5 papers · 1 filter
Intern-S1-Pro: Scientific Multimodal Foundation Model at Trillion Scale
Yicheng Zou, Dongsheng Zhu, Lin Zhu +174
We introduce Intern-S1-Pro, the first one-trillion-parameter scientific multimodal foundation model. Scaling to this unprecedented size, the model delivers a comprehensive enhancem…
What Makes Position Zero Special? A Mechanistic Study of Position Zero Attention Sinks in LLMs
Runyu Peng, Ruixiao Li, Mingshu Chen +5
Transformers frequently allocate disproportionate attention to specific tokens, a phenomenon known as attention sinks. Causal large language models reliably form one at position ze…
Explicit Multi-head Attention for Inter-head Interaction in Large Language Models
Runyu Peng, Yunhua Zhou, Demin Song +4
In large language models built upon the Transformer architecture, recent studies have shown that inter-head interaction can enhance attention performance. Motivated by this, we pro…
Intern-S1: A Scientific Multimodal Foundation Model
Lei Bai, Zhongrui Cai, Yuhang Cao +173
In recent years, a plethora of open-source foundation models have emerged, achieving remarkable progress in some widely attended fields, with performance being quite close to that…
Implicit Reward as the Bridge: A Unified View of SFT and DPO Connections
Bo Wang, Qinyuan Cheng, Runyu Peng +7
Post-training processes are essential phases in grounding pre-trained language models to real-world tasks, with learning from demonstrations or preference signals playing a crucial…