5 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)…
RobuQ: Pushing DiTs to W1.58A2 via Robust Activation Quantization
Kaicheng Yang, Xun Zhang, Haotong Qin +4
Diffusion Transformers (DiTs) have recently emerged as a powerful backbone for image generation, demonstrating superior scalability and performance over U-Net architectures. Howeve…
ReCalKV: Low-Rank KV Cache Compression via Head Reordering and Offline Calibration
Xianglong Yan, Zhiteng Li, Tianao Zhang +4
Large language models (LLMs) have demonstrated remarkable performance, but their long-context reasoning remains constrained by the excessive memory required for the Key-Value (KV)…
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…