Megrez-Omni Technical Report
Fuente:
arXiv
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| Autores principales: | , , , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866913708096094208 |
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| author | 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 |
| author_facet | 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 |
| 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_15803 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | 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 Machine Learning Computation and Language 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. |
| title | Megrez-Omni Technical Report |
| topic | Machine Learning Computation and Language |
| url | https://arxiv.org/abs/2502.15803 |