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trunks
/
blip-image-captioning-base

Image-to-Text
Transformers
PyTorch
blip
image-text-to-text
Model card Files Files and versions
xet
Community
1

Instructions to use trunks/blip-image-captioning-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use trunks/blip-image-captioning-base 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="trunks/blip-image-captioning-base")
    # Load model directly
    from transformers import AutoProcessor, AutoModelForImageTextToText
    
    processor = AutoProcessor.from_pretrained("trunks/blip-image-captioning-base")
    model = AutoModelForImageTextToText.from_pretrained("trunks/blip-image-captioning-base")
  • Notebooks
  • Google Colab
  • Kaggle
blip-image-captioning-base
991 MB
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  • 1 contributor
History: 8 commits
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trunks
Update README.md
73f9adb over 2 years ago
  • .gitattributes
    1.52 kB
    initial commit over 2 years ago
  • README.md
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  • added_tokens.json
    82 Bytes
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  • config.json
    660 Bytes
    Upload BlipForConditionalGeneration over 2 years ago
  • generation_config.json
    141 Bytes
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  • preprocessor_config.json
    431 Bytes
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  • pytorch_model.bin

    Detected Pickle imports (3)

    • "collections.OrderedDict",
    • "torch._utils._rebuild_tensor_v2",
    • "torch.FloatStorage"

    What is a pickle import?

    990 MB
    xet
    Upload BlipForConditionalGeneration 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
    1.41 kB
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  • vocab.txt
    232 kB
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