Instructions to use UsefulSensors/moonshine with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use UsefulSensors/moonshine with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://UsefulSensors/moonshine") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b9fb7b2ecf3bb8929d683d6d594c5443d0f1bc509febb53ab9059c140b92f0bf
- Size of remote file:
- 246 MB
- SHA256:
- fdaf4aaded4c6e0dbacb5cadc5f8bd73f309efb3d0ff30417f8c896c7a0e05f6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.