title: Aria: An Open Multimodal Native Mixture-of-Experts Model
summary: Information comes in diverse modalities. Multimodal native AI models are
  essential to integrate real-world information and deliver comprehensive
  understanding. While proprietary multimodal native models exist, their lack of
  openness imposes obstacles for adoptions, let alone adaptations. To fill this
  gap, we introduce Aria, an open multimodal native model with best-in-class
  performance across a wide range of multimodal, language, and coding tasks. Aria
  is a mixture-of-expert model with 3.9B and 3.5B activated parameters per visual
  token and text token, respectively. It outperforms Pixtral-12B and
  Llama3.2-11B, and is competitive against the best proprietary models on various
  multimodal tasks. We pre-train Aria from scratch following a 4-stage pipeline,
  which progressively equips the model with strong capabilities in language
  understanding, multimodal understanding, long context window, and instruction
  following. We open-source the model weights along with a codebase that
  facilitates easy adoptions and adaptations of Aria in real-world applications.
paper_authors._id: 67073615be29abe376815ae4
paper_authors.name: Dongxu Li
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paper_authors.fullname: Dongxu Li
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paper_authors.user: dxli1
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paper_authors.name: Yudong Liu
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paper_authors.name: Haoning Wu
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paper_authors.fullname: Haoning Wu, Teo
paper_authors.type: user
paper_authors.user: teowu
paper_authors._id: 67073615be29abe376815ae7
paper_authors.name: Yue Wang
paper_authors._id: 67073615be29abe376815ae8
paper_authors.name: Zhiqi Shen
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paper_authors.name: Bowen Qu
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paper_authors.fullname: Bowen(Brian) Qu
paper_authors.type: user
paper_authors.user: Coobiw
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paper_authors.name: Xinyao Niu
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paper_authors.fullname: Xinyao Niu
paper_authors.type: user
paper_authors.user: sirius-ctrl
paper_authors._id: 67073615be29abe376815aeb
paper_authors.name: Guoyin Wang
paper_authors.status: admin_assigned
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paper_authors.avatarUrl: /avatars/ae13c7b21fe9ced7541dcd664d1b94ed.svg
paper_authors.fullname: Guoyin Wang
paper_authors.type: user
paper_authors.user: guoyinwang
paper_authors._id: 67073615be29abe376815aec
paper_authors.name: Bei Chen
paper_authors._id: 67073615be29abe376815aed
paper_authors.name: Junnan Li
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-10-10T08:21:58.418Z
paper_authors._id: 61f9d3b54ac99e8a1bae85f4
paper_authors.avatarUrl: /avatars/ac47d13204dd22452e4bc46e280842d5.svg
paper_authors.fullname: JunnanLi
paper_authors.type: user
paper_authors.user: JunnanLi
paper_title: Aria: An Open Multimodal Native Mixture-of-Experts Model
paper_summary: Information comes in diverse modalities. Multimodal native AI models are
  essential to integrate real-world information and deliver comprehensive
  understanding. While proprietary multimodal native models exist, their lack of
  openness imposes obstacles for adoptions, let alone adaptations. To fill this
  gap, we introduce Aria, an open multimodal native model with best-in-class
  performance across a wide range of multimodal, language, and coding tasks. Aria
  is a mixture-of-expert model with 3.9B and 3.5B activated parameters per visual
  token and text token, respectively. It outperforms Pixtral-12B and
  Llama3.2-11B, and is competitive against the best proprietary models on various
  multimodal tasks. We pre-train Aria from scratch following a 4-stage pipeline,
  which progressively equips the model with strong capabilities in language
  understanding, multimodal understanding, long context window, and instruction
  following. We open-source the model weights along with a codebase that
  facilitates easy adoptions and adaptations of Aria in real-world applications.
paper_ai_summary: Aria is an open multimodal native AI model with best-in-class performance across various tasks, designed with a mixture-of-experts architecture and pre-trained through a four-stage pipeline.
paper_ai_keywords: mixture-of-expert model visual token text token pre-trained language understanding multimodal understanding long context window instruction following