ai.hackcv
论文精选 65arXiv

Why Gated DeltaNet Survives 4-Bit Quantization: NVFP4 W4A4 for the Recurrent Half of a Hybrid 27B LLM· 为什么Gated DeltaNet能承受4位量化

Hybrid LLMs pair softmax attention with linear-attention layers such as Gated DeltaNet (GDN), whose recurrent state summarizes the context in fixed size. Early community 4-bit quantizations of Qwen3.8-27B (48 GDN layers, 16 attention layers) left the GDN block in 8- or 16-bit precision -- especially its decay and write-strength gates -- on the intuition that errors in a recurrence accumulate over long contexts. We test that intuition by building Minima: NVFP4 W4A4 on all 496 linear layers, GDN included. Across perplexity at 4K/32K, MMLU-Pro, GSM8K, AIME'25, GPQA-Diamond, LiveCodeBench, and RULER retrieval to 64K, Minima matches BF16 within seed noise (5-task average -0.52) while being the smallest (17.5 GiB) and fastest-prefill (+14-19%) recipe we compare, and its 32K perplexity gap shrink

AI 解读论文

探索GDN模型在4位量化下的表现与优势

核心方法
通过构建Minima模型,实现NVFP4 W4A4对所有496个线性层(包括GDN)的4位量化,测试递归状态的误差累积问题
适合谁读
研究者 / 工程师
要解决的问题
早期4位量化方法在Gated DeltaNet (GDN) 的递归部分积累了误差,导致模型性能下降
关键实验
在多个任务(4K/32K perplexity, MMLU-Pro, GSM8K, AIME'25, GPQA-Diamond, LiveCodeBench, RULER 64K检索)上进行了实验,Minima的性能与BF16相近,且在32K perplexity上差距缩小
主要贡献
证明了GDN可以在4位量化下保持高性能,缩小了32K perplexity的差距,并且模型更小、预填充更快
意义与局限
该研究证明了4位量化在大规模语言模型中的可行性和优势,为模型的高效部署和推理提供了新的思路,但仍然需要进一步验证其在更多任务和数据上的表现
领域:cs.AI作者:Sergii Kozyrev、Davyd Maiboroda
相关推荐

本站内容由 LLM 精选聚合,原文版权归 arXiv 所有 · 摘录仅供参考