Instructions to use hf-tiny-model-private/tiny-random-Data2VecVisionModel 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-Data2VecVisionModel 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-Data2VecVisionModel")# Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("hf-tiny-model-private/tiny-random-Data2VecVisionModel") model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-Data2VecVisionModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 343073bdd261c416fb860a42fd0c5b7f929d730babc162abb91bd0f90e75b8fb
- Size of remote file:
- 133 kB
- SHA256:
- 04648e1d0fa189b80f5597e8dd19f1faf503b088aa51249068262483b60656c5
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