Instructions to use ChamathEka/sinhala-gemma-9b-CoT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use ChamathEka/sinhala-gemma-9b-CoT with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://ChamathEka/sinhala-gemma-9b-CoT") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ddc9296a8b75eb45faeaf491dc4f51184918b6cf9a909211d2491192bdec2e90
- Size of remote file:
- 37.1 GB
- SHA256:
- cb41b565846e32182bc26e29b07a381367d03cf4d988ad28fec1b9c077f16609
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