#Model Compression

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

• 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