title: SmolDocling: An ultra-compact vision-language model for end-to-end
    multi-modal document conversion
summary: 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.
paper_authors._id: 67d7d1c38a15934c10b1576d
paper_authors.name: Ahmed Nassar
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-18T08:09:03.445Z
paper_authors._id: 63c64fdd77caf00391006211
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1673940984052-63c64fdd77caf00391006211.jpeg
paper_authors.fullname: Ahmed Nassar
paper_authors.type: user
paper_authors.user: asnassar
paper_authors._id: 67d7d1c38a15934c10b1576e
paper_authors.name: Andres Marafioti
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-17T14:42:18.190Z
paper_authors._id: 65d66b494bbd0d92b641cdbb
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/65d66b494bbd0d92b641cdbb/6-7dm7B-JxcoS1QlCPdMN.jpeg
paper_authors.fullname: Andres Marafioti
paper_authors.type: user
paper_authors.user: andito
paper_authors._id: 67d7d1c38a15934c10b1576f
paper_authors.name: Matteo Omenetti
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-17T15:05:38.055Z
paper_authors._id: 663e1254887b6f5645a0399f
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/663e1254887b6f5645a0399f/CXtmipcpwK3LMIQNeinVg.jpeg
paper_authors.fullname: Matteo Omenetti
paper_authors.type: user
paper_authors.user: MatteoOmenetti
paper_authors._id: 67d7d1c38a15934c10b15770
paper_authors.name: Maksym Lysak
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-17T16:32:51.359Z
paper_authors._id: 665a2d536a8e042a0bf9b7b0
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/665a2d536a8e042a0bf9b7b0/wA8QmKPW1BSRWNJyx_st0.jpeg
paper_authors.fullname: Maksym Lysak
paper_authors.type: user
paper_authors.user: MaxMnemonic
paper_authors._id: 67d7d1c38a15934c10b15771
paper_authors.name: Nikolaos Livathinos
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-17T16:32:49.368Z
paper_authors._id: 67938030aa5e507f71eb0e44
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/67938030aa5e507f71eb0e44/U_7TxvZFuTeJ-Gtfatbzj.jpeg
paper_authors.fullname: Nikos Livathinos
paper_authors.type: user
paper_authors.user: nlivathinos
paper_authors._id: 67d7d1c38a15934c10b15772
paper_authors.name: Christoph Auer
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-17T15:05:12.752Z
paper_authors._id: 67aa428095116c7b8e4204d4
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/dT638jwk8CTQ6tGJ0ullR.png
paper_authors.fullname: Christoph Auer
paper_authors.type: user
paper_authors.user: ChristophAuer
paper_authors._id: 67d7d1c38a15934c10b15773
paper_authors.name: Lucas Morin
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-17T15:03:18.614Z
paper_authors._id: 6628967d99b11a1df0b1f256
paper_authors.avatarUrl: /avatars/22d5a465df2b839e0f804521c732318b.svg
paper_authors.fullname: Lucas Morin
paper_authors.type: user
paper_authors.user: LucasMorin
paper_authors._id: 67d7d1c38a15934c10b15774
paper_authors.name: Rafael Teixeira de Lima
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-17T15:03:11.221Z
paper_authors._id: 65ce0dbef3bbc55ca4d0528f
paper_authors.avatarUrl: /avatars/ae19424457dbec49579fe5a4ae7177b0.svg
paper_authors.fullname: Rafael Teixeira de Lima
paper_authors.type: user
paper_authors.user: rtdl
paper_authors._id: 67d7d1c38a15934c10b15775
paper_authors.name: Yusik Kim
paper_authors._id: 67d7d1c38a15934c10b15776
paper_authors.name: A. Said Gurbuz
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-17T15:03:01.577Z
paper_authors._id: 65d4dab8be26e8e084d29b98
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/65d4dab8be26e8e084d29b98/67m-1xtaONgP3sJCw5_k1.jpeg
paper_authors.fullname: Said Gurbuz
paper_authors.type: user
paper_authors.user: Saidgurbuz
paper_authors._id: 67d7d1c38a15934c10b15777
paper_authors.name: Michele Dolfi
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-17T15:02:52.883Z
paper_authors._id: 63c64dd877caf00391004e20
paper_authors.avatarUrl: /avatars/6350443da6405ea7beccf47a80d8710e.svg
paper_authors.fullname: Michele Dolfi
paper_authors.type: user
paper_authors.user: dolfim-ibm
paper_authors._id: 67d7d1c38a15934c10b15778
paper_authors.name: Miquel Farré
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-17T15:05:05.893Z
paper_authors._id: 61ed0ff29539bc0a3bbc89f4
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/61ed0ff29539bc0a3bbc89f4/iYWK7GParA7Ke5F6q132W.jpeg
paper_authors.fullname: Miquel Farré
paper_authors.type: user
paper_authors.user: mfarre
paper_authors._id: 67d7d1c38a15934c10b15779
paper_authors.name: Peter W. J. Staar
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-17T15:01:33.085Z
paper_authors._id: 63c6522d8fddd19431f48bcc
paper_authors.avatarUrl: /avatars/e2ec9c76656b7520472718595f9dafe1.svg
paper_authors.fullname: Peter W. J. Staar
paper_authors.type: user
paper_authors.user: PeterStaar
paper_title: SmolDocling: An ultra-compact vision-language model for end-to-end
    multi-modal document conversion
paper_summary: 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.
paper_ai_keywords: ultra-compact vision-language model end-to-end document conversion DocTags universal markup format page elements full context location foundational models ensemble solutions handcrafted pipelines specialized models vision-language model parameters document features code listings tables equations charts lists document types business documents academic papers technical reports patents forms publicly sourced datasets charts tables equations code recognition Vision Language Models