OussamaEL/MedGemma-4B-ECG

MedGemma-4B ECG Report Generator

This is a fully merged, standalone model fine-tuned from unsloth/medgemma-4b-pt for ECG interpretation and clinical report generation. It was trained using the Unsloth library for high-efficiency, memory-optimized fine-tuning.

This model is designed to take structured output from a primary ML classifier (which provides findings like "Atrial Fibrillation: 82% confidence, Present") and synthesize it into a coherent, human-readable clinical report, complete with an impression, detailed analysis, and clinical recommendations.

Model Details

• Base Model: unsloth/medgemma-4b-pt
• Fine-tuning Method: Unsloth + LoRA (merged into base model)
• Training Data: 500 curated ECG interpretation examples.
• Evaluation Score: The model achieved an average structural correctness score of 1.000 / 1.0 on a hold-out set.

Usage

This model follows a standard instruction format. Provide the instruction and the structured input to get a clinical report.

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_name = "OussamaEL/MedGemma-4B-ECG"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

# Alpaca prompt format is required
alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
### Instruction:
{}
### Input:
{}
### Response:
{}"""

instruction = "You are a medical AI assistant specializing in ECG interpretation. Analyze the ECG findings and patient context to generate a clinical report."

input_text = """ECG FINDINGS:
- Atrial Fibrillation (AFIB): 95% confidence, Present
- Sinus Tachycardia (STACH): 88% confidence, Present

PATIENT CONTEXT:
68-year-old male with diabetes and hypertension presents with 2 days of worsening shortness of breath and leg swelling."""

inputs = tokenizer(
    alpaca_prompt.format(instruction, input_text, ""),
    return_tensors="pt"
).to("cuda")

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True).split("### Response:")[1].strip())

This model is intended for research and development purposes and is not a substitute for professional medical advice.