Aria: An Open Multimodal Native Mixture-of-Experts Model
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.
67073615be29abe376815ae4
Dongxu Li
claimed_verified
2024-10-10T08:01:43.587Z
6357362f811ee2fa05070f64
/avatars/2cf37efb80f5cfb3e4e9d08674de6dd1.svg
Dongxu Li
user
dxli1
67073615be29abe376815ae5
Yudong Liu
67073615be29abe376815ae6
Haoning Wu
claimed_verified
2024-10-10T08:01:45.855Z
63047ed2412a1b9d381b09c9
https://cdn-avatars.huggingface.co/v1/production/uploads/63047ed2412a1b9d381b09c9/2Ill5G0uSMyGstrawgmIb.jpeg
Haoning Wu, Teo
user
teowu
67073615be29abe376815ae7
Yue Wang
67073615be29abe376815ae8
Zhiqi Shen
67073615be29abe376815ae9
Bowen Qu
claimed_verified
2025-01-16T19:44:15.924Z
64be2d5e86e7fb5b8a7ca957
https://cdn-avatars.huggingface.co/v1/production/uploads/64be2d5e86e7fb5b8a7ca957/r_CG8vhogZAvUsW0i9C20.jpeg
Bowen(Brian) Qu
user
Coobiw
67073615be29abe376815aea
Xinyao Niu
admin_assigned
2024-10-10T08:21:06.702Z
627f4c100d2c1c0ba3da8a72
/avatars/404869a87480ef526c1d5fccb583bfa8.svg
Xinyao Niu
user
sirius-ctrl
67073615be29abe376815aeb
Guoyin Wang
admin_assigned
2024-10-10T08:21:17.466Z
6490d4ba1afdee3acd1147f6
/avatars/ae13c7b21fe9ced7541dcd664d1b94ed.svg
Guoyin Wang
user
guoyinwang
67073615be29abe376815aec
Bei Chen
67073615be29abe376815aed
Junnan Li
admin_assigned
2024-10-10T08:21:58.418Z
61f9d3b54ac99e8a1bae85f4
/avatars/ac47d13204dd22452e4bc46e280842d5.svg
JunnanLi
user
JunnanLi
Aria: An Open Multimodal Native Mixture-of-Experts Model
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.
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.
mixture-of-expert model visual token text token pre-trained language understanding multimodal understanding long context window instruction following