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:
- 39722ef62da405038624374e335246b01b0b9216a1599ba38163c4ff35550689
- Size of remote file:
- 14.1 MB
- SHA256:
- 6d3ccf645d9a209a9c73eb3378a9b23e98d4954e880caa8187b85b083dd2eb41
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.