Instructions to use Araeynn/e with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use Araeynn/e with Keras:
# !pip install -U keras tensorflow huggingface_hub # Keras needs TensorFlow installed to read "hf://" paths, so the tensorflow backend is selected here; # "jax" and "torch" also work for computation once TensorFlow is installed. import os os.environ["KERAS_BACKEND"] = "tensorflow" import keras model = keras.saving.load_model("hf://Araeynn/e") - Notebooks
- Google Colab
- Kaggle
Commit History
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Set `library_name` to `tf-keras`.
Verified signatureModel 'Araeynn/e' seems to be compatible only with "Keras 2" and not "Keras 3". To distinguish them, models compatible with legacy Keras 2.x should be tagged as `tf-keras` while models compatible with Keras 3.x are tagged as `keras`. This PR updates the model card to replace the explicit `library_name: keras` metadata which is now outdated by `library_name: tf-keras`. Updating this metadata will facilitate its discoverability and usage. For more information about `keras` and `tf-keras` library names, check out this pull request: https://github.com/huggingface/huggingface.js/pull/774.908dfa4
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Push Keras model using huggingface_hub.
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Push Keras model using huggingface_hub.
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Push Keras model using huggingface_hub.
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Push Keras model using huggingface_hub.
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Push Keras model using huggingface_hub.
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Push Keras model using huggingface_hub.
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Push Keras model using huggingface_hub.
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Push Keras model using huggingface_hub.
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initial commit
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