Instructions to use wlaminack/GradientBoostedTreesModeltest2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use wlaminack/GradientBoostedTreesModeltest2 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/GradientBoostedTreesModeltest2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f69c24d4fa82b245f62a11649d6ec82e02ef79606dea194d71c8af26e49f0ed8
- Size of remote file:
- 57 Bytes
- SHA256:
- d23cacf6120fcd251cdff284525bf7bc5debbdd0df22b7b9643301202b8f59d6
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