Instructions to use rcodina/tm2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use rcodina/tm2 with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://rcodina/tm2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fc186c7df57195386dc9a0a974af45bad471c1450c9b081c6efeb2877a5aad8c
- Size of remote file:
- 5.25 MB
- SHA256:
- b19fa21d876fc4e22a6d8a3904a82ebea192c7d9400693ba9361f72f6eed7e87
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.