Instructions to use google/gemma-4-12B-it-qat-w4a16-ct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/gemma-4-12B-it-qat-w4a16-ct with Transformers:
# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("google/gemma-4-12B-it-qat-w4a16-ct") model = AutoModelForMultimodalLM.from_pretrained("google/gemma-4-12B-it-qat-w4a16-ct") - Notebooks
- Google Colab
- Kaggle
fix: add missing num_soft_tokens and model_patch_size to vision_config
#5
by lucianommartins - opened
The vision_config section is missing num_soft_tokens and model_patch_size fields that are present in the base google/gemma-4-12B-it checkpoint.
This causes vLLM to crash with:
AttributeError: Gemma4UnifiedVisionConfig object has no attribute num_soft_tokens
Fix: add num_soft_tokens=280 and model_patch_size=48 to match the base model.
osanseviero changed pull request status to merged