Instructions to use nadjla12/skyline-api with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use nadjla12/skyline-api with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://nadjla12/skyline-api") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1c52e39c4b4a4cb98ae7942069ce4bdea6ba0f0cded06567881bcafea6cb4015
- Size of remote file:
- 20 MB
- SHA256:
- d671d0ec2c181c27841708a16cd5985de65cf27f13f612f586cbe1d2355f53fa
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