Instructions to use hf-internal-testing/tiny-random-Data2VecVisionModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-Data2VecVisionModel 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-Data2VecVisionModel")# Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("hf-internal-testing/tiny-random-Data2VecVisionModel") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-Data2VecVisionModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 40d291ab77ac7dba4c186818bec50e32997883104c8ff543773d3e4577688520
- Size of remote file:
- 118 kB
- SHA256:
- d39457770f83f510a24cd16fdc9422c850c017b28baa2f87da2bdcfcd22186af
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