---
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

<img src="GemmaECG Vision_ Future of Health.png" width="800" />

`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)

```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](https://huggingface.co/datasets/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`](https://huggingface.co/datasets/yasserrmd/pulse-ecg-instruct-subset)



### **Training Loss Summary**

<img src="tl.png" >

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](https://huggingface.co/yasserrmd)




[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)