量化 · 论文
共 10 个清单条目；已读 0，待读 10。
Quantization & Compression
| Status | Paper | Venue | Why It Matters | Links |
|---|---|---|---|---|
| ⬜ | LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale | NeurIPS'22 | 混合精度分解 activation outlier，是理解 LLM INT8 的起点 | Paper |
| ⬜ | GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers | ICLR'23 | 基于近似二阶信息的 one-shot weight-only PTQ | Paper |
| ⬜ | SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models | ICML'23 | 用等价缩放把 activation 量化难度迁移到 weight，形成 W8A8 路线 | Paper |
| ⬜ | AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration | MLSys'24 | 用 activation 识别显著通道并保护关键权重，形成主流 W4A16 路线 | Paper |
| ⬜ | SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression | ICLR'24 | 将少量 outlier 与低比特权重分离，理解稀疏异常值处理 | Paper |
| ⬜ | OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models | ICLR'24 | 通过可学习的 clipping 与等价变换改善低比特 PTQ | Paper |
| ⬜ | AQLM: Extreme Compression of Large Language Models via Additive Quantization | ICML'24 | additive codebook 与极低 bit/weight，连接压缩算法和解码 kernel | Paper |
| ⬜ | KIVI: A Tuning-Free Asymmetric 2bit Quantization for KV Cache | ICML'24 | 区分 K/V 的通道与 token 统计特性，理解 KV cache 低比特化 | Paper |
| ⬜ | QuaRot: Outlier-Free 4-Bit Inference in Rotated LLMs | NeurIPS'24 | 用 Hadamard rotation 消除 outlier，适合从算法追到融合 kernel | Paper |
| ⬜ | The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits | arXiv'24 | BitNet b1.58 与三值权重，理解量化感知训练的架构路线 | Paper |