9 citations · 11 across the 22 of their papers we have counts for
4 papers · 1 filter
PT-LLM: Post-Training Ternarization for Large Language Models
Xianglong Yan, Chengzhu Bao, Zhiteng Li +6
Large Language Models (LLMs) have shown impressive capabilities across diverse tasks, but their large memory and compute demands hinder deployment. Ternarization has gained attenti…
Quant-dLLM: Post-Training Extreme Low-Bit Quantization for Diffusion Large Language Models
Tianao Zhang, Zhiteng Li, Xianglong Yan +3
Diffusion large language models (dLLMs), which offer bidirectional context and flexible masked-denoising generation, are emerging as a compelling alternative to autoregressive (AR)…
Low-bit Model Quantization for Deep Neural Networks: A Survey
Kai Liu, Qian Zheng, Kaiwen Tao +9
With unprecedented rapid development, deep neural networks (DNNs) have deeply influenced almost all fields. However, their heavy computation costs and model sizes are usually unacc…
Progressive Binarization with Semi-Structured Pruning for LLMs
Xianglong Yan, Tianao Zhang, Zhiteng Li +2
Large language models (LLMs) have achieved remarkable progress in natural language processing, but their high computational and memory costs hinder deployment on resource-constrain…