Instructions to use Umair2002/codeLevelGemma with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use Umair2002/codeLevelGemma with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://Umair2002/codeLevelGemma") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6f5e044c0349071e31355edc6e885d2ddc0b4a0dfc041fe98cbcfb810245669d
- Size of remote file:
- 4.01 GB
- SHA256:
- 08a1e9f8edad39008565d24a52231735c2b7f318397a6143b7de4d32f28fc233
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