Instructions to use molkab/dashboard with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use molkab/dashboard with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://molkab/dashboard") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3bb332b3de65de7d7cadbccd68423a8dc0e9e2433b799b9ac77be83c4585c168
- Size of remote file:
- 85.1 kB
- SHA256:
- 695590985871df4cc26f7c806657ae49a677be8cfff06d436934219337375707
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.