activity
20232026
most citedOWQ: Outlier-Aware Weight Quantization for Efficient Fine-Tuning and Inference of Large Language Models

8 citations · 10 across the 7 of their papers we have counts for

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Showing 2024Show all

2 papers · 1 filter

cs.CV2024

PTQ4VM: Post-Training Quantization for Visual Mamba

Younghyun Cho, Changhun Lee, Seonggon Kim +1

Visual Mamba is an approach that extends the selective space state model, Mamba, to vision tasks. It processes image tokens sequentially in a fixed order, accumulating information…

cs.CL2024★ 1 cited

QEFT: Quantization for Efficient Fine-Tuning of LLMs

Changhun Lee, Jun-gyu Jin, Younghyun Cho +1

With the rapid growth in the use of fine-tuning for large language models (LLMs), optimizing fine-tuning while keeping inference efficient has become highly important. However, thi…