language: en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- ruslanmv
- llama
- trl
base_model: unsloth/llama-3-8b-bnb-4bit
datasets:
- ruslanmv/ai-medical-chatbot
Medical-Llama3-8B-16bit: Fine-Tuned Llama3 for Medical Q&A
This repository provides a fine-tuned version of the powerful Llama3 8B model, specifically designed to answer medical questions in an informative way. It leverages the rich knowledge contained in the AI Medical Chatbot dataset (ruslanmv/ai-medical-chatbot).
Model & Development
- Developed by: ruslanmv
- License: Apache-2.0
- Finetuned from model: unsloth/llama-3-8b-bnb-4bit
Key Features
- Medical Focus: Optimized to address health-related inquiries.
- Knowledge Base: Trained on a comprehensive medical chatbot dataset.
- Text Generation: Generates informative and potentially helpful responses.
Installation
This model is accessible through the Hugging Face Transformers library. Install it using pip:
pip install transformers
Usage Example
Here's a Python code snippet demonstrating how to interact with the Medical-Llama3-8B-16bit model and generate answers to your medical questions:
from transformers import AutoTokenizer, AutoModelForCausalLM
# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("ruslanmv/Medical-Llama3-8B-16bit")
model = AutoModelForCausalLM.from_pretrained("ruslanmv/Medical-Llama3-8B-16bit").to("cuda") # If using GPU
# Function to format and generate response with prompt engineering
def askme(question):
medical_prompt = """You are an AI Medical Assistant trained on a vast dataset of health information. Below is a medical question:
Question: {}
Please provide an informative and comprehensive answer:
Answer: """.format(question)
EOS_TOKEN = tokenizer.eos_token
def format_prompt(question):
return medical_prompt + question + EOS_TOKEN
inputs = tokenizer(format_prompt(question), return_tensors="pt").to("cuda") # If using GPU
outputs = model.generate(**inputs, max_new_tokens=64, use_cache=True) # Adjust max_new_tokens for longer responses
answer = tokenizer.batch_decode(outputs)[0].strip()
return answer
# Example usage
question = "What should I do to reduce my weight gained due to genetic hypothyroidism?"
print(askme(question))
Important Note
This model is intended for informational purposes only and should not be used as a substitute for professional medical advice. Always consult with a qualified healthcare provider for any medical concerns.
License
This model is distributed under the Apache License 2.0 (see LICENSE file for details).
Contributing
We welcome contributions to this repository! If you have improvements or suggestions, feel free to create a pull request.
Disclaimer
While we strive to provide informative responses, the accuracy of the model's outputs cannot be guaranteed. It is crucial to consult a doctor or other healthcare professional for definitive medical advice. ```