Instructions to use OneclickAI/LSTM_GUE_test_Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use OneclickAI/LSTM_GUE_test_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://OneclickAI/LSTM_GUE_test_Model") - Notebooks
- Google Colab
- Kaggle
Download gru_model.keras from OneclickAI/LSTM_GUE_test_Model: direct link, hf CLI and curl.
- Browser
- Download file 15.8 MB
-
https://huggingface.co/OneclickAI/LSTM_GUE_test_Model/resolve/main/gru_model.keras
- Command line
-
hf download hf://OneclickAI/LSTM_GUE_test_Model/gru_model.keras
-
curl -L -o gru_model.keras https://huggingface.co/OneclickAI/LSTM_GUE_test_Model/resolve/main/gru_model.keras
15.8 MB
- Xet hash:
- a49a3a399e15f644cd746e7563bed436dc20ee2f178fdf32e3819503f90fe193
- Size of remote file:
- 15.8 MB
- SHA256:
- f8befd990e303da99cce08bd0b43dab6e14142274a12dc7019e8f56322ad4a9f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.