Instructions to use ChristianTeroerde/modelscan-nested-lambda-poc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use ChristianTeroerde/modelscan-nested-lambda-poc with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://ChristianTeroerde/modelscan-nested-lambda-poc") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3398838ab194297741bb9ddb4b18b45ef756f71d51560481ee70e321d4232694
- Size of remote file:
- 9.22 kB
- SHA256:
- f218fcaa1e36d10f85c44364be6e228b9943962c663d183783af5502f10ed833
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