Instructions to use hf-tiny-model-private/tiny-random-DeiTForMaskedImageModeling with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-DeiTForMaskedImageModeling with Transformers:
# Load model directly from transformers import AutoImageProcessor, DeiTForMaskedImageModeling processor = AutoImageProcessor.from_pretrained("hf-tiny-model-private/tiny-random-DeiTForMaskedImageModeling") model = DeiTForMaskedImageModeling.from_pretrained("hf-tiny-model-private/tiny-random-DeiTForMaskedImageModeling", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d222c5ef316e07fbdb13a5e72b8b6d1e34bbe48c1185747d857f2769ecead0b1
- Size of remote file:
- 296 kB
- SHA256:
- 687290b36637026e5a1eb78feb7d241d56297cf655edf10f0cc6dbb144ad6bf8
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