Instructions to use kfahn/mandelbulb with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use kfahn/mandelbulb with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://kfahn/mandelbulb") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f54b44b138349dc852163bdc7967ef02d13d5ee77a104eedfed4d9d956099e0d
- Size of remote file:
- 20.8 MB
- SHA256:
- 7e51313fcfef3f4e3e3eda1a3fc6d922893a417fbebf6c06afd3a6ce4997cfb6
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