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:
- fe17203a27fa1210820ca3e38fa8f5481f59ce0c64d354b836d63cfef20fb525
- Size of remote file:
- 118 kB
- SHA256:
- 1228e8ca6d466df7d6c6495ecfe59af069db9e40ee3bbf6f6823a8a938103629
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.