Instructions to use UCSC-VLAA/openvision-vit-base-patch8-384 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UCSC-VLAA/openvision-vit-base-patch8-384 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="UCSC-VLAA/openvision-vit-base-patch8-384")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("UCSC-VLAA/openvision-vit-base-patch8-384", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download open_clip_pytorch_model.bin from UCSC-VLAA/openvision-vit-base-patch8-384: direct link, hf CLI and curl.
- Browser
- Download file 568 MB
-
https://huggingface.co/UCSC-VLAA/openvision-vit-base-patch8-384/resolve/main/open_clip_pytorch_model.bin
- Command line
-
hf download hf://UCSC-VLAA/openvision-vit-base-patch8-384/open_clip_pytorch_model.bin
-
curl -L -o open_clip_pytorch_model.bin https://huggingface.co/UCSC-VLAA/openvision-vit-base-patch8-384/resolve/main/open_clip_pytorch_model.bin
568 MB
- Xet hash:
- 953883caf6f3bc71026175ce22f58a7a41becca1e18055ebe864902fc5893aeb
- Size of remote file:
- 568 MB
- SHA256:
- 33bff5d4db996c7268192b84b0d416978add0e3754efd70f7ceb195e02631ef2
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