Quantizing MedGemma to INT4 (GPTQ/W4A16): Everything That Broke Along the Way
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Quantized Google's MedGemma-1.5-4B (a medical vision-language model) to INT4 (W4A16) via llm-compressor 's GPTQModifier, for self-hosted deployment. 8.6 GB in BF16 -> 5.2 GB quantized. Full step-by-step below, model link at the bottom. References: gemma3_example.py (model class, GPTQ/W4A16 recipe) and gemma4_example.py (calibration dataset pattern). Step 1: Choose the quantization method GPTQ, via llm-compressor . Not AWQ. AutoAWQ is officially deprecated . Even setting that aside, AutoAWQ…
1Key Takeaways
- Quantized Google's MedGemma-1.5-4B (a medical vision-language model) to INT4 (W4A16) via llm-compressor 's GPTQModifier, for self-hosted deployment.
- Full step-by-step below, model link at the bottom.
- References: gemma3_example.py (model class, GPTQ/W4A16 recipe) and gemma4_example.py (calibration dataset pattern).
- Step 1: Choose the quantization method GPTQ, via llm-compressor .
2AIWedia Score
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3Why it matters
Coding AI shifts how fast software ships and how much human review each change needs. DEV — ML reports that quantized Google's MedGemma-1.5-4B (a medical vision-language model) to INT4 (W4A16) via llm-compressor 's GPTQModifier, for self-hosted deployment.
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