Instructions to use Bananaws/Python_Task6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use Bananaws/Python_Task6 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://Bananaws/Python_Task6") - Notebooks
- Google Colab
- Kaggle
Download model-autoencoder/saved_model.pb from Bananaws/Python_Task6: direct link, hf CLI and curl.
- Browser
- Download file 277 kB
-
https://huggingface.co/Bananaws/Python_Task6/resolve/d3186bc431c961db0a29ddd18e88a9f1d151d8a0/model-autoencoder/saved_model.pb
- Command line
-
hf download hf://Bananaws/Python_Task6@d3186bc431c961db0a29ddd18e88a9f1d151d8a0/model-autoencoder/saved_model.pb
-
curl -L -o saved_model.pb https://huggingface.co/Bananaws/Python_Task6/resolve/d3186bc431c961db0a29ddd18e88a9f1d151d8a0/model-autoencoder/saved_model.pb
277 kB
- Xet hash:
- 2624511b938690970d93a90a21a3368044d1899da71d0d9c964239d9c7813553
- Size of remote file:
- 277 kB
- SHA256:
- 823744bfa59942480021a4dbbcb666510896611096033655c4c80e467fd03cf9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.