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/AMCL(PRAN)/scaler_AMCL(PRAN).bin from StockCast/seperate: direct link, hf CLI and curl.
- Browser
- Download file 719 Bytes
-
https://huggingface.co/StockCast/seperate/resolve/main/models/AMCL(PRAN)/scaler_AMCL(PRAN).bin
- Command line
-
hf download 'hf://StockCast/seperate/models/AMCL(PRAN)/scaler_AMCL(PRAN).bin'
-
curl -L -o 'scaler_AMCL(PRAN).bin' 'https://huggingface.co/StockCast/seperate/resolve/main/models/AMCL(PRAN)/scaler_AMCL(PRAN).bin'
719 Bytes
- Xet hash:
- fef482398c78c17ff0f9340610d1a3fc15a785cf15ec32c850b7053c72c34f0d
- Size of remote file:
- 719 Bytes
- SHA256:
- 8f9dfa930b6ce79d018a75b5475750003d7eb221f8eebb425d6e8dbea998016a
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