13 citations · 27 across the 10 of their papers we have counts for
3 papers · 1 filter
Uncertainty-Aware Gradient Signal-to-Noise Data Selection for Instruction Tuning
Zhihang Yuan, Chengyu Yue, Long Huang +2
Instruction tuning is a standard paradigm for adapting large language models (LLMs), but modern instruction datasets are large, noisy, and redundant, making full-data fine-tuning c…
LLM Inference Unveiled: Survey and Roofline Model Insights
Zhihang Yuan, Yuzhang Shang, Yang Zhou +11
The field of efficient Large Language Model (LLM) inference is rapidly evolving, presenting a unique blend of opportunities and challenges. Although the field has expanded and is v…
ASVD: Activation-aware Singular Value Decomposition for Compressing Large Language Models
Zhihang Yuan, Yuzhang Shang, Yue Song +4
In this paper, we introduce a new post-training compression paradigm for Large Language Models (LLMs) to facilitate their wider adoption. We delve into LLM weight low-rank decompos…