1 citations · 2 across the 17 of their papers we have counts for
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Learnable Permutation for Structured Sparsity on Transformer Models
Zekai Li, Ji Liu, Guanchen Li +5
Structured sparsity has emerged as a popular model pruning technique, widely adopted in various architectures, including CNNs, Transformer models, and especially large language mod…
PARD: Accelerating LLM Inference with Low-Cost PARallel Draft Model Adaptation
Zihao An, Huajun Bai, Ziqiong Liu +2
The autoregressive nature of large language models (LLMs) fundamentally limits inference speed, as each forward pass generates only a single token and is often bottlenecked by memo…
Týr-the-Pruner: Structural Pruning LLMs via Global Sparsity Distribution Optimization
Guanchen Li, Yixing Xu, Zeping Li +4
Structural pruning enhances hardware-agnostic inference efficiency for large language models (LLMs) yet often fails to maintain comparable performance. Local pruning performs effic…
TernaryLLM: Ternarized Large Language Model
Tianqi Chen, Zhe Li, Weixiang Xu +6
Large language models (LLMs) have achieved remarkable performance on Natural Language Processing (NLP) tasks, but they are hindered by high computational costs and memory requireme…