Instructions to use UCSC-VLAA/openvision-vit-small-patch16-384 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UCSC-VLAA/openvision-vit-small-patch16-384 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="UCSC-VLAA/openvision-vit-small-patch16-384")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("UCSC-VLAA/openvision-vit-small-patch16-384", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c731b04485f5a8a75a0ced47945aa78dd9c0d5ca37c117746316beeb10f80d19
- Size of remote file:
- 223 MB
- SHA256:
- 7808d56585500788f022b4869e1d7c3490706a89eb7bef5219a2bcfd6be7deaa
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