base_model:
- google/gemma-3n-E2B-it
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3n
- medical
- vision-language
- gemma
- ecg
- cardiology
- healthcare
license: cc-by-4.0
datasets:
- yasserrmd/pulse-ecg-instruct-subset
language:
- en
GemmaECG-Vision
GemmaECG-Vision is a fine-tuned vision-language model built on google/gemma-3n-e2b, designed for ECG image interpretation tasks. The model accepts a medical ECG image along with a clinical instruction prompt and generates a structured analysis suitable for triage or documentation use cases.
This model was developed using Unsloth for efficient fine-tuning and supports image + text inputs with medical task-specific prompt formatting. It is designed to run in offline or edge environments, enabling healthcare triage in resource-constrained settings.
Model Objective
To assist healthcare professionals and emergency responders by providing AI-generated ECG analysis directly from medical images, without requiring internet access or cloud resources.
Usage
This model expects:
- An ECG image (
PIL.Image) - A textual instruction such as:
You are a clinical assistant specialized in ECG interpretation. Given an ECG image, generate a concise, structured, and medically accurate report.
Use this exact format:
Rhythm:
PR Interval:
QRS Duration:
Axis:
Bundle Branch Blocks:
Atrial Abnormalities:
Ventricular Hypertrophy:
Q Wave or QS Complexes:
T Wave Abnormalities:
ST Segment Changes:
Final Impression:
Inference Example (Python)
from transformers import AutoProcessor, Gemma3nForConditionalGeneration
from PIL import Image
import torch
model_id = "yasserrmd/GemmaECG-Vision"
model = Gemma3nForConditionalGeneration.from_pretrained(model_id, torch_dtype=torch.bfloat16).eval().to("cuda")
processor = AutoProcessor.from_pretrained(model_id)
image = Image.open("example_ecg.png").convert("RGB")
messages = [
{
"role": "user",
"content": [
{"type": "image"},
{"type": "text", "text": "Interpret this ECG and provide a structured triage report."}
]
}
]
prompt = processor.apply_chat_template(messages, add_generation_prompt=True)
inputs = processor(image, prompt, return_tensors="pt").to("cuda")
outputs = model.generate(
**inputs,
max_new_tokens=256,
temperature=1.0,
top_p=0.95,
top_k=64,
use_cache=True
)
result = processor.decode(outputs[0], skip_special_tokens=True)
print(result)
Training Details
- Framework: Unsloth + TRL SFTTrainer
- Hardware: Google Colab Pro (L4)
- Batch Size: 2
- Epochs: 1
- Learning Rate: 2e-4
- Scheduler: Cosine
- Loss: CrossEntropy
- Precision: bfloat16
Dataset
The training dataset is a curated subset of the PULSE-ECG/ECGInstruct dataset, reformatted for VLM instruction tuning.
- 3,272 samples of ECG image + structured instruction + clinical output
- Focused on realistic and medically relevant triage cases
Dataset link: yasserrmd/pulse-ecg-instruct-subset
Training Loss Summary
The model was fine-tuned over 409 steps using the pulse-ecg-instruct-subset dataset. The training loss started above 9.5 and steadily declined to below 0.5, showing consistent convergence and learning throughout the single epoch. The loss curve demonstrates a stable optimization process without overfitting spikes. The chart below visualizes this progression, highlighting the model’s ability to adapt quickly to the ECG image-to-text task.
Intended Use
- Emergency triage in offline settings
- On-device ECG assessment
- Integration with medical edge devices (Jetson, Pi, Android)
- Rapid analysis during disaster response
Limitations
- Not intended to replace licensed medical professionals
- Accuracy may vary depending on image quality
- Model outputs should be reviewed by a clinician before action
License
This model is licensed under CC BY 4.0. You are free to use, modify, and distribute it with attribution.
Author
Mohamed Yasser Hugging Face Profile
