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:
- d1092101c8d85ea51da1b014adffabb548fd2da69451d1e2cc3c90d29e2896a6
- Size of remote file:
- 134 kB
- SHA256:
- 0f1073e7148b0ad8c10aad67f63cdd5e67dfd4b5746b072bc75c5e0174aa037a
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