Instructions to use reshmasuresh/mlmodel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use reshmasuresh/mlmodel with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://reshmasuresh/mlmodel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- df72f128ad34db640f5a64f230cae6471f69d05f38787b87de200df7ef35d9de
- Size of remote file:
- 4.3 kB
- SHA256:
- ff7a191c4476cc22a864ea6db1b84c43f72d25a45ad4fb60a9e482c6b261f1bf
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