RREADME.md

MedicalChatBot is a state-of-the-art, domain-specialized chatbot fine-tuned on high-quality medical dialogue and question-answer datasets using LoRA (Low-Rank Adaptation) on top of the Mistral-7B-Instruct large language model. It is designed to provide accurate, natural language responses to medical and health-related queries for educational, research, and health literacy use cases.

🚀 Key Features
Foundation Model: Built on mistralai/Mistral-7B-Instruct, a powerful open-weight instruction-tuned LLM with strong zero-shot reasoning capabilities.
LoRA Fine-Tuning: Efficient fine-tuning method using PEFT that adapts only a small number of trainable parameters (~7 million), enabling training even on low-resource environments like Google Colab.
Domain Data: Trained on kberta2014/medical-chat-dataset, a curated dataset composed of patient-symptom-dialogs, condition-based queries, and verified health questions.
Optimized for Inference: Lightweight, fast, and easily deployable in inference pipelines or interactive chatbot interfaces like Gradio.
Ethical by Design: Returns informative, non-diagnostic answers and includes disclaimers in accordance with medical AI safety standards.
📚 Training Objectives
The goal of MedicalChatBot is to:

Improve accessibility to trustworthy medical information using conversational AI.
Support healthcare education for students, patients, and researchers.
Serve as a baseline for future domain-adapted large language models in healthcare.
Evaluate instruction-tuned LLMs in medical NLP tasks such as question answering, summarization, and dialogue understanding.
🛠️ Technical Details
Model Architecture: Decoder-only transformer (Mistral-7B)
Training Method: Parameter-Efficient Fine-Tuning using LoRA
PEFT Config:
r=8, lora_alpha=16
lora_dropout=0.05
target_modules=["q_proj", "v_proj"]
Prompt Format: Instruction-based input (Alpaca-style)
Batch Size: 2 (Google Colab T4)
Epochs: 3
Precision: bfloat16/float16
Tokenizer: Mistral tokenizer
🧪 Example Prompts
Instruction: What are common symptoms of asthma?
Response: Asthma symptoms include wheezing, coughing (especially at night), shortness of breath, and chest tightness...

🧠 Use Cases
Category Examples
Medical Q&A “What are the signs of a stroke?”
Symptom Explanation “What does chest pain usually indicate?”
Health Literacy “Explain diabetes in simple terms.”
Patient Education “How should I prepare for an MRI?”
Research Prototyping Testing domain-specific NLP for clinical AI models
⚠️ Responsible Use
This model is not intended for diagnosis, treatment, or clinical decision-making. It should be used only for educational or research purposes. Responses are generated by a language model and are not a substitute for professional medical advice.

📈 Future Work
Integrate with speech-to-text for voice-based medical assistants.
Expand dataset coverage to include multilingual and region-specific healthcare content.
Evaluate with benchmark datasets like MedQA, MedMCQA, or PubMedQA.
Explore RLHF or supervised fine-tuning with doctor-verified output

README_Enhanced_MedicalChatBot.md

@@ -0,0 +1,142 @@
+# 🩺 MedicalChatBot
+
+**MedicalChatBot** is a medical domain-focused chatbot fine-tuned using **LoRA (Low-Rank Adaptation)** on top of [`mistralai/Mistral-7B-Instruct`](https://huggingface.co/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](https://github.com/huggingface/peft) + LoRA on a medical dataset
+- 📚 Trained on: [`kberta2014/medical-chat-dataset`](https://huggingface.co/datasets/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
+
+```python
+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
+
+```python
+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`](https://huggingface.co/datasets/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:
+
+```bibtex
+@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`.