Instructions to use Jabka/Neue_Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use Jabka/Neue_Model 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://Jabka/Neue_Model") - Notebooks
- Google Colab
- Kaggle
Download my_model/saved_model.pb from Jabka/Neue_Model: direct link, hf CLI and curl.
- Browser
- Download file 249 kB
-
https://huggingface.co/Jabka/Neue_Model/resolve/main/my_model/saved_model.pb
- Command line
-
hf download hf://Jabka/Neue_Model/my_model/saved_model.pb
-
curl -L -o saved_model.pb https://huggingface.co/Jabka/Neue_Model/resolve/main/my_model/saved_model.pb
249 kB
- Xet hash:
- d1b93a04d508c5170e106240ef36c6b972a3afd93dbdd74132b2118074700ed8
- Size of remote file:
- 249 kB
- SHA256:
- 21f0e1f53e9874456f8cbe4a3e3e1fd879cd786158d04f9dded8ba892b49a629
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.