Instructions to use hf-internal-testing/tiny-random-Swinv2Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-Swinv2Model 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-Swinv2Model")# Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("hf-internal-testing/tiny-random-Swinv2Model") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-Swinv2Model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7a6cd268e423d4185856585106c91dae3dd9c4fc156cbf0f658642b4b8e4c9b3
- Size of remote file:
- 328 kB
- SHA256:
- 2df26b40f8c00afd60b81f48988b7ae9adf81deb276ce16fe14d52ac8289b384
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