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:
- 3ac5ba78db53810589685347cd4b1f59042d5018ed1fa198ee24b290fb2c644b
- Size of remote file:
- 1.28 kB
- SHA256:
- 2f99e3adb1f6ef591417646730181cb5170c89248e0eb9094dbbd5958dd292d3
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