Aria: An Open Multimodal Native Mixture-of-Experts Model
Authors: name: Dongxu Li, name: Yudong Liu, name: Haoning Wu, name: Yue Wang, name: Zhiqi Shen, name: Bowen Qu, name: Xinyao Niu, name: Guoyin Wang, name: Bei Chen, name: Junnan Li

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.