Instructions to use zeromodels/gemma-4-e2b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ZeroModels
How to use zeromodels/gemma-4-e2b with ZeroModels:
# pip install -U zeromodels # ZeroModels is pure Keras 3, so pick a backend: "jax", "torch" or "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" from zeromodels import AutoZModel # AutoZModel reads the repo's model_type and loads the matching class. # For a task head use the matching loader, e.g. AutoZMImageClassify / AutoZMDetect / # AutoZMSemanticSegment / AutoZMTextGenerate (see zeromodels.auto). model = AutoZModel.from_weights("zeromodels/gemma-4-e2b") - Keras
How to use zeromodels/gemma-4-e2b with Keras:
# !pip install -U keras tensorflow huggingface_hub # Keras needs TensorFlow installed to read "hf://" paths, so the tensorflow backend is selected here; # "jax" and "torch" also work for computation once TensorFlow is installed. import os os.environ["KERAS_BACKEND"] = "tensorflow" import keras model = keras.saving.load_model("hf://zeromodels/gemma-4-e2b") - Notebooks
- Google Colab
- Kaggle
File size: 408 Bytes
cc5734b b51e234 cc5734b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | {
"library_name": "zeromodels",
"zeromodels_version": "1.2.1",
"preprocessor_module": "zeromodels.models.gemma4",
"preprocessor_class": "Gemma4ImageProcessor",
"variant": "gemma-4-e2b",
"patch_size": 16,
"max_soft_tokens": 280,
"pooling_kernel_size": 3,
"rescale_factor": 0.00392156862745098,
"image_mean": [
0.0,
0.0,
0.0
],
"image_std": [
1.0,
1.0,
1.0
]
} |