|
--- |
|
license: llama3.1 |
|
datasets: |
|
- openlifescienceai/medmcqa |
|
- bigbio/med_qa |
|
- bigbio/pubmed_qa |
|
- empirischtech/med-qa-orpo-dpo |
|
language: |
|
- en |
|
metrics: |
|
- accuracy |
|
base_model: |
|
- meta-llama/Llama-3.1-8B-Instruct |
|
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. |
