Instructions to use satpalsr/fnet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use satpalsr/fnet with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://satpalsr/fnet") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2e318c4023845f49ab5408e0eed64d6e6f66f38f7fa30012ed6ee438fd2b22fc
- Size of remote file:
- 1.1 MB
- SHA256:
- d7df70a4a9beea46f1b8d43b35d3d917f3886e81810ccaaf2d7da058b0387a51
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