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Bibliographic Details
Main Authors:	Li, Boxun, Li, Yadong, Li, Zhiyuan, Liu, Congyi, Liu, Weilin, Niu, Guowei, Tan, Zheyue, Xu, Haiyang, Yao, Zhuyu, Yuan, Tao, Zhou, Dong, Zhuang, Yueqing, Yan, Shengen, Dai, Guohao, Wang, Yu
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Computation and Language
Online Access:	https://arxiv.org/abs/2502.15803
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Table of Contents:

In this work, we present the Megrez models, comprising a language model (Megrez-3B-Instruct) and a multimodal model (Megrez-3B-Omni). These models are designed to deliver fast inference, compactness, and robust edge-side intelligence through a software-hardware co-design approach. Megrez-3B-Instruct offers several advantages, including high accuracy, high speed, ease of use, and a wide range of applications. Building on Megrez-3B-Instruct, Megrez-3B-Omni is an on-device multimodal understanding LLM that supports image, text, and audio analysis. It achieves state-of-the-art accuracy across all three modalities and demonstrates strong versatility and robustness, setting a new benchmark for multimodal AI models.

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