Instructions to use hf-tiny-model-private/tiny-random-BeitModel 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-BeitModel 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-BeitModel")# Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("hf-tiny-model-private/tiny-random-BeitModel") model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-BeitModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5c88dc7bbf98deb11e9ba5f816e114f2e5c06a54335d833fe50f31222b2df267
- Size of remote file:
- 119 kB
- SHA256:
- 0d4331be7317de5c594127d09a70c83a04d59d8f1c46e8a654ead456e7073d6d
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