3 citations · 6 across the 8 of their papers we have counts for
8 papers
Sigma-MoE-Tiny Technical Report
Qingguo Hu, Zhenghao Lin, Ziyue Yang +12
Mixture-of-Experts (MoE) has emerged as a promising paradigm for foundation models due to its efficient and powerful scalability. In this work, we present Sigma-MoE-Tiny, an MoE la…
SIGMA: An AI-Empowered Training Stack on Early-Life Hardware
Lei Qu, Lianhai Ren, Peng Cheng +12
An increasing variety of AI accelerators is being considered for large-scale training. However, enabling large-scale training on early-life AI accelerators faces three core challen…
Beyond Sunk Costs: Boosting LLM Pre-training Efficiency via Orthogonal Growth of Mixture-of-Experts
Ruizhe Wang, Yucheng Ding, Xiao Liu +5
As the computational demands for pre-training Large Language Models (LLMs) continue to surge, the need for efficient training paradigms becomes critical. Despite the vast resources…
Training Matryoshka Mixture-of-Experts for Elastic Inference-Time Expert Utilization
Yaoxiang Wang, Qingguo Hu, Yucheng Ding +6
Mixture-of-Experts (MoE) has emerged as a promising paradigm for efficiently scaling large language models without a proportional increase in computational cost. However, the stand…
Detecting Manipulated Contents Using Knowledge-Grounded Inference
Mark Huasong Meng, Ruizhe Wang, Meng Xu +2
The detection of manipulated content, a prevalent form of fake news, has been widely studied in recent years. While existing solutions have been proven effective in fact-checking a…
Optimizing Large Language Model Training Using FP4 Quantization
Ruizhe Wang, Yeyun Gong, Xiao Liu +5
The growing computational demands of training large language models (LLMs) necessitate more efficient methods. Quantized training presents a promising solution by enabling low-bit…
FP8-LM: Training FP8 Large Language Models
Houwen Peng, Kan Wu, Yixuan Wei +17
In this paper, we explore FP8 low-bit data formats for efficient training of large language models (LLMs). Our key insight is that most variables, such as gradients and optimizer s…
Robust Embeddings Via Distributions
Kira A. Selby, Yinong Wang, Ruizhe Wang +4
Despite recent monumental advances in the field, many Natural Language Processing (NLP) models still struggle to perform adequately on noisy domains. We propose a novel probabilist…