English(EN) Attention Quantization for Tabular Foundation Models

新的注意力量化加速表格基础模型

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-14 04:00

研究人员为表格基础模型开发了一种新的注意力量化策略，以提高推理性能。该方法侧重于将查询、键和值量化为 FP8，并利用显式的 FP8 矩阵乘法指令。一项关键发现是，必须在训练和测试数据之间对齐量化误差，以防止精度下降。开发的 Triton 内核在标准 16 位内核上实现了高达 1.7 倍的加速，同时在 TabPFN-v3 和 TabICLv2 等模型在基准数据集上的表现没有显著的精度损失。 AI

影响这项研究可能导致表格基础模型更高效的部署和使用，降低计算成本和延迟。

排序理由研究论文，详细介绍了一种优化模型推理的新方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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arXiv cs.LG TIER_1 English(EN) · Jonas M. K\"ubler, Benjamin J\"ager, Klemens Fl\"oge, Noah Hollmann, Frank Hutter · 2026-09-14 04:00

用于表格基础模型的注意力量化

arXiv:2609.13031v1 Announce Type: new Abstract: With the recent rise and adoption of tabular foundation models, optimizing their inference performance becomes an emerging field for efficiency research. While the models are architecturally similar to transformer-based large langua…

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用于表格基础模型的注意力量化

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