Instructions to use Omamaa12/iris-models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use Omamaa12/iris-models with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://Omamaa12/iris-models") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3ca833f4560ddc38e68df82d0a08088860453fb4874dc14c59b30824d37c6b6d
- Size of remote file:
- 1.74 MB
- SHA256:
- f37932022cadfbb362f9f8d039e560e6764317eba16a2623087416de0a576921
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.