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:
- 66b164eb1b94bd49f0953a9b3e7b354366fde0604746fcfebe912f42ce9d7c12
- Size of remote file:
- 118 kB
- SHA256:
- 50eca9e0f41bbc8da69eb1b9aa3b9dcfcba0988978caabc55473bf0339f317a4
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