25 citations · 31 across the 22 of their papers we have counts for
12 papers · 1 filter
Block-Sparse Attention with Semantic-Geometric Decoupled Routing
Xinwei Long, Weigao Sun, Weibo Gao +6
Long-context inference has become a defining capability of large language models, but exact dense attention remains costly due to its quadratic scaling with sequence length. Block-…
Elucidating the Design Space of Decay in Linear Attention
Zhen Qin, Xuyang Shen, Yiran Zhong
This paper presents a comprehensive investigation into the decay mechanisms inherent in linear complexity sequence models. We systematically delineate the design space of decay mec…
MiniMax-01: Scaling Foundation Models with Lightning Attention
MiniMax, Aonian Li, Bangwei Gong +87
We introduce MiniMax-01 series, including MiniMax-Text-01 and MiniMax-VL-01, which are comparable to top-tier models while offering superior capabilities in processing longer conte…
Scaling Laws for Linear Complexity Language Models
Xuyang Shen, Dong Li, Ruitao Leng +3
The interest in linear complexity models for large language models is on the rise, although their scaling capacity remains uncertain. In this study, we present the scaling laws for…
Various Lengths, Constant Speed: Efficient Language Modeling with Lightning Attention
Zhen Qin, Weigao Sun, Dong Li +3
We present Lightning Attention, the first linear attention implementation that maintains a constant training speed for various sequence lengths under fixed memory consumption. Due…
You Only Scan Once: Efficient Multi-dimension Sequential Modeling with LightNet
Zhen Qin, Yuxin Mao, Xuyang Shen +4
Linear attention mechanisms have gained prominence in causal language models due to their linear computational complexity and enhanced speed. However, the inherent decay mechanism…
Unlocking the Secrets of Linear Complexity Sequence Model from A Unified Perspective
Zhen Qin, Xuyang Shen, Dong Li +4
We present the Linear Complexity Sequence Model (LCSM), a comprehensive solution that unites various sequence modeling techniques with linear complexity, including linear attention…
HGRN2: Gated Linear RNNs with State Expansion
Zhen Qin, Songlin Yang, Weixuan Sun +4
Hierarchically gated linear RNN (HGRN, \citealt{HGRN}) has demonstrated competitive training speed and performance in language modeling while offering efficient inference. However,…
CO2: Efficient Distributed Training with Full Communication-Computation Overlap
Weigao Sun, Zhen Qin, Weixuan Sun +5
The fundamental success of large language models hinges upon the efficacious implementation of large-scale distributed training techniques. Nevertheless, building a vast, high-perf…
Lightning Attention-2: A Free Lunch for Handling Unlimited Sequence Lengths in Large Language Models
Zhen Qin, Weigao Sun, Dong Li +3
Linear attention is an efficient attention mechanism that has recently emerged as a promising alternative to conventional softmax attention. With its ability to process tokens in l…
Accelerating Toeplitz Neural Network with Constant-time Inference Complexity
Zhen Qin, Yiran Zhong
Toeplitz Neural Networks (TNNs) have exhibited outstanding performance in various sequence modeling tasks. They outperform commonly used Transformer-based models while benefiting f…
Hierarchically Gated Recurrent Neural Network for Sequence Modeling
Zhen Qin, Songlin Yang, Yiran Zhong
Transformers have surpassed RNNs in popularity due to their superior abilities in parallel training and long-term dependency modeling. Recently, there has been a renewed interest i…