Instructions to use hf-tiny-model-private/tiny-random-SwinModel 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-SwinModel 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-SwinModel")# Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("hf-tiny-model-private/tiny-random-SwinModel") model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-SwinModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 016c2fea5406d64b51165b7628e4180a0d6bddd276ae7735254c737edc8490a7
- Size of remote file:
- 368 kB
- SHA256:
- 851d8f3c8f1fb97ed8870c08e2768e9e566b2380ada9659f690b7c42222ce463
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.