Instructions to use JmPaunlagui/Improve with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use JmPaunlagui/Improve with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://JmPaunlagui/Improve") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- aa01b221af8d3209c2caf28f8f1776f5ee215b7685d3e83ac4608255b6683fc1
- Size of remote file:
- 10.8 MB
- SHA256:
- ffd5b639bf6db06442a72ca37c63fb797b9a914168f862a2837d59a96019cd3d
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