Instructions to use StockCast/seperate with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use StockCast/seperate 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://StockCast/seperate") - Notebooks
- Google Colab
- Kaggle
Download models/ASIATICLAB/lstm_ASIATICLAB_seq60_nahead3.keras from StockCast/seperate: direct link, hf CLI and curl.
- Browser
- Download file 390 kB
-
https://huggingface.co/StockCast/seperate/resolve/main/models/ASIATICLAB/lstm_ASIATICLAB_seq60_nahead3.keras
- Command line
-
hf download hf://StockCast/seperate/models/ASIATICLAB/lstm_ASIATICLAB_seq60_nahead3.keras
-
curl -L -o lstm_ASIATICLAB_seq60_nahead3.keras https://huggingface.co/StockCast/seperate/resolve/main/models/ASIATICLAB/lstm_ASIATICLAB_seq60_nahead3.keras
390 kB
- Xet hash:
- b52404fe700c5009289b8a85b7aeac4648a444602bbf48cac796145067c7726b
- Size of remote file:
- 390 kB
- SHA256:
- 4cb6ac697b21a9d070db51ff5713299de338f1913b41bc33b4a39d62cc7f714e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.