Instructions to use hf-tiny-model-private/tiny-random-BitModel 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-BitModel 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-BitModel")# Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("hf-tiny-model-private/tiny-random-BitModel") model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-BitModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 82a962f2b011e08e0a531edcc12472c89f7af6a161a2ffec4f77a47c555e0ccb
- Size of remote file:
- 99.9 kB
- SHA256:
- 2fb3d399dfdda56dda1a557fa628224d4e594eee3f72d32e0bdcf3c881ca0a2e
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