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: Q4_K_M / Q5_K_M GGUF
Context window: up to 4096 tokens

Repository Contents

medgemma-4b-cardiology.Q4_K_M.gguf medgemma-4b-cardiology.Q5_K_M.gguf tokenizer.json README.md

Usage Examples

llama.cpp

./main -m medgemma-4b-cardiology.Q4_K_M.gguf
-p "You are a cardiologist. Based on the clinical details, generate treatment recommendations."

Ollama

Create a file named Modelfile:

FROM ./medgemma-4b-cardiology.Q4_K_M.gguf TEMPLATE "user\n{{ .Prompt }}\n\nassistant" 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( model_path="medgemma-4b-cardiology.Q4_K_M.gguf", n_ctx=4096, n_gpu_layers=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:

user
[Your instruction or clinical text]

assistant

Example:

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

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 = {Ostashko, Vitaliy}, year = {2025}, url = {ai.esemi.org} }

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