Instructions to use hf-internal-testing/tiny-random-DeiTForMaskedImageModeling with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-DeiTForMaskedImageModeling with Transformers:
# Load model directly from transformers import AutoImageProcessor, DeiTForMaskedImageModeling processor = AutoImageProcessor.from_pretrained("hf-internal-testing/tiny-random-DeiTForMaskedImageModeling") model = DeiTForMaskedImageModeling.from_pretrained("hf-internal-testing/tiny-random-DeiTForMaskedImageModeling", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- afb617cb038e67b48b64b34ff1a0c6765a0144d5952b9c0f2952598e79f13838
- Size of remote file:
- 296 kB
- SHA256:
- 65a5fb5d9c57e86d3fc501b9142467437682dd162d25a821e8a41072c2bda12c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.