Instructions to use convaiinnovations/medgemma-4b-ecginstruct-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use convaiinnovations/medgemma-4b-ecginstruct-lora with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="convaiinnovations/medgemma-4b-ecginstruct-lora")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("convaiinnovations/medgemma-4b-ecginstruct-lora", device_map="auto")

PEFT
How to use convaiinnovations/medgemma-4b-ecginstruct-lora with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use convaiinnovations/medgemma-4b-ecginstruct-lora with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "convaiinnovations/medgemma-4b-ecginstruct-lora"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "convaiinnovations/medgemma-4b-ecginstruct-lora",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/convaiinnovations/medgemma-4b-ecginstruct-lora

SGLang

How to use convaiinnovations/medgemma-4b-ecginstruct-lora with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "convaiinnovations/medgemma-4b-ecginstruct-lora" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "convaiinnovations/medgemma-4b-ecginstruct-lora",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "convaiinnovations/medgemma-4b-ecginstruct-lora" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "convaiinnovations/medgemma-4b-ecginstruct-lora",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use convaiinnovations/medgemma-4b-ecginstruct-lora with Docker Model Runner:
```
docker model run hf.co/convaiinnovations/medgemma-4b-ecginstruct-lora
```

MedGemma-4B ECGInstruct LoRA

Fine-tuned LoRA adapter for Google's MedGemma-4B-it model on the ECGInstruct dataset for automated ECG interpretation.

Model Description

This is a LoRA (Low-Rank Adaptation) fine-tuned version of google/medgemma-4b-it trained on the PULSE-ECG/ECGInstruct dataset containing 1.15M ECG instruction-following examples.

Developed by: convaiinnovations
Base Model: google/medgemma-4b-it
Training Infrastructure: AIRAWAT (C-DAC) - 8x NVIDIA A100 40GB GPUs
Training Duration: ~40 hours (1 epoch)
Final Token Accuracy: 88.23%

Training Details

Training Data

Dataset: PULSE-ECG/ECGInstruct
Samples: 1,154,110 training examples
Image Sources: MIMIC-IV-ECG, PTB-XL, CODE-15%, and other ECG datasets
Task: Vision-language instruction following for ECG interpretation

Training Procedure

Hardware:

8x NVIDIA A100 40GB GPUs (AIRAWAT supercomputer)
Distributed training with PyTorch DDP

Hyperparameters:

LoRA rank (r): 32
LoRA alpha: 64
LoRA dropout: 0.05
Learning rate: 2e-4
Batch size: 128 (effective)
Optimizer: AdamW (fused)
LR scheduler: Cosine with warmup
Precision: bfloat16
Gradient checkpointing: Enabled

Training Metrics:

Final training loss: 10.997
Mean token accuracy: 88.23%
Entropy: 1.796
Total tokens processed: 253,325,537

Usage

Installation

pip install transformers peft pillow torch

Loading the Model

from transformers import AutoModelForImageTextToText, AutoProcessor
from peft import PeftModel
from PIL import Image

# Load base model
base_model_id = "google/medgemma-4b-it"
model = AutoModelForImageTextToText.from_pretrained(
    base_model_id,
    torch_dtype="auto",
    device_map="auto"
)

# Load LoRA adapter
model = PeftModel.from_pretrained(
    model,
    "convaiinnovations/medgemma-4b-ecginstruct-lora"
)

# Load processor
processor = AutoProcessor.from_pretrained(base_model_id)

Inference Example

# Load ECG image
image = Image.open("ecg_image.png").convert("RGB")

# Prepare prompt
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image"},
            {"type": "text", "text": "Analyze this ECG and provide a detailed interpretation."}
        ]
    }
]

# Process input
text = processor.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
inputs = processor(text=[text], images=[[image]], return_tensors="pt", padding=True)
inputs = {k: v.to(model.device) for k, v in inputs.items()}

# Generate
outputs = model.generate(
    **inputs,
    max_new_tokens=512,
    do_sample=False,
    temperature=None,
    top_p=None
)

# Decode
response = processor.decode(outputs[0], skip_special_tokens=True)
print(response)

Model Capabilities

This model can:

✅ Interpret 12-lead ECG images
✅ Identify cardiac abnormalities (arrhythmias, ischemia, hypertrophy, etc.)
✅ Generate detailed clinical reports
✅ Answer questions about ECG findings
✅ Provide diagnostic suggestions

Limitations

Trained primarily on adult ECG data
Should not replace professional medical diagnosis
Performance may vary on ECG formats not seen during training
Requires high-quality ECG images for optimal results

Ethical Considerations

⚠️ Medical Disclaimer: This model is intended for research and educational purposes only. It should not be used as a substitute for professional medical advice, diagnosis, or treatment. Always consult qualified healthcare professionals for medical decisions.

Citation

If you use this model, please cite:

@misc{medgemma-ecginstruct-lora,
  author = {convaiinnovations},
  title = {MedGemma-4B ECGInstruct LoRA},
  year = {2025},
  publisher = {HuggingFace},
  howpublished = {\url{https://huggingface.co/convaiinnovations/medgemma-4b-ecginstruct-lora}}
}

Acknowledgments

Base Model: Google's MedGemma team
Dataset: PULSE-ECG/ECGInstruct
Infrastructure: AIRAWAT AI Innovation Challenge (C-DAC)
Training Framework: HuggingFace Transformers, PEFT, TRL

License

Apache 2.0 (following base model license)

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Model tree for convaiinnovations/medgemma-4b-ecginstruct-lora

Base model

google/gemma-3-4b-pt

Finetuned

google/medgemma-4b-pt