Instructions to use UCSC-VLAA/openvision-vit-so400m-patch14-224 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UCSC-VLAA/openvision-vit-so400m-patch14-224 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="UCSC-VLAA/openvision-vit-so400m-patch14-224")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("UCSC-VLAA/openvision-vit-so400m-patch14-224", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download open_clip_pytorch_model.bin from UCSC-VLAA/openvision-vit-so400m-patch14-224: direct link, hf CLI and curl.
- Browser
- Download file 3.45 GB
-
https://huggingface.co/UCSC-VLAA/openvision-vit-so400m-patch14-224/resolve/main/open_clip_pytorch_model.bin
- Command line
-
hf download hf://UCSC-VLAA/openvision-vit-so400m-patch14-224/open_clip_pytorch_model.bin
-
curl -L -o open_clip_pytorch_model.bin https://huggingface.co/UCSC-VLAA/openvision-vit-so400m-patch14-224/resolve/main/open_clip_pytorch_model.bin
3.45 GB
- Xet hash:
- 202b74af977b3ced91b965c603f4bae4807644b2dcca04c35539e0fbd8decb6a
- Size of remote file:
- 3.45 GB
- SHA256:
- ccd6c6f3d9c65f34254169ef718e407fda418616d485adcb16696496cbc534c7
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