Instructions to use hf-internal-testing/tiny-random-BitModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-BitModel 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-BitModel")# Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("hf-internal-testing/tiny-random-BitModel") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-BitModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b78a176d74bc6d0525e46875e8a85b3ec604fcaa045d3dd7c95610b5f56d717f
- Size of remote file:
- 89.4 kB
- SHA256:
- eb3b79f8b8f2e32d7104fdefae9187f2dca09aaa79e55f6282fa355accdddb09
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