SmolDocling: An ultra-compact vision-language model for end-to-end multi-modal document conversion

We introduce SmolDocling, an ultra-compact vision-language model targeting end-to-end document conversion. Our model comprehensively processes entire pages by generating DocTags, a new universal markup format that captures all page elements in their full context with location. Unlike existing approaches that rely on large foundational models, or ensemble solutions that rely on handcrafted pipelines of multiple specialized models, SmolDocling offers an end-to-end conversion for accurately capturing content, structure and spatial location of document elements in a 256M parameters vision-language model. SmolDocling exhibits robust performance in correctly reproducing document features such as code listings, tables, equations, charts, lists, and more across a diverse range of document types including business documents, academic papers, technical reports, patents, and forms -- significantly extending beyond the commonly observed focus on scientific papers. Additionally, we contribute novel publicly sourced datasets for charts, tables, equations, and code recognition. Experimental results demonstrate that SmolDocling competes with other Vision Language Models that are up to 27 times larger in size, while reducing computational requirements substantially. The model is currently available, datasets will be publicly available soon.

@article{nassar2025_2503.11576,
  title={ SmolDocling: An ultra-compact vision-language model for end-to-end multi-modal document conversion },
  author={ Ahmed Nassar and Andres Marafioti and Matteo Omenetti and Maksym Lysak and Nikolaos Livathinos and Christoph Auer and Lucas Morin and Rafael Teixeira de Lima and Yusik Kim and A. Said Gurbuz and Michele Dolfi and Miquel Farré and Peter W. J. Staar },
  journal={arXiv preprint arXiv:2503.11576},
  year={ 2025 }
}