4 papers
Signs Beat Floats: Low-Rank Double-Binary Adaptation for On-Device Fine-Tuning
Yoshihiko Fujisawa, Yuma Ichikawa, Yudai Fujimoto +2
On-device adaptation of large language models commonly keeps a quantized base model frozen while training and deploying a small, task-specific LoRA adapter. In the unmerged adapter…
OneComp: One-Line Revolution for Generative AI Model Compression
Yuma Ichikawa, Keiji Kimura, Akihiro Yoshida +11
Deploying foundation models is increasingly constrained by memory footprint, latency, and hardware costs. Post-training compression can mitigate these bottlenecks by reducing the p…
More Than Bits: Multi-Envelope Double Binary Factorization for Extreme Quantization
Yuma Ichikawa, Yoshihiko Fujisawa, Yudai Fujimoto +2
For extreme low-bit quantization of large language models (LLMs), Double Binary Factorization (DBF) is attractive as it enables efficient inference without sacrificing accuracy. Ho…
LPCD: Unified Framework from Layer-Wise to Submodule Quantization
Yuma Ichikawa, Yudai Fujimoto, Akira Sakai
Post-training quantization (PTQ) aims to preserve model-level behavior; however, most methods focus on individual linear layers. Even recent extensions, such as QEP and LoaQ, which…