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ljnlonoljpiljm
/
CLIP-ViT-H-14-laion2B-s32B-b79K-384-xlm-roberta-large-tv

Feature Extraction
Transformers
Safetensors
vision-text-dual-encoder
Model card Files Files and versions
xet
Community

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
CLIP-ViT-H-14-laion2B-s32B-b79K-384-xlm-roberta-large-tv
4.79 GB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 3 commits
ljnlonoljpiljm's picture
ljnlonoljpiljm
Upload processor
1382cae verified almost 2 years ago
  • .gitattributes
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  • README.md
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  • config.json
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  • model.safetensors
    4.77 GB
    xet
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  • preprocessor_config.json
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  • special_tokens_map.json
    964 Bytes
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  • tokenizer.json
    17.1 MB
    xet
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  • tokenizer_config.json
    1.2 kB
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