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README.md

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+# Text-to-Text Transfer Transformer (T5) Quantized Model for Medical Chatbot
+
+This repository hosts a quantized version of the T5 model, fine-tuned for Medical Chatbot  tasks. The model has been optimized for efficient deployment while maintaining high accuracy, making it suitable for resource-constrained environments.
+
+## Model Details
+- **Model Architecture:** T5  
+- **Task:** Medical Chatbot  
+- **Dataset:** Hugging Face's ‘medical-qa-datasets’
+ 
+- **Quantization:** Float16
+- **Fine-tuning Framework:** Hugging Face Transformers  
+
+## Usage
+### Installation
+```sh
+pip install transformers torch
+```
+
+### Loading the Model
+```python
+from transformers import T5Tokenizer, T5ForConditionalGeneration
+import torch
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+model_name = "AventIQ-AI/t5-medical-chatbot”
+tokenizer = T5Tokenizer.from_pretrained(model_name)
+model = T5ForConditionalGeneration.from_pretrained(model_name).to(device)
+
+def test_medical_t5(instruction, input_text, model, tokenizer):
+    """Format input like the training dataset and test the quantized model."""
+    formatted_input = f"Instruction: {instruction} Input: {input_text}"
+ 
+    # ✅ Tokenize input & move to correct device
+    inputs = tokenizer(
+        formatted_input, return_tensors="pt", padding=True, truncation=True, max_length=512
+    ).to(device)
+ 
+    # ✅ Generate response with optimized settings
+    with torch.no_grad():
+        outputs = model.generate(
+            input_ids=inputs["input_ids"],  # Explicitly specify input tensor
+            attention_mask=inputs["attention_mask"],
+            max_length=200,
+            num_return_sequences=1,
+            temperature=0.6,
+            top_k=40,
+            top_p=0.85,
+            repetition_penalty=2.0,
+            no_repeat_ngram_size=3,
+            early_stopping=True
+        )
+ 
+    # ✅ Decode output
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return response
+
+
+# Test Example
+instruction = "As a medical expert, provide a detailed and accurate diagnosis based on the patient's symptoms."
+input_text = "A patient is experiencing persistent hair fall, dizziness, and nausea. What could be the underlying cause and recommended next steps?"
+```
+
+## 📊 ROUGE Evaluation Results
+After fine-tuning the T5-Small model for Medical Chatbot, we obtained the following ROUGE scores:
+
+| **Metric**  | **Score** | **Meaning**  |
+|------------|---------|--------------------------------------------------------------|
+| **ROUGE-1**  | 1.0 (~100%) | Measures overlap of unigrams (single words) between the reference and generated text. |
+| **ROUGE-2**  | 0.5 (~50%) | Measures overlap of bigrams (two-word phrases), indicating coherence and fluency. |
+| **ROUGE-L**  | 1.0 (~100%) | Measures longest matching word sequences, testing sentence structure preservation. |
+| **ROUGE-Lsum**  | 0.95 (~95%) | Similar to ROUGE-L but optimized for summarization tasks. |
+
+## Fine-Tuning Details
+### Dataset
+The Hugging Face's `medical-qa-datasets’ dataset was used, containing different types of Patient and Doctor Questions and respective Answers.
+
+### Training
+- **Number of epochs:** 3  
+- **Batch size:** 8  
+- **Evaluation strategy:** epoch  
+
+### Quantization
+Post-training quantization was applied using PyTorch's built-in quantization framework to reduce the model size and improve inference efficiency.
+
+## Repository Structure
+```
+.
+├── model/               # Contains the quantized model files
+├── tokenizer_config/    # Tokenizer configuration and vocabulary files
+├── model.safetensors/   # Quantized Model
+├── README.md            # Model documentation
+```
+
+## Limitations
+- The model may not generalize well to domains outside the fine-tuning dataset.
+- Currently, it only supports English to French translations.
+- Quantization may result in minor accuracy degradation compared to full-precision models.
+
+## Contributing
+Contributions are welcome! Feel free to open an issue or submit a pull request if you have suggestions or improvements.