Instructions to use hf-internal-testing/tiny-random-data2vec_vision with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-data2vec_vision 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-data2vec_vision")# Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("hf-internal-testing/tiny-random-data2vec_vision") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-data2vec_vision", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a6631e3740d648feb0a6a5e0a2cfa0ef5c73c61c6126f25434003ae55ed989dd
- Size of remote file:
- 263 kB
- SHA256:
- 7f7fd80dcd9e148337f1684d817e96a2fadda39baebe262734ea2cced2452a43
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