Instructions to use hf-tiny-model-private/tiny-random-BitBackbone 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-BitBackbone with Transformers:
# Load model directly from transformers import AutoImageProcessor, BitBackbone processor = AutoImageProcessor.from_pretrained("hf-tiny-model-private/tiny-random-BitBackbone") model = BitBackbone.from_pretrained("hf-tiny-model-private/tiny-random-BitBackbone") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1c99119fd8d6b5278b03c83c8d33d357a8455c27a09e2c422e98a4ca4244b0ca
- Size of remote file:
- 100 kB
- SHA256:
- 5b1781264f7f7e8cc765b7267a689228ca3d5c29934fb71ec0b926ed5489b2b0
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