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:
- 014de9b7aff97ae25247dabec114d16e7b63568a1ead6393b4235f91e95bb6d9
- Size of remote file:
- 58.6 kB
- SHA256:
- d5646b72761ae347e0821f119e0de5e73a747cedf07335bbca9d04d8a08ff538
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