Instructions to use sujal7102003/recommendation-models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use sujal7102003/recommendation-models 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://sujal7102003/recommendation-models") - Notebooks
- Google Colab
- Kaggle
Download final_model_card_scaled.pdf from sujal7102003/recommendation-models: direct link, hf CLI and curl.
- Browser
- Download file 134 kB
-
https://huggingface.co/sujal7102003/recommendation-models/resolve/main/final_model_card_scaled.pdf
- Command line
-
hf download hf://sujal7102003/recommendation-models/final_model_card_scaled.pdf
-
curl -L -o final_model_card_scaled.pdf https://huggingface.co/sujal7102003/recommendation-models/resolve/main/final_model_card_scaled.pdf
134 kB
- Xet hash:
- 2369cc45023a58838b57a893ea5cff3bf012b1cec3f30702aa70ec4552afc6db
- Size of remote file:
- 134 kB
- SHA256:
- 50b4b9a15559b1e05e18786ac53238c20ed6dbb54dba8af12b57dba7c894e5b3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.