Instructions to use csukuangfj/moonshine-fork with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use csukuangfj/moonshine-fork with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://csukuangfj/moonshine-fork") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cc7bd51e281db31509597e1c8d0900d258f45cc2e21f594bd8af2162b032f880
- Size of remote file:
- 6.82 MB
- SHA256:
- 7fc2ea6d695c32b8672271db0ede8eb6285e18cef37c13accbb1f92c83dc6bb6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.