1 citations · 1 across the 4 of their papers we have counts for
4 papers
DIVE into MoE: Diversity-Enhanced Reconstruction of Large Language Models from Dense into Mixture-of-Experts
Yuchen Feng, Bowen Shen, Naibin Gu +4
Large language models (LLMs) with the Mixture-of-Experts (MoE) architecture achieve high cost-efficiency by selectively activating a subset of the parameters. Despite the inference…
TailorKV: A Hybrid Framework for Long-Context Inference via Tailored KV Cache Optimization
Dingyu Yao, Bowen Shen, Zheng Lin +4
The Key-Value (KV) cache in generative large language models (LLMs) introduces substantial memory overhead. Existing works mitigate this burden by offloading or compressing the KV…
Pruning Large Language Models to Intra-module Low-rank Architecture with Transitional Activations
Bowen Shen, Zheng Lin, Daren Zha +4
Structured pruning fundamentally reduces computational and memory overheads of large language models (LLMs) and offers a feasible solution for end-side LLM deployment. Structurally…
Light-PEFT: Lightening Parameter-Efficient Fine-Tuning via Early Pruning
Naibin Gu, Peng Fu, Xiyu Liu +3
Parameter-efficient fine-tuning (PEFT) has emerged as the predominant technique for fine-tuning in the era of large language models. However, existing PEFT methods still have inade…