Instructions to use hf-internal-testing/tiny-random-BeitModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-BeitModel 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-BeitModel")# Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("hf-internal-testing/tiny-random-BeitModel") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-BeitModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d9f455cecb7e4dbbe2a14fc3a0fd452045a76f48561f15d0106831018472c6c9
- Size of remote file:
- 134 kB
- SHA256:
- 934f01423e9b8f012cc0f8edc1f3a764d2287408c0c7f2e0e58f6e9e559b9167
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