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
Raise per-image vision soft-token budget from 280 to 1120
#8
by lucianommartins - opened
Increases the default per-image vision soft-token budget from 280 to 1120 (the maximum supported value) so images are encoded at higher resolution by default. Video budget is unchanged (video_processor.max_soft_tokens = 70); no other values are modified.
Exact changes in this repo:
config.json:vision_config.num_soft_tokens: 280 → 1120
processor_config.json:image_processor.max_soft_tokens: 280 → 1120image_seq_length: 280 → 1120
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