4 papers
Rethinking 1-bit Optimization Leveraging Pre-trained Large Language Models
Zhijun Tu, Jian Li, Yuanyuan Xi +5
1-bit LLM quantization offers significant advantages in reducing storage and computational costs. However, existing methods typically train 1-bit LLMs from scratch, failing to full…
Transferable text data distillation by trajectory matching
Rong Yao, Hailin Hu, Yifei Fu +5
In the realm of large language model (LLM), as the size of large models increases, it also brings higher training costs. There is a urgent need to minimize the data size in LLM tra…
CBQ: Cross-Block Quantization for Large Language Models
Xin Ding, Xiaoyu Liu, Zhijun Tu +8
Post-training quantization (PTQ) has played a key role in compressing large language models (LLMs) with ultra-low costs. However, existing PTQ methods only focus on handling the ou…
SAM-DiffSR: Structure-Modulated Diffusion Model for Image Super-Resolution
Chengcheng Wang, Zhiwei Hao, Yehui Tang +4
Diffusion-based super-resolution (SR) models have recently garnered significant attention due to their potent restoration capabilities. But conventional diffusion models perform no…