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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2d4b6ccda146af0c02b2c07383dbb9817e07cb6f3ca5cb429f890063af555865
- Size of remote file:
- 133 kB
- SHA256:
- 01d6b6ef8b442424cb6973ee5d2317dcf21d95a9440a8021d775ed4138c4b78e
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