safetensors llama medical climate biology chemistry en dataset:openlifescienceai/medmcqa dataset:bigbio/med_qa dataset:bigbio/pubmed_qa dataset:empirischtech/med-qa-orpo-dpo base_model:meta-llama/Llama-3.1-8B-Instruct base_model:finetune:meta-llama/Llama-3.1-8B-Instruct license:llama3.1 region:us
---
base_model:
- meta-llama/Llama-3.1-8B-Instruct
datasets:
- openlifescienceai/medmcqa
- bigbio/med_qa
- bigbio/pubmed_qa
- empirischtech/med-qa-orpo-dpo
language:
- en
license: llama3.1
metrics:
- accuracy
tags:
- medical
- climate
- biology
- chemistry
---

# Llama-3.1-8B Medical Fine-Tuned Model

## Overview
This is a **fine-tuned version of Llama-3.1-8B** trained on a specialized **medical dataset** to enhance accuracy and contextual understanding in healthcare-related queries. The model has been optimized to provide **precise and reliable answers** to medical questions while improving performance in topic tagging and sentiment analysis.

## Features
- **Medical Question Answering**: Improved capability to understand and respond to medical inquiries with domain-specific knowledge.
- **Topic Tagging**: Enhanced ability to categorize medical content into relevant topics for better organization and retrieval.
- **Sentiment Analysis**: Tuned to assess emotional tone in medical discussions, making it useful for patient feedback analysis and clinical communication.

## Use Cases
- **Clinical Decision Support**: Assisting healthcare professionals in retrieving relevant medical insights.
- **Medical Chatbots**: Providing accurate and context-aware responses to patient queries.
- **Healthcare Content Analysis**: Extracting key topics and sentiments from medical literature, patient reviews, and discussions.

## Model Details
- **Base Model**: Llama-3.1-8B
- **Fine-Tuning Dataset**: Curated medical literature, clinical case studies, and healthcare FAQs
- **Task-Specific Training**: Trained with reinforcement learning and domain-specific optimizations

## Installation & Usage
```python
from transformers import AutoModel, AutoTokenizer

model_name = "empirischtech/Llama-3.1-8B-Instruct-MedQA"

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name)

# Example usage
text = "What are the symptoms of diabetes?"
inputs = tokenizer(text, return_tensors="pt")
outputs = model(**inputs)
```

## License
This model is intended for research and educational purposes. Please review the licensing terms before commercial use.

## Acknowledgments
We acknowledge the contributions of medical professionals and researchers who provided valuable insights for fine-tuning this model.

---
**Disclaimer**: This model is not a substitute for professional medical advice. Always consult a healthcare provider for clinical decisions.