Megrez-Omni Technical Report
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
–arXiv.org Artificial Intelligence
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.
arXiv.org Artificial Intelligence
Feb-19-2025
- Genre:
- Research Report (0.64)
- Industry:
- Education (0.67)
- Technology:
- Information Technology > Artificial Intelligence
- Machine Learning > Neural Networks
- Deep Learning (1.00)
- Natural Language
- Chatbot (1.00)
- Large Language Model (1.00)
- Representation & Reasoning (1.00)
- Vision (1.00)
- Machine Learning > Neural Networks
- Information Technology > Artificial Intelligence