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:
- 284d58c7f195681a9904a9ce3314082110d89f400be0174de00c58a691c28b86
- Size of remote file:
- 58.6 kB
- SHA256:
- c68e422f663ecd276c9ad144ffa461991eac3533832b90385011de641180e06e
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