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 Base Model Performance (pre quantization) These are the published…
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