8 citations · 8 across the 2 of their papers we have counts for
6 papers · 1 filter
Systems and Algorithms for Convolutional Multi-Hybrid Language Models at Scale
Jerome Ku, Eric Nguyen, David W. Romero +13
We introduce convolutional multi-hybrid architectures, with a design grounded on two simple observations. First, operators in hybrid models can be tailored to token manipulation ta…
Mechanistic Design and Scaling of Hybrid Architectures
Michael Poli, Armin W Thomas, Eric Nguyen +9
The development of deep learning architectures is a resource-demanding process, due to a vast design space, long prototyping times, and high compute costs associated with at-scale…
FlashFFTConv: Efficient Convolutions for Long Sequences with Tensor Cores
Daniel Y. Fu, Hermann Kumbong, Eric Nguyen +1
Convolution models with long filters have demonstrated state-of-the-art reasoning abilities in many long-sequence tasks but lag behind the most optimized Transformers in wall-clock…
HyenaDNA: Long-Range Genomic Sequence Modeling at Single Nucleotide Resolution
Eric Nguyen, Michael Poli, Marjan Faizi +10
Genomic (DNA) sequences encode an enormous amount of information for gene regulation and protein synthesis. Similar to natural language models, researchers have proposed foundation…
Simple Hardware-Efficient Long Convolutions for Sequence Modeling
Daniel Y. Fu, Elliot L. Epstein, Eric Nguyen +5
State space models (SSMs) have high performance on long sequence modeling but require sophisticated initialization techniques and specialized implementations for high quality and r…
Hyena Hierarchy: Towards Larger Convolutional Language Models
Michael Poli, Stefano Massaroli, Eric Nguyen +6
Recent advances in deep learning have relied heavily on the use of large Transformers due to their ability to learn at scale. However, the core building block of Transformers, the…