#Quantization

2026

• 05-15 EN

  Zero Sum SVD: A Global, Loss-Aware Rank Budget for LLM Compression

• 05-15 中

  Zero Sum SVD：用「损失零和」做全局奇异值预算分配的 LLM 压缩方法

• 04-24 EN

  Generalization at the Edge of Stability: A Random Dynamical Systems Perspective

• 04-08 EN

  SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models — In-Depth Technical Review

• 04-08 中

  SmoothQuant：大型语言模型的精准高效训练后量化 — 深度阅读笔记

• 04-03 EN

  AWQ: Activation-aware Weight Quantization for On-Device LLM Compression and Acceleration — In-Depth Technical Review

• 04-03 中

  AWQ：感知激活值的大模型权重量化压缩与加速 — 深度阅读笔记

• 03-25 EN

  GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers — In-Depth Technical Review

• 03-21 EN

  BitNet: Scaling 1-bit Transformers for Large Language Models — In-Depth Technical Review