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:
- a6eb49477b32e1806be5371cf9f8a6c9717af381336b7ff3839bfdace093c174
- Size of remote file:
- 129 MB
- SHA256:
- d557662e58d03f80f62539304b81978f4ed2fb5e4c70a9b52f7f544bd96d4004
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