Megrez-Omni Technical Report
This work addresses the need for compact, fast, and robust multimodal AI models for edge devices, representing a strong specific gain rather than a foundational breakthrough.
The authors tackled the problem of creating efficient and versatile AI models for edge-side intelligence by introducing Megrez-3B-Instruct and Megrez-3B-Omni, which achieve state-of-the-art accuracy across text, image, and audio modalities.
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.