Instructions to use hf-internal-testing/tiny-random-Swinv2Backbone with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-Swinv2Backbone with Transformers:
# Load model directly from transformers import AutoImageProcessor, Swinv2Backbone processor = AutoImageProcessor.from_pretrained("hf-internal-testing/tiny-random-Swinv2Backbone") model = Swinv2Backbone.from_pretrained("hf-internal-testing/tiny-random-Swinv2Backbone", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d4e19c1fb3a08f91400c9a7b853dc388050685062626bd08193d699d9a854bad
- Size of remote file:
- 308 kB
- SHA256:
- 1e93eacd32329295ef460424d7bd3c0b2e63ce1041d885ba784a94f0e46a2d0d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.