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:
- 7ebffdaeefe3ec412478ce9c3fca6130b262629a0262e40ce05db4b47b0895bc
- Size of remote file:
- 118 kB
- SHA256:
- 0c7d28a37b1893714b95c5c6b03b30e0bcd295ced5a77c4b283e13a854b64c4f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.