Instructions to use ashraq/ml-latest-small-user-model-32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use ashraq/ml-latest-small-user-model-32 with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://ashraq/ml-latest-small-user-model-32") - Notebooks
- Google Colab
- Kaggle

- Xet hash:
- 517c2b68ab19781b22725d984fd0acb03716a966538a2d0e934d4e55f22f7eb4
- Size of remote file:
- 5.47 kB
- SHA256:
- f2a25034094d4060c4faa7aeccdf4029fe8811e0eb5a03b2bcaa82d7b68e9f16
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.