uaritm/medgemma-4b-cardiology-gguf

MedGemma-4B Cardiology (GGUF)

uaritm — Domain-Finetuned Clinical LLM for Cardiology Based on Google MedGemma-4B-IT

Model Description

MedGemma-4B-Cardiology is a domain-adapted medical language model built on google/medgemma-4b-it and fine-tuned via supervised instruction tuning (SFT) on anonymized cardiology clinical data:

• —discharge summaries
• —history of illness
• —surgical procedures
• —treatment pathways
• —physician recommendations

The model is optimized for:

• —generating clinically grounded cardiology recommendations
• —structuring and summarizing medical documentation
• —rewriting clinical narratives
• —assisting in cardiology decision support

GGUF format ensures compatibility with:

• —llama.cpp
• —Ollama
• —LM Studio
• —GPT4All
• —koboldcpp

Architecture

• —Base model: Google MedGemma-4B-IT
• —Parameters: ~4B
• —Fine-tuning: LoRA + SFT
• —Precision: Q4KM / Q5KM GGUF
• —Context window: up to 4096 tokens

Repository Contents

medgemma-4b-cardiology.Q4KM.gguf medgemma-4b-cardiology.Q5KM.gguf tokenizer.json README.md

Usage Examples

llama.cpp

./main -m medgemma-4b-cardiology.Q4KM.gguf \ -p "You are a cardiologist. Based on the clinical details, generate treatment recommendations."

Ollama

Create a file named Modelfile:

FROM ./medgemma-4b-cardiology.Q4KM.gguf TEMPLATE "<startofturn>user\n{{ .Prompt }}\n<endofturn>\n<startofturn>assistant" PARAMETER temperature 0.3

Then run:

ollama create medcardio -f Modelfile ollama run medcardio

Python (llama-cpp-python)

from llama_cpp import Llama

llm = Llama( modelpath="medgemma-4b-cardiology.Q4KM.gguf", nctx=4096, ngpulayers=35, )

prompt = "You are a cardiologist. Patient: 67-year-old male with stable angina. Provide evidence-based recommendations."

output = llm(prompt, max_tokens=400) print(output["choices"][0]["text"])

Prompt Format (MedGemma Chat Template)

The model expects chat-structured input:

<startofturn>user [Your instruction or clinical text] <endofturn> <startofturn>assistant

Example:

<startofturn>user You are a cardiologist. Generate recommendations for: Sex: Male Age: 67 Length of stay: 10 days [discharge summary...] <endofturn> <startofturn>assistant

Fine-Tuning Details

Training dataset included >12,000 cardiology-specific instruction-response pairs:

{ "prompt": "...", "response": "..." }

Converted into Gemma-style messages:

{ "messages": [ {"role": "user", "content": "..."}, {"role": "assistant", "content": "..."} ] }

Training setup:

• —LoRA r=16, alpha=32
• —BF16 training
• —max_length: 2048–4096
• —learning rate sweep: 5e-5 → 3e-4
• —warmup_ratio: 0.03–0.10
• —gradient checkpointing: enabled
• —optimizer and scheduler from TRL SFTTrainer

Disclaimer

This model is NOT a medical device and must not be used for autonomous diagnosis or treatment decisions. Outputs require interpretation and verification by certified medical professionals. Intended solely for research and decision-support augmentation.

Limitations

• —Optimized for cardiology and cardiac surgery
• —Reduced accuracy outside these domains
• —No vision capabilities (text-only MedGemma IT)
• —May generate incomplete or generalized recommendations

Citing & Authors

If you use this model in your research, please cite:

@misc{Ostashko2025MedGemmaCardiology, title = {MedGemma-4B-Cardiology: A Domain-Finetuned Clinical LLM for Cardiology}, author = {Uaritm}, year = {2025}, url = {ai.esemi.org} }

Project homepage: https://ai.esemi.org

HuggingFace

If you found this model useful — please give a star on HuggingFace:

https://huggingface.co/uaritm/medgemma-4b-cardiology-gguf