4 citations · 6 across the 6 of their papers we have counts for
3 papers · 1 filter
REAP the Experts: Why Pruning Prevails for One-Shot MoE compression
Mike Lasby, Ivan Lazarevich, Nish Sinnadurai +3
Sparsely-activated Mixture-of-Experts (SMoE) models offer efficient pre-training and low latency but their large parameter counts create significant memory overhead, motivating res…
SD: Self-Distilled Sparse Drafters
Mike Lasby, Nish Sinnadurai, Valavan Manohararajah +3
Speculative decoding is a powerful technique for reducing the latency of Large Language Models (LLMs), offering a fault-tolerant framework that enables the use of highly compressed…
Command A: An Enterprise-Ready Large Language Model
Team Cohere, :, Aakanksha +227
In this report we describe the development of Command A, a powerful large language model purpose-built to excel at real-world enterprise use cases. Command A is an agent-optimised…