Instructions to use ICFgainAI/Section with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use ICFgainAI/Section 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://ICFgainAI/Section") - Notebooks
- Google Colab
- Kaggle
Download ICFgainAI_cross_section.keras from ICFgainAI/Section: direct link, hf CLI and curl.
- Browser
- Download file 13.1 MB
-
https://huggingface.co/ICFgainAI/Section/resolve/main/ICFgainAI_cross_section.keras
- Command line
-
hf download hf://ICFgainAI/Section/ICFgainAI_cross_section.keras
-
curl -L -o ICFgainAI_cross_section.keras https://huggingface.co/ICFgainAI/Section/resolve/main/ICFgainAI_cross_section.keras
13.1 MB
- Xet hash:
- 9606a99ab8873b2fac19f143c08b47c9f7e57226b3c281840a19b0c93d5ee247
- Size of remote file:
- 13.1 MB
- SHA256:
- c3cbab420dc98439776bc3319df17c5f28dd37086572645ae20cffff47b28960
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.