1 citations · 1 across the 1 of their papers we have counts for
4 papers
Mixture-of-Experts Can Surpass Dense LLMs Under Strictly Equal Resource
Houyi Li, Ka Man Lo, Shijie Xuyang +7
Mixture-of-Experts (MoE) language models dramatically expand model capacity and achieve remarkable performance without increasing per-token compute. However, can MoEs surpass dense…
Step 3.5 Flash: Open Frontier-Level Intelligence with 11B Active Parameters
Ailin Huang, Ang Li, Aobo Kong +213
We introduce Step 3.5 Flash, a sparse Mixture-of-Experts (MoE) model that bridges frontier-level agentic intelligence and computational efficiency. We focus on what matters most wh…
A Closer Look into Mixture-of-Experts in Large Language Models
Ka Man Lo, Zeyu Huang, Zihan Qiu +2
Mixture-of-experts (MoE) is gaining increasing attention due to its unique properties and remarkable performance, especially for language tasks. By sparsely activating a subset of…
MuPT: A Generative Symbolic Music Pretrained Transformer
Xingwei Qu, Yuelin Bai, Yinghao Ma +25
In this paper, we explore the application of Large Language Models (LLMs) to the pre-training of music. While the prevalent use of MIDI in music modeling is well-established, our f…