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Ming-Flash-Omni: A Sparse, Unified Architecture for Multimodal Perception and Generation
arXiv – CS AI|Inclusion AI, :, Bowen Ma, Cheng Zou, ChengKun Du, Canxiang Yan, Chunxiang Jin, Chunjie Shen, Chenyu Lian, Chengxiang Fan, Dandan Zheng, Fudong Wang, Furong Xu, Guangming Yao, Haohao Liu, Han Peng, Jun Zhou, Junluan Xia, Jingdong Chen, Jianing Li, Jianxin Sun, Jianjiang Zhu, Jianping Jiang, Jinpeng Ou, Jun Peng, Jin Peng, Kaixiang Ji, Li Tang, Libin Wang, Lixiang Ru, Longhua Tan, Lu Ma, Lan Wang, Mochen Bai, Minghong Cai, Mingxue Yang, Ning Gao, Qingpei Guo, Qinglong Zhang, Qiang Xu, Qin Zhao, Rui Liu, Ruijie Xiong, Ruobing Zheng, Sirui Gao, Shaoxiong Lin, Tao Zhang, Tianqi Li, Tinghao Liu, Tongli Wang, Taoye Huang, Weilong Chai, Xiaomei Wang, Xiaolong Wang, Xiaojian Liu, Xiao Lu, Xiaoyu Li, Xingning Dong, Xuzheng Yu, Xuezhi Wang, Yi Yuan, Yuting Gao, Yuting Xiao, Yunxiao Sun, Yipeng Chen, Yifan Mao, Yifei Wu, Yongjie Lyu, Yingying Zhang, YuQian Li, Ziping Ma, Zhiqiang Fang, Zhihao Qiu, Ziyuan Huang, Zizheng Yang, Zhengyu He|
🤖AI Summary
Ming-Flash-Omni is a new 100 billion parameter multimodal AI model with Mixture-of-Experts architecture that uses only 6.1 billion active parameters per token. The model demonstrates unified capabilities across vision, speech, and language tasks, achieving performance comparable to Gemini 2.5 Pro on vision-language benchmarks.
Key Takeaways
- →Ming-Flash-Omni uses sparse MoE architecture with 100B total parameters but only 6.1B active per token for computational efficiency.
- →The model achieves performance on par with Gemini 2.5 Pro on vision-language understanding benchmarks.
- →It enables seamless multimodal task switching in conversations across vision, speech, and language.
- →The architecture supports continuous generation of speech, sound, and music with contextual awareness.
- →Introduces generative semantic segmentation with improved spatial control and identity preservation capabilities.
Mentioned in AI
Models
GeminiGoogle
#multimodal-ai#mixture-of-experts#moe#computer-vision#speech-recognition#artificial-general-intelligence#agi#neural-networks#deep-learning
Read Original →via arXiv – CS AI
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