Instructions to use UCSC-VLAA/openvision-vit-small-patch8-160 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UCSC-VLAA/openvision-vit-small-patch8-160 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-patch8-160")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("UCSC-VLAA/openvision-vit-small-patch8-160", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- aa1ca5854b3be7196c46e3363b0e41aa107c686372f798a4ae91981b92ecc2f3
- Size of remote file:
- 222 MB
- SHA256:
- c3217ce2ce1ab33c0d11b83c43b1d0d44b5f255417cd3b351196fb8cf6802544
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