2 citations · 2 across the 7 of their papers we have counts for
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How Local Mixing Encodes Relative Position in Global NoPE Attention
Cutter Dawes, Nick Alonso, Tom Figliolia +1
The attention operation is naively position invariant. However, positional information is fundamental to natural language, and therefore a variety of explicit position encodings ha…
Can Scale Save Us From Plasticity Loss in Large Language Models?
J. Fernando Hernandez-Garcia, Tomás Figliolia, Beren Millidge
The loss of plasticity - the ability of a network to learn new information after having already learned older information - is a fundamental challenge in creating artificial neural…
Scaling Adaptive Depth with Norm-Agnostic Residual Networks
Tomás Figliolia, Beren Millidge
Residual architectures are ubiquitous in deep learning, but they suffer from a subtle structural limitation: the norm of the residual stream can grow rapidly with depth. As a resul…
ZAYA1-8B Technical Report
Robert Washbourne, Rishi Iyer, Tomas Figliolia +15
We present ZAYA1-8B, a reasoning-focused mixture-of-experts (MoE) model with 700M active and 8B total parameters, built on Zyphra's MoE++ architecture. ZAYA1-8B's core pretraining,…
Hybrid Associative Memories
Leon Lufkin, Tomás Figliolia, Beren Millidge +1
Recurrent neural networks (RNNs) and self-attention are both widely used sequence-mixing layers that maintain an internal memory. However, this memory is constructed using two orth…
Online Vector Quantized Attention
Nick Alonso, Tomas Figliolia, Beren Millidge
Standard sequence mixing layers used in language models struggle to balance efficiency and performance. Self-attention performs well on long context tasks but has expensive quadrat…