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/1STPRIMFMF/scaler_1STPRIMFMF.bin from StockCast/seperate: direct link, hf CLI and curl.
- Browser
- Download file 719 Bytes
-
https://huggingface.co/StockCast/seperate/resolve/main/models/1STPRIMFMF/scaler_1STPRIMFMF.bin
- Command line
-
hf download hf://StockCast/seperate/models/1STPRIMFMF/scaler_1STPRIMFMF.bin
-
curl -L -o scaler_1STPRIMFMF.bin https://huggingface.co/StockCast/seperate/resolve/main/models/1STPRIMFMF/scaler_1STPRIMFMF.bin
719 Bytes
- Xet hash:
- 47e34dbb2b2e6679a8a8029ed0b89cfa65435343e3029f7f9adce0ced0d65b38
- Size of remote file:
- 719 Bytes
- SHA256:
- b5c46846e9b3df8d48c36c5df47a02c7e0903933d371432bec56ca7f667d505e
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