Instructions to use wlaminack/GradientBoostedTreesModeltest with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use wlaminack/GradientBoostedTreesModeltest with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://wlaminack/GradientBoostedTreesModeltest") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ac448223413d525b1a5ee01c560760efb475f3764ae5d9bef8a43ee12e9d6713
- Size of remote file:
- 2.76 kB
- SHA256:
- 29a71f6d2fdcb380b560a849f727681dd9abc9d59db3ad1a4944c0a267001fa2
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