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:
- eb2c35ea3f679ef72912de7625dcb9b63b77c8a3965c33f20bd5dbb3986ebe2b
- Size of remote file:
- 118 kB
- SHA256:
- 3e7362902c4b881b12ae4b2e9a161240902608d71f7ac6c9f639fd02e4bd27fd
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.