Instructions to use davebraga/wrdbTI6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use davebraga/wrdbTI6 with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://davebraga/wrdbTI6") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6229da4fbb3d23357ff809953d5bcf51227a8341f0ea6d482b264300773e24b1
- Size of remote file:
- 31.5 MB
- SHA256:
- c6a4ee34242f8067858cc99a8a49cd72faf412387bcc5359ab220985ddd40d11
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