Instructions to use jazzy-waffle/GradientBoostedTreesModeltest with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use jazzy-waffle/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://jazzy-waffle/GradientBoostedTreesModeltest") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e9a7b0548d067bf27e31c9ddff655c4c3c3e2f85a909756e839464b0d064d5b7
- Size of remote file:
- 58.6 kB
- SHA256:
- 23e3ed73cfdf62d5a8cc4d2a9f54a81bd942cb143f444a7a85b1ccd4ba491b90
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.