Instructions to use hf-internal-testing/tiny-random-ViTForMaskedImageModeling with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-ViTForMaskedImageModeling with Transformers:
# Load model directly from transformers import AutoImageProcessor, ViTForMaskedImageModeling processor = AutoImageProcessor.from_pretrained("hf-internal-testing/tiny-random-ViTForMaskedImageModeling") model = ViTForMaskedImageModeling.from_pretrained("hf-internal-testing/tiny-random-ViTForMaskedImageModeling", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from hf-internal-testing/tiny-random-ViTForMaskedImageModeling: direct link, hf CLI and curl.
- Browser
- Download file 197 kB
-
https://huggingface.co/hf-internal-testing/tiny-random-ViTForMaskedImageModeling/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://hf-internal-testing/tiny-random-ViTForMaskedImageModeling@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/hf-internal-testing/tiny-random-ViTForMaskedImageModeling/resolve/refs%2Fpr%2F1/pytorch_model.bin
197 kB
- Xet hash:
- 615be1e60e5ca243de545da83354f3c63564250245696732fa065b680e2a00e9
- Size of remote file:
- 197 kB
- SHA256:
- b6b197d0e52c1fcd8b65dcc7524ece41790776e4f50ca850ed97efe5a8cd3680
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