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dbcccc
/
TypLens

Image-to-Text
Transformers
ONNX
Safetensors
vision-encoder-decoder
image-text-to-text
typst
math-ocr
formula-recognition
browser
Model card Files Files and versions
xet
Community

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

  • Libraries
  • Transformers

    How to use dbcccc/TypLens 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="dbcccc/TypLens")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForMultimodalLM
    
    tokenizer = AutoTokenizer.from_pretrained("dbcccc/TypLens")
    model = AutoModelForMultimodalLM.from_pretrained("dbcccc/TypLens", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
TypLens
270 MB
Ctrl+K
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  • 1 contributor
History: 2 commits
dbcccc's picture
dbcccc
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  • licenses
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  • onnx
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  • .gitattributes
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  • INFERENCE.md
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  • NOTICE.md
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  • README.md
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  • SHA256SUMS
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  • config.json
    1.63 kB
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  • generation_config.json
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  • model.safetensors
    118 MB
    xet
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  • native-output.json
    152 Bytes
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  • preprocessor_config.json
    450 Bytes
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  • release-manifest.json
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  • special_tokens_map.json
    964 Bytes
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  • token-bytes.json
    50.3 kB
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  • tokenizer.json
    66.4 kB
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  • tokenizer_config.json
    1.24 kB
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