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shadowlilac
/
visor

Image-to-Text
Transformers
Safetensors
blip
image-text-to-text
image-captioning
anime
Model card Files Files and versions
xet

Instructions to use shadowlilac/visor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use shadowlilac/visor with Transformers:

    # Use a pipeline as a high-level helper
    # Warning: Pipeline type "image-to-text" is no longer supported in transformers v5.
    # You must load the model directly (see below) or downgrade to v4.x with:
    # 'pip install "transformers<5.0.0'
    from transformers import pipeline
    
    pipe = pipeline("image-to-text", model="shadowlilac/visor")
    # Load model directly
    from transformers import AutoProcessor, AutoModelForImageTextToText
    
    processor = AutoProcessor.from_pretrained("shadowlilac/visor")
    model = AutoModelForImageTextToText.from_pretrained("shadowlilac/visor")
  • Notebooks
  • Google Colab
  • Kaggle
visor
941 MB
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  • 1 contributor
History: 13 commits
shadowlilac's picture
shadowlilac
Update README.md
c0d8e07 over 2 years ago
  • .gitattributes
    1.52 kB
    initial commit over 2 years ago
  • LICENSE
    1.8 kB
    Update LICENSE over 2 years ago
  • LICENSE_BLIP
    1.5 kB
    Rename LICENSE.txt to LICENSE_BLIP over 2 years ago
  • README.md
    394 Bytes
    Update README.md over 2 years ago
  • config.json
    806 Bytes
    Upload BlipForConditionalGeneration over 2 years ago
  • generation_config.json
    136 Bytes
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  • model.safetensors
    940 MB
    xet
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  • preprocessor_config.json
    471 Bytes
    Upload processor over 2 years ago
  • special_tokens_map.json
    125 Bytes
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  • tokenizer.json
    712 kB
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  • tokenizer_config.json
    1.38 kB
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  • vocab.txt
    232 kB
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