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oddadmix
/
Nawah-VL-25M

Image-to-Text
Transformers
Safetensors
Arabic
lfm2_vl
image-text-to-text
arabic
vlm
image-captioning
siglip2
lfm2-vl
emhotob
Model card Files Files and versions
xet
Community

Instructions to use oddadmix/Nawah-VL-25M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use oddadmix/Nawah-VL-25M 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="oddadmix/Nawah-VL-25M")
    # Load model directly
    from transformers import AutoProcessor, AutoModelForMultimodalLM
    
    processor = AutoProcessor.from_pretrained("oddadmix/Nawah-VL-25M")
    model = AutoModelForMultimodalLM.from_pretrained("oddadmix/Nawah-VL-25M", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
Nawah-VL-25M / training
193 kB
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  • 1 contributor
History: 2 commits
oddadmix's picture
oddadmix
Refresh training code; link the published dataset
f6e3ac6 verified about 1 month ago
  • __pycache__
    Refresh training code; link the published dataset about 1 month ago
  • arabic_vlm
    Refresh training code; link the published dataset about 1 month ago
  • HOWTO.md
    3.09 kB
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  • overfit_test.py
    4.07 kB
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  • pretrain.yaml
    1.4 kB
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  • pyproject.toml
    954 Bytes
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  • run_training.sh
    2 kB
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  • run_training_50m.sh
    2.09 kB
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  • sitecustomize.py
    1.11 kB
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  • smoke_test.py
    7.95 kB
    Add full training source and reproduction notes about 1 month ago