bbarn4/medgemma-27b-text-it-GPTQ

MedGemma-27B-Text-IT GPTQ (4-bit)

This is a 4-bit GPTQ quantization of google/medgemma-27b-text-it, the text-only variant of Google's MedGemma optimized for medical text reasoning.

Created For

This quantized model was generated by Ben Barnard Ph.D and Oladimeji Adaramewa for MPART — the Medical Policy Applied Research Team at Innovate Springfield and University of Illinois Springfield. MPART focuses on applied research in healthcare policy, Medicaid concerns, and health system funding analysis.

Why this model?

The text-only MedGemma 27B scores higher on medical text benchmarks than the multimodal version (89.8 vs 87.0 on MedQA, 74.2 vs 70.2 on MedMCQA) while being simpler to deploy. This quantized version reduces memory requirements from ~55GB to ~15GB, making it runnable on a single GPU with 24GB+ VRAM. We found that it is very good with understanding healthcare policy and finance.

Quantization Details

• Method: GPTQ via llmcompressor (Neural Magic)
• Bits: 4 (W4A16 — 4-bit weights, 16-bit activations)
• Group size: 128
• Calibration data: 256 samples from C4 (English)
• Ignored layers: lm_head (kept at full precision)

Usage

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model = AutoModelForCausalLM.from_pretrained(
    "bbarn4/medgemma-27b-text-it-GPTQ",
    device_map="auto",
    dtype="auto"
)
tokenizer = AutoTokenizer.from_pretrained("bbarn4/medgemma-27b-text-it-GPTQ")

prompt = "What are the key differences between Type 1 and Type 2 diabetes?"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

with torch.inference_mode():
    output = model.generate(**inputs, max_new_tokens=500, do_sample=False)

print(tokenizer.decode(output[0], skip_special_tokens=True))

Base Model Performance (pre-quantization)

These are the published scores for the full-precision base model:

Benchmark	MedGemma 27B Text	Gemma 3 27B
MedQA (4-op)	89.8	74.9
MedMCQA	74.2	62.6
PubMedQA	76.8	73.4
MMLU Med	87.0	83.3
MedXpertQA	25.7	15.7

Intended Use

This model is intended as a starting point for developers and researchers building healthcare applications involving medical text. It is NOT intended for direct clinical use. All outputs require independent verification by qualified professionals.

License

Use is governed by the Health AI Developer Foundations terms of use.