#Model Compression

2026

• 06-26 EN

  SigmaScale: Learning to Scale Weight Matrices for Better SVD-Based LLM Compression

• 06-26 中

  SigmaScale 阅读笔记：通过学习缩放矩阵改进 SVD 大语言模型压缩

• 06-19 EN

  LASER: How Throwing Away 99% of a Weight Matrix Can Make LLMs Smarter

• 06-19 中

  LASER：丢掉 99% 的矩阵秩，LLM 推理准确率反而提高了 27%

• 06-12 EN

  SliceGPT: Post-Training LLM Compression via Computational Invariance

• 06-12 中

  SliceGPT 阅读笔记：用计算不变性删除 Transformer 的行与列

• 05-29 EN

  IO-SVD: Input-Output Whitened SVD for Adaptive-Rank LLM Compression

• 05-29 中

  IO-SVD：基于输入输出双侧白化的自适应秩LLM压缩方法

• 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

• 05-08 EN

  Swift-SVD: Activation-Aware Low-Rank Compression for LLM Weights and KV Cache

• 04-17 EN

  GRASP Technical Review: Replacing Redundant LLM Layers with Adaptive Singular Parameters

• 04-10 EN

  SVD-LLM: Truncation-aware Singular Value Decomposition for Large Language Model Compression — Deep Technical Review

• 04-10 中

  SVD-LLM：面向大语言模型压缩的“截断感知”奇异值分解方法 — 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

• 04-01 EN

  Layer Pruning for Efficient Large Language Models — In-Depth Technical Review

• 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