牛哥精选 · 本月

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📝 深度技术 arXiv NLP 2026-05-20

E-PMQ: Expert-Guided Post-Merge Quantization with Merged-Weight Anchoring

提出专家引导的后合并量化方法，利用合并权重锚定，在低资源部署中平衡模型压缩与性能。

arXiv:2605.16882v1 Announce Type: new Abstract: Low-resource deployment constraints have made model quantization essential for deploying neural networ…

模型量化神经网络压缩后合并量化加权锚定低资源部署

📝 深度技术 arXiv 机器学习 2026-05-19

Rethinking Output Alignment For 1-bit Post-Training Quantization of Large Language Models

1-bit量化大模型新思路，输出对齐策略再审视，助力低资源设备高效推理

arXiv:2512.21651v3 Announce Type: replace Abstract: Large Language Models (LLMs) deliver strong performance across a wide range of NLP tasks, but thei…

llm 1-bit量化后训练量化输出对齐模型压缩

📅 日期

2026-05-20 2026-05-19

🐂 牛哥精选

E-PMQ: Expert-Guided Post-Merge Quantization with Merged-Weight Anchoring

Rethinking Output Alignment For 1-bit Post-Training Quantization of Large Language Models

📅 日期