Instructions to use UCSC-VLAA/openvision-vit-large-patch14-336 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UCSC-VLAA/openvision-vit-large-patch14-336 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="UCSC-VLAA/openvision-vit-large-patch14-336")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("UCSC-VLAA/openvision-vit-large-patch14-336", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c838428609f2cf8d4bb29810c287d85251f28a3ec9858ff22566e6700b2419fc
- Size of remote file:
- 1.66 GB
- SHA256:
- 15bef61373e9541d7195885a2377b692ebef120df8e8838c771072ef9c445198
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.