This is a fine-tuned version of meta-llama/Llama-3.2-3B-Instruct trained on medical Q&A data to answer patient queries about lab reports and health conditions.

Training Details

Base Model: LLaMA-3.2-3B-Instruct
Method: QLoRA (4-bit quantization + LoRA rank 16)
Dataset: MedQuAD + iCliniq (~10k examples)
Epochs: 2
Hardware: NVIDIA T4 (Google Colab)

Intended Use

Answering patient questions about lab report values
Explaining medical terminology in plain language
Providing general health information

⚠️ Limitations & Disclaimer

This model is for educational and informational purposes only. It is NOT a substitute for professional medical advice, diagnosis, or treatment. Always consult a qualified healthcare provider for medical decisions.

Quick Start

python

from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from peft import PeftModel
import torch

base_model = AutoModelForCausalLM.from_pretrained(
    'meta-llama/Llama-3.2-3B-Instruct',
    quantization_config=BitsAndBytesConfig(load_in_4bit=True),
    device_map='auto'
)
model = PeftModel.from_pretrained(base_model, 'jb10231/MedLLaMA-3.2-3B-LabReport')
tokenizer = AutoTokenizer.from_pretrained('jb10231/MedLLaMA-3.2-3B-LabReport')