Instructions to use Higgs32/tornet-ml-higgins with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use Higgs32/tornet-ml-higgins with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://Higgs32/tornet-ml-higgins") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3b37ece7665bc825b11de84e674f0622559c40d4d5ac7d94e07df49d04515e35
- Size of remote file:
- 12 MB
- SHA256:
- 39c7829822438a00da987831a7456975b7968472d10c20a981dc89416ab1c1cd
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