Cover Image

Foundations of Large Language Model Compression -- Part 1: Weight Quantization

Fuente: arXiv

Saved in:

Bibliographic Details
Main Author:	Young, Sean I.
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Computation and Language
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Radio: Rate-Distortion Optimization for Large Language Model Compression
by: Young, Sean I.
Published: (2025)

On the Compressibility of Quantized Large Language Models
by: Mao, Yu, et al.
Published: (2024)

Extreme Compression of Large Language Models via Additive Quantization
by: Egiazarian, Vage, et al.
Published: (2024)

GWQ: Gradient-Aware Weight Quantization for Large Language Models
by: Shao, Yihua, et al.
Published: (2024)

LLMC: Benchmarking Large Language Model Quantization with a Versatile Compression Toolkit
by: Gong, Ruihao, et al.
Published: (2024)

PolarQuant: Optimal Gaussian Weight Quantization via Hadamard Rotation for LLM Compression
by: Vicentino, Caio
Published: (2026)

Quantization of Large Language Models with an Overdetermined Basis
by: Merkulov, Daniil, et al.
Published: (2024)

WKVQuant: Quantizing Weight and Key/Value Cache for Large Language Models Gains More
by: Yue, Yuxuan, et al.
Published: (2024)

AMQ: Enabling AutoML for Mixed-precision Weight-Only Quantization of Large Language Models
by: Lee, Sangjun, et al.
Published: (2025)

CBQ: Cross-Block Quantization for Large Language Models
by: Ding, Xin, et al.
Published: (2023)

FBQuant: FeedBack Quantization for Large Language Models
by: Liu, Yijiang, et al.
Published: (2025)

How Quantization Shapes Bias in Large Language Models
by: Marcuzzi, Federico, et al.
Published: (2025)

Scaling Laws for Post Training Quantized Large Language Models
by: Xu, Zifei, et al.
Published: (2024)

BAQ: Efficient Bit Allocation Quantization for Large Language Models
by: Zhang, Chao, et al.
Published: (2025)

OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models
by: Shao, Wenqi, et al.
Published: (2023)

ApiQ: Finetuning of 2-Bit Quantized Large Language Model
by: Liao, Baohao, et al.
Published: (2024)

LCQ: Low-Rank Codebook based Quantization for Large Language Models
by: Cai, Wen-Pu, et al.
Published: (2024)

RSAVQ: Riemannian Sensitivity-Aware Vector Quantization for Large Language Models
by: Xu, Zukang, et al.
Published: (2025)

QuIP: 2-Bit Quantization of Large Language Models With Guarantees
by: Chee, Jerry, et al.
Published: (2023)

Optimizing Large Language Model Training Using FP4 Quantization
by: Wang, Ruizhe, et al.
Published: (2025)

Foundations of Large Language Models
by: Xiao, Tong, et al.
Published: (2025)

Fast and Effective Weight Update for Pruned Large Language Models
by: Boža, Vladimír
Published: (2024)

SKVQ: Sliding-window Key and Value Cache Quantization for Large Language Models
by: Duanmu, Haojie, et al.
Published: (2024)

PrefixQuant: Eliminating Outliers by Prefixed Tokens for Large Language Models Quantization
by: Chen, Mengzhao, et al.
Published: (2024)

Compression Scaling Laws:Unifying Sparsity and Quantization
by: Frantar, Elias, et al.
Published: (2025)

SliceGPT: Compress Large Language Models by Deleting Rows and Columns
by: Ashkboos, Saleh, et al.
Published: (2024)

FoldGPT: Simple and Effective Large Language Model Compression Scheme
by: Liu, Songwei, et al.
Published: (2024)

Iterative Layer-wise Distillation for Efficient Compression of Large Language Models
by: Kovalev, Grigory, et al.
Published: (2025)

DoTA: Weight-Decomposed Tensor Adaptation for Large Language Models
by: Hu, Xiaolin, et al.
Published: (2024)

SliM-LLM: Salience-Driven Mixed-Precision Quantization for Large Language Models
by: Huang, Wei, et al.
Published: (2024)

Prefixing Attention Sinks can Mitigate Activation Outliers for Large Language Model Quantization
by: Son, Seungwoo, et al.
Published: (2024)

ResQ: Mixed-Precision Quantization of Large Language Models with Low-Rank Residuals
by: Saxena, Utkarsh, et al.
Published: (2024)

Towards Linguistically-Aware and Language-Independent Tokenization for Large Language Models (LLMs)
by: Rahman, Abrar, et al.
Published: (2024)

Unveiling the Mystery of Weight in Large Foundation Models: Gaussian Distribution Never Fades
by: Si, Chongjie, et al.
Published: (2025)

Basis Sharing: Cross-Layer Parameter Sharing for Large Language Model Compression
by: Wang, Jingcun, et al.
Published: (2024)

PocketLLM: Ultimate Compression of Large Language Models via Meta Networks
by: Tian, Ye, et al.
Published: (2025)

MoreauPruner: Robust Pruning of Large Language Models against Weight Perturbations
by: Wang, Zixiao, et al.
Published: (2024)

Merging Methods for Multilingual Knowledge Editing for Large Language Models: An Empirical Odyssey
by: Lee, Kunil, et al.
Published: (2026)

CPTQuant - A Novel Mixed Precision Post-Training Quantization Techniques for Large Language Models
by: Nanda, Amitash, et al.
Published: (2024)

L4Q: Parameter Efficient Quantization-Aware Fine-Tuning on Large Language Models
by: Jeon, Hyesung, et al.
Published: (2024)