Instructions to use Datadragon110/freshwater-quantization-models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use Datadragon110/freshwater-quantization-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://Datadragon110/freshwater-quantization-models") - Notebooks
- Google Colab
- Kaggle
Download verification_bundle.zip from Datadragon110/freshwater-quantization-models: direct link, hf CLI and curl.
- Browser
- Download file 139 kB
-
https://huggingface.co/Datadragon110/freshwater-quantization-models/resolve/main/verification_bundle.zip
- Command line
-
hf download hf://Datadragon110/freshwater-quantization-models/verification_bundle.zip
-
curl -L -o verification_bundle.zip https://huggingface.co/Datadragon110/freshwater-quantization-models/resolve/main/verification_bundle.zip
139 kB
- Xet hash:
- c879cad9b0a651df6797cbf1a8305237000c436e5f4d50e787b5b3853321f4c0
- Size of remote file:
- 139 kB
- SHA256:
- 4cbc487219cb857c5a510a15a861c301b3bf5cc87a701902d3ec887aadaaee81
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