Instructions to use hf-internal-testing/tiny-random-BeitBackbone with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-BeitBackbone with Transformers:
# Load model directly from transformers import AutoImageProcessor, BeitBackbone processor = AutoImageProcessor.from_pretrained("hf-internal-testing/tiny-random-BeitBackbone") model = BeitBackbone.from_pretrained("hf-internal-testing/tiny-random-BeitBackbone") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 76415bc71a3c94fb5f1130795215d8c43e38664be0a8e0ac599a384828ffed28
- Size of remote file:
- 118 kB
- SHA256:
- 2384fe5f2199900b903d441e7e08512539a2f780d870cb8259c832d7e18a2cad
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.