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:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://zeromodels/gemma-4-e2b") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 10595853a0eca7cb37b0f9c68634a0fa18d6068e7bdab415e221fea1175e103d
- Size of remote file:
- 3.45 GB
- SHA256:
- c01c7efd2f0879c159c287b64352f4febaac48d1992deb6b31087cfdab0c6f7e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.