Image-Text-to-Text
ZeroModels
Keras
PyTorch
JAX
TensorFlow
English
gemma3n
gemma-3n
audio-text-to-text
multimodal
Instructions to use zeromodels/gemma-3n-e2b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ZeroModels
How to use zeromodels/gemma-3n-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-3n-e2b") - Keras
How to use zeromodels/gemma-3n-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-3n-e2b") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from zeromodels/gemma-3n-e2b: direct link, hf CLI and curl.
- Browser
- Download file 33.4 MB
-
https://huggingface.co/zeromodels/gemma-3n-e2b/resolve/main/tokenizer.json
- Command line
-
hf download hf://zeromodels/gemma-3n-e2b/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/zeromodels/gemma-3n-e2b/resolve/main/tokenizer.json
33.4 MB
- Xet hash:
- 92b513641e467929ed2b320c76702e35d1dbcf91e32e4fc4366ded61c7965377
- Size of remote file:
- 33.4 MB
- SHA256:
- c4c19736bf24d1c6805cf49340e31bd02c70fb7857a2cb31065c90c2b5719c4e
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