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:
- 41fa22db50707796c09021e5f1aa233f6d999b4d721e6c973161a613f15d9978
- Size of remote file:
- 118 kB
- SHA256:
- 81509dbd2fedc657fcffd4c56e80a70782f562bf992ffd7529215a9b272b3c46
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.