcare provider for real medical concerns.

---

📄 License

Apache 2.0 — same as the base model mistralai/Mistral-7B-Instruct.