LightTransfer: Your Long-Context LLM is Secretly a Hybrid Model with Effortless Adaptation

Xuan Zhang, Fengzhuo Zhang, Cunxiao Du, Chao Du, Tianyu Pang, Wei Gao, Min Lin

Keywords: Hybrid Model, Long-Context Understanding, o1-like Long Reasoning Generation

TL;DR: We propose a lightweight method that transfers vanilla transformer into a hybrid architecture.

Abstract:

Scaling language models to handle longer contexts introduces substantial memory challenges due to the growing cost of key-value (KV) caches. Motivated by the efficiency gains of hybrid models and the broad availability of pretrained large transformer backbones, we explore transitioning transformer models into hybrid architectures for a more efficient generation. In this work, we propose LightTransfer, a lightweight method that transforms models such as LLaMA into hybrid variants. Our approach identifies lazy layers—those focusing on recent or initial tokens—and replaces their full attention with streaming attention. This transformation can be performed without any training for long-context understanding tasks or with minimal fine-tuning for o1-like long reasoning generation tasks that require stronger reasoning capabilities. Experiments across diverse benchmarks and models (e.g., LLaMA, Mistral, QwQ-STILL) demonstrate that, even when half of the layers are identified as lazy, LightTransfer achieves up to 2.17
throughput improvement with minimal performance loss (
on LongBench) and achieves 53.3% on math benchmark AIME24 of advanced o1-like long reasoning model QwQ-STILL.

Submission Number: 76