4 papers · 1 filter
One-Step Generation via Riemannian Wasserstein Gradient Flows
David Li, Chanhyuk Lee, Jaehoon Yoo +7
Recently, Drifting Models and Wasserstein Gradient Flows have attracted substantial attention because they move iterative distributional refinement to training and amortize it into…
PatchKV: Weight-Space Compensation of KV Cache
Chanryeol Lee, Chanhyuk Lee, Yeonwoo Choi +2
Long-context inference with Large Language Models (LLMs) is bottlenecked by the linearly growing memory of the key-value (KV) cache. Existing compression methods reduce the cache t…
AdaRank: Adaptive Rank Pruning for Enhanced Model Merging
Chanhyuk Lee, Jiho Choi, Chanryeol Lee +2
Model merging has emerged as a promising approach for unifying independently fine-tuned models into an integrated framework, significantly enhancing computational efficiency in mul…
Revisiting Weight Averaging for Model Merging
Jiho Choi, Donggyun Kim, Chanhyuk Lee +1
Model merging aims to build a multi-task learner by combining the parameters of individually fine-tuned models without additional training. While a straightforward approach is to a…