Instructions to use UCSC-VLAA/openvision-vit-base-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-base-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-base-patch8-160")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("UCSC-VLAA/openvision-vit-base-patch8-160", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a68b40e74b18fda02de34b440f7968054bf3b4402eb102ec2cfab4d9c6469516
- Size of remote file:
- 562 MB
- SHA256:
- e017de221aeebdd3e2b6ed2286035de2d20e6f9bdbdcbe5e4bad668e3fe4ffbd
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