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Instructions to use me-aas/EinsteinNet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use me-aas/EinsteinNet with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://me-aas/EinsteinNet") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 54fb88230b23ca8b5ea7abcceded0ca4eda510bdeb19dfe7ed0719bb532a2cfa
- Size of remote file:
- 259 kB
- SHA256:
- 1356af9763d7ba257c03a8ad33130587447bacf80f175a9820d1fd8bc84ae757
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