8 citations · 13 across the 7 of their papers we have counts for
4 papers · 1 filter
GraLoRA: Granular Low-Rank Adaptation for Parameter-Efficient Fine-Tuning
Yeonjoon Jung, Daehyun Ahn, Hyungjun Kim +2
Low-Rank Adaptation (LoRA) is a popular method for parameter-efficient fine-tuning (PEFT) of generative models, valued for its simplicity and effectiveness. Despite recent enhancem…
Mixture of Scales: Memory-Efficient Token-Adaptive Binarization for Large Language Models
Dongwon Jo, Taesu Kim, Yulhwa Kim +1
Binarization, which converts weight parameters to binary values, has emerged as an effective strategy to reduce the size of large language models (LLMs). However, typical binarizat…
QUICK: Quantization-aware Interleaving and Conflict-free Kernel for efficient LLM inference
Taesu Kim, Jongho Lee, Daehyun Ahn +4
We introduce QUICK, a group of novel optimized CUDA kernels for the efficient inference of quantized Large Language Models (LLMs). QUICK addresses the shared memory bank-conflict p…
Squeezing Large-Scale Diffusion Models for Mobile
Jiwoong Choi, Minkyu Kim, Daehyun Ahn +6
The emergence of diffusion models has greatly broadened the scope of high-fidelity image synthesis, resulting in notable advancements in both practical implementation and academic…