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convaiinnovations
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Model Details
Model Provider
convaiinnovations
Model Tree
google/medgemma-4b-it
Input Modalities
Output Modalities
Supported Functionality
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%
Hardware:
Hyperparameters:
Training Metrics:
bash
pip install transformers peft pillow torchpython
from transformers import AutoModelForImageTextToText, AutoProcessorfrom peft import PeftModelfrom PIL import Image # Load base modelbase_model_id = "google/medgemma-4b-it"model = AutoModelForImageTextToText.from_pretrained( base_model_id, torch_dtype="auto", device_map="auto") # Load LoRA adaptermodel = PeftModel.from_pretrained( model, "convaiinnovations/medgemma-4b-ecginstruct-lora") # Load processorprocessor = AutoProcessor.from_pretrained(base_model_id)python
# Load ECG imageimage = Image.open("ecg_image.png").convert("RGB") # Prepare promptmessages = [ { "role": "user", "content": [ {"type": "image"}, {"type": "text", "text": "Analyze this ECG and provide a detailed interpretation."} ] }] # Process inputtext = 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()} # Generateoutputs = model.generate( **inputs, max_new_tokens=512, do_sample=False, temperature=None, top_p=None) # Decoderesponse = processor.decode(outputs[0], skip_special_tokens=True)print(response)This model can:
⚠️ 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.
If you use this model, please cite:
bibtex
@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}}}Apache 2.0 (following base model license)
from transformers import AutoModelForImageTextToText, AutoProcessorfrom peft import PeftModelfrom PIL import Image # Load base modelbase_model_id = "google/medgemma-4b-it"model = AutoModelForImageTextToText.from_pretrained( base_model_id, torch_dtype="auto", device_map="auto") # Load LoRA adaptermodel = PeftModel.from_pretrained( model, "convaiinnovations/medgemma-4b-ecginstruct-lora") # Load processorprocessor = AutoProcessor.from_pretrained(base_model_id)# Load ECG imageimage = Image.open("ecg_image.png").convert("RGB") # Prepare promptmessages = [ { "role": "user", "content": [ {"type": "image"}, {"type": "text", "text": "Analyze this ECG and provide a detailed interpretation."} ] }] # Process inputtext = 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()} # Generateoutputs = model.generate( **inputs, max_new_tokens=512, do_sample=False, temperature=None, top_p=None) # Decoderesponse = processor.decode(outputs[0], skip_special_tokens=True)print(response)@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}}}