Instructions to use JabaleNurAdnan/CS_Checkpoints_Dataset with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use JabaleNurAdnan/CS_Checkpoints_Dataset with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://JabaleNurAdnan/CS_Checkpoints_Dataset") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d9c50d4d807521b2c32e7ce8325e0714d50709864c360a12d25e2e91ee0a7af4
- Size of remote file:
- 4.12 MB
- SHA256:
- f7498f30b737b9a455c0e2b328fd6ddc2a41a0d098397d06e34131cd1f129d6e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.