MeZO enables larger models for on-device fine-tuning by estimating gradients via forward passes only, with theoretical size estimates and numerical results showing accuracy benefits when wall-clock time is sufficient.
From llms to edge: Parameter-efficient fine-tuning on edge devices
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
verdicts
UNVERDICTED 3roles
background 1polarities
background 1representative citing papers
Presents quantization, checkpointing, softmax approximation, and logits masking to achieve substantial peak memory reductions in LoRA fine-tuning of 3B LLMs.
DAT combines a small-large model cascade with fine-tuning and bandwidth-aware multi-stream transmission to deliver high-accuracy event recognition and low-latency alerts for video streams in edge-cloud systems.
citing papers explorer
-
On-Device Fine-Tuning via Backprop-Free Zeroth-Order Optimization
MeZO enables larger models for on-device fine-tuning by estimating gradients via forward passes only, with theoretical size estimates and numerical results showing accuracy benefits when wall-clock time is sufficient.
-
Techniques for Peak Memory Reduction for LoRA Fine-tuning of LLMs on Edge Devices
Presents quantization, checkpointing, softmax approximation, and logits masking to achieve substantial peak memory reductions in LoRA fine-tuning of 3B LLMs.
-
DAT: Dual-Aware Adaptive Transmission for Efficient Multimodal LLM Inference in Edge-Cloud Systems
DAT combines a small-large model cascade with fine-tuning and bandwidth-aware multi-stream transmission to deliver high-accuracy event recognition and low-latency alerts for video streams in edge-cloud systems.