File size: 1,664 Bytes
6afd76a 9c7cba6 6afd76a 9c7cba6 6afd76a 9c7cba6 6afd76a 9c7cba6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 | ---
language: en
license: llama3.2
base_model: meta-llama/Llama-3.2-3B-Instruct
tags:
- medical
- healthcare
- lab-reports
- llama
- qlora
- peft
datasets:
- lavita/ChatDoctor-HealthCareMagic-100k
- lavita/ChatDoctor-iCliniq
---
# MedLLaMA-3.2-3B: AI Lab Report Analyzer
## Model Description
This is a fine-tuned version of [meta-llama/Llama-3.2-3B-Instruct](https://huggingface.co/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')
```
|