Instructions to use aakashjapi/temp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use aakashjapi/temp with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://aakashjapi/temp") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 90574eea47e93e0761b6633b5f64ed1f3483b28a6e2aaf1e90cd33859f635df0
- Size of remote file:
- 13.4 kB
- SHA256:
- 2af123ca891dcc377f77ab8fcf174ca9909a765a67916d8a2e334524e4daa231
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