64 citations · 156 across the 18 of their papers we have counts for
4 papers · 1 filter
Apple Intelligence Foundation Language Models
Tom Gunter, Zirui Wang, Chong Wang +152
We present foundation language models developed to power Apple Intelligence features, including a ~3 billion parameter model designed to run efficiently on devices and a large serv…
Revisiting MoE and Dense Speed-Accuracy Comparisons for LLM Training
Xianzhi Du, Tom Gunter, Xiang Kong +5
Mixture-of-Experts (MoE) enjoys performance gain by increasing model capacity while keeping computation cost constant. When comparing MoE to dense models, prior work typically adop…
Large Language Model-guided Document Selection
Xiang Kong, Tom Gunter, Ruoming Pang
Large Language Model (LLM) pre-training exhausts an ever growing compute budget, yet recent research has demonstrated that careful document selection enables comparable model quali…
MM1: Methods, Analysis & Insights from Multimodal LLM Pre-training
Brandon McKinzie, Zhe Gan, Jean-Philippe Fauconnier +29
In this work, we discuss building performant Multimodal Large Language Models (MLLMs). In particular, we study the importance of various architecture components and data choices. T…