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:
- 6a3e6ac01cabe299b6bfa46daf4e571ffd343859c6d323c7e5ef66772db6cd4c
- Size of remote file:
- 4.77 GB
- SHA256:
- f8ff07efcb33fd2848101ccaaca2f4a1f65a7b71c715a80f1d3e5a70edaae3e4
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