Instructions to use hf-internal-testing/tiny-random-ViTMAEModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-ViTMAEModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="hf-internal-testing/tiny-random-ViTMAEModel")# Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("hf-internal-testing/tiny-random-ViTMAEModel") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-ViTMAEModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3b2b62b1ca6d2ee66b7c02968835b43b558a5a982de52aeba16b4af873d80f8c
- Size of remote file:
- 194 kB
- SHA256:
- d1f20d03c9e6c8dc61a2304b60e4bb5a08989a81bbebfb49bfec27a987873e6c
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