2 citations · 2 across the 5 of their papers we have counts for
4 papers · 1 filter
A Simple Linear Patch Revives Layer-Pruned Large Language Models
Xinrui Chen, Haoli Bai, Tao Yuan +7
Layer pruning has emerged as a widely used technique for compressing large language models (LLMs). However, existing layer pruning approaches often incur substantial performance de…
Quantization Hurts Reasoning? An Empirical Study on Quantized Reasoning Models
Ruikang Liu, Yuxuan Sun, Manyi Zhang +5
Recent advancements in reasoning language models have demonstrated remarkable performance in complex tasks, but their extended chain-of-thought reasoning process increases inferenc…
FlatQuant: Flatness Matters for LLM Quantization
Yuxuan Sun, Ruikang Liu, Haoli Bai +10
Recently, quantization has been widely used for the compression and acceleration of large language models (LLMs). Due to the outliers in LLMs, it is crucial to flatten weights and…
IntactKV: Improving Large Language Model Quantization by Keeping Pivot Tokens Intact
Ruikang Liu, Haoli Bai, Haokun Lin +6
Large language models (LLMs) excel in natural language processing but demand intensive computation. To mitigate this, various quantization methods have been explored, yet they comp…