Instructions to use hf-tiny-model-private/tiny-random-ViTMAEModel 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-ViTMAEModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="hf-tiny-model-private/tiny-random-ViTMAEModel")# Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("hf-tiny-model-private/tiny-random-ViTMAEModel") model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-ViTMAEModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 997320c639aa9fc80fbc1104191dd6caea3fbaef8a8ab1c38ba48e6cba53ef1e
- Size of remote file:
- 176 kB
- SHA256:
- 6abbcf75d74cc9f13a063553972acc54163f260b41fc4193fa997c98fd414041
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