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bn22
/
CLIPlike-ViT-B-32-Qwen3

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

Instructions to use bn22/CLIPlike-ViT-B-32-Qwen3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use bn22/CLIPlike-ViT-B-32-Qwen3 with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("feature-extraction", model="bn22/CLIPlike-ViT-B-32-Qwen3")
    # Load model directly
    from transformers import AutoProcessor, AutoModel
    
    processor = AutoProcessor.from_pretrained("bn22/CLIPlike-ViT-B-32-Qwen3")
    model = AutoModel.from_pretrained("bn22/CLIPlike-ViT-B-32-Qwen3", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
CLIPlike-ViT-B-32-Qwen3
1.56 GB
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  • 1 contributor
History: 11 commits
bn22's picture
bn22
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bb84ed0 verified 6 months ago
  • .gitattributes
    1.57 kB
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  • README.md
    5.17 kB
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  • chat_template.jinja
    4.12 kB
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  • config.json
    3.48 kB
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  • model.safetensors
    1.55 GB
    xet
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  • processor_config.json
    639 Bytes
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  • tokenizer.json
    11.4 MB
    xet
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  • tokenizer_config.json
    756 Bytes
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