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:
- cd54cd820966e57d37b69d1531fe0acf5db4a29b63560194cc723295ab05aeaa
- Size of remote file:
- 57 Bytes
- SHA256:
- 8492370a86fea4c2fa4c6b669793332d97a163a8bb285406308a7478c9684435
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