Instructions to use hf-internal-testing/tiny-random-Swinv2ForMaskedImageModeling with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-Swinv2ForMaskedImageModeling with Transformers:
# Load model directly from transformers import AutoImageProcessor, Swinv2ForMaskedImageModeling processor = AutoImageProcessor.from_pretrained("hf-internal-testing/tiny-random-Swinv2ForMaskedImageModeling") model = Swinv2ForMaskedImageModeling.from_pretrained("hf-internal-testing/tiny-random-Swinv2ForMaskedImageModeling", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e6bdcc439c2704d052f8cbb0de27b55be056329d721debcdc13dd5d7c21a8e0a
- Size of remote file:
- 379 kB
- SHA256:
- a6bb39d66594f43bd65303491c8774aa8b5399b9d751d1db6a875bb7430184e5
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