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:
- aa444173a35b3051735d0d38fca0a7272920c805769d875f8d5fa670392c42f7
- Size of remote file:
- 89.4 kB
- SHA256:
- 32e32e08b6624da494e3d9f9ba432fb7345ce23fb5fd71a9e42428dd12664b85
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