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
File size: 884 Bytes
dcfe122 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 | default_stage:
default_modifiers:
QuantizationModifier:
config_groups:
group_0:
targets: [Linear]
weights:
num_bits: 4
type: !!python/object/apply:compressed_tensors.quantization.quant_args.QuantizationType [
int]
symmetric: true
group_size: 32
strategy: group
block_structure: null
dynamic: false
actorder: null
scale_dtype: null
zp_dtype: null
observer: memoryless_minmax
observer_kwargs: {}
input_activations: null
output_activations: null
format: null
targets: [Linear]
ignore: [lm_head, 're:.*embed.*', 're:.*vision_tower.*', 're:.*vision_embedder.*', 're:.*audio_tower.*',
're:.*router.*']
bypass_divisibility_checks: false
|