6 papers
ixi-sLM: On-Device sLM for AI Call Agent Service
Han-Sang Lee, Kyuho Lee, Juneyoung Park +10
Running an AI call agent on-device makes it private, network-independent, and free of per-query cloud cost, but shipping one as a real-world service imposes constraints a cloud dep…
Opt.Gear Technical Report
Juneyoung Park, Youngwook Kwon
We introduce OptGear, a foundation model designed for efficient on-device deployment, real-tim inference, and strong task capability. It includes a dense model (1M, 270M, and 1B) w…
FoRA: Fisher-orthogonal Rank Adaptation for Parameter-Efficient Fine-Tuning
Juneyoung Park, Seongbae Lee, Han-Sang Lee +6
Parameter-efficient fine-tuning(PEFT) has largely focused on LoRA and its accuracy-oriented variants, leaving the original goal of reducing trainable parameters has receivedcompara…
Rethinking the Rank Threshold for LoRA Fine-Tuning
Juneyoung Park
A recent landscape analysis of LoRA fine-tuning in the neural tangent kernel regime establishes a sufficient condition on the LoRA rank for the absence of spuri…
LCSB: Layer-Cyclic Selective Backpropagation for Memory-Efficient On-Device LLM Fine-Tuning
Juneyoung Park, Eunbeen Yoon, Seongwan Kim. Jaeho Lee
Memory-efficient backpropagation (MeBP) has enabled first-order fine-tuning of large language models (LLMs) on mobile devices with less than 1GB memory. However, MeBP requires back…
Memory-Efficient Structured Backpropagation for On-Device LLM Fine-Tuning
Juneyoung Park, Yuri Hong, Seongwan Kim +1
On-device fine-tuning enables privacy-preserving personalization of large language models, but mobile devices impose severe memory constraints, typically 6--12GB shared across all…