4 papers
Quantize the Target, Quantize the Drafter: Efficient Inference with Qwen3.5-4B
Jaeyeon Kim, Jewon Lee, Bo-Kyeong Kim
This report describes our approach to the Efficient Qwen Competition, where the goal is to enable low-latency serving of Qwen3.5-4B on a resource-constrained NVIDIA A10G GPU. Our s…
ERGO: Efficient High-Resolution Visual Understanding for Vision-Language Models
Jewon Lee, Wooksu Shin, Seungmin Yang +5
Efficient processing of high-resolution images is crucial for real-world vision-language applications. However, existing Large Vision-Language Models (LVLMs) incur substantial comp…
Efficient LLaMA-3.2-Vision by Trimming Cross-attended Visual Features
Jewon Lee, Ki-Ung Song, Seungmin Yang +6
Visual token reduction lowers inference costs caused by extensive image features in large vision-language models (LVLMs). Unlike relevant studies that prune tokens in self-attentio…
Assessing the Answerability of Queries in Retrieval-Augmented Code Generation
Geonmin Kim, Jaeyeon Kim, Hancheol Park +2
Thanks to unprecedented language understanding and generation capabilities of large language model (LLM), Retrieval-augmented Code Generation (RaCG) has recently been widely utiliz…