Instructions to use ljnlonoljpiljm/CLIP-ViT-H-14-laion2B-s32B-b79K-384-xlm-roberta-large-tv with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ljnlonoljpiljm/CLIP-ViT-H-14-laion2B-s32B-b79K-384-xlm-roberta-large-tv with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="ljnlonoljpiljm/CLIP-ViT-H-14-laion2B-s32B-b79K-384-xlm-roberta-large-tv")# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("ljnlonoljpiljm/CLIP-ViT-H-14-laion2B-s32B-b79K-384-xlm-roberta-large-tv") model = AutoModel.from_pretrained("ljnlonoljpiljm/CLIP-ViT-H-14-laion2B-s32B-b79K-384-xlm-roberta-large-tv", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 900d575674d5bfcd5c2b9059c9221a42f92c957ed7230f7e096d3e9580c3e4c2
- Size of remote file:
- 17.1 MB
- SHA256:
- 883b037111086fd4dfebbbc9b7cee11e1517b5e0c0514879478661440f137085
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