Instructions to use emresvd/u160 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use emresvd/u160 with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://emresvd/u160") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d633e23e49a9356bee18ea7f94a68a6afbafd5877321c410e7e73fd52932b755
- Size of remote file:
- 115 kB
- SHA256:
- 8094e5386ff7a790291de8d98bfa122f5c5bd22886f91a0cf136e11288658b77
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.