4 citations · 8 across the 9 of their papers we have counts for
5 papers · 1 filter
The Devil Is in the Reconstruction Loss Scale: Rethinking Optimization in LLM Quantization
Chao Li, Shigeng Wang, Anbang Yao
Post-training quantization (PTQ) methods typically use sequential quantization that partitions a pre-trained LLM into a series of units (e.g., transformer blocks), with one unit qu…
Attend to Your Own Thoughts: Breaking the Barrier for Post-Training Quantization of Reasoning LLMs through the Lens of 1.58-Bit Quantization
Shigeng Wang, Chao Li, Yangyuxuan Kang +2
We propose ScaleQ-1.58, a scalable ternary post-training quantization (PTQ) framework for reasoning LLMs. Its core insight stems from an empirical finding: although modern LLMs are…
CAT-Q: Cost-efficient and Accurate Ternary Quantization for LLMs
Shigeng Wang, Chao Li, Yangyuxuan Kang +2
In this paper, we present CAT-Q, Cost-efficient and Accurate Ternary Quantization, for compressing and accelerating LLMs. Unlike existing state-of-the-art ternary quantization meth…
Chain-of-Models Pre-Training: Rethinking Training Acceleration of Vision Foundation Models
Jiawei Fan, Shigeng Wang, Chao Li +2
In this paper, we present Chain-of-Models Pre-Training (CoM-PT), a novel performance-lossless training acceleration method for vision foundation models (VFMs). This approach fundam…
SliderQuant: Accurate Post-Training Quantization for LLMs
Shigeng Wang, Chao Li, Yangyuxuan Kang +3
In this paper, we address post-training quantization (PTQ) for large language models (LLMs) from an overlooked perspective: given a pre-trained high-precision LLM, the predominant…