10 citations · 26 across the 22 of their papers we have counts for
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cs.LG2024
CorDA: Context-Oriented Decomposition Adaptation of Large Language Models for Task-Aware Parameter-Efficient Fine-tuning
Yibo Yang, Xiaojie Li, Zhongzhu Zhou +4
Current parameter-efficient fine-tuning (PEFT) methods build adapters widely agnostic of the context of downstream task to learn, or the context of important knowledge to maintain.…
cs.CV2024★ 2 cited
Dragonfly: Multi-Resolution Zoom-In Encoding Enhances Vision-Language Models
Rahul Thapa, Kezhen Chen, Ian Covert +4
Recent advances in vision-language models (VLMs) have demonstrated the advantages of processing images at higher resolutions and utilizing multi-crop features to preserve native re…
cs.LG2024★ 3 cited
FP6-LLM: Efficiently Serving Large Language Models Through FP6-Centric Algorithm-System Co-Design
Haojun Xia, Zhen Zheng, Xiaoxia Wu +10
Six-bit quantization (FP6) can effectively reduce the size of large language models (LLMs) and preserve the model quality consistently across varied applications. However, existing…