Cover Image

RaanA: A Fast, Flexible, and Data-Efficient Post-Training Quantization Algorithm

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Yang, Yongyi, Gao, Jianyang, Hu, Wei
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

mHC-lite: You Don't Need 20 Sinkhorn-Knopp Iterations
by: Yang, Yongyi, et al.
Published: (2026)

Post-Training Quantization of OpenPangu Models for Efficient Deployment on Atlas A2
by: Luo, Yilun, et al.
Published: (2025)

Dr. Post-Training: A Data Regularization Perspective on LLM Post-Training
by: Hu, Pingbang, et al.
Published: (2026)

MoBiE: Efficient Inference of Mixture of Binary Experts under Post-Training Quantization
by: Zhao, Zhixiong, et al.
Published: (2026)

A Quantized VAE-MLP Botnet Detection Model: A Systematic Evaluation of Quantization-Aware Training and Post-Training Quantization Strategies
by: Wasswa, Hassan, et al.
Published: (2025)

CoScale-RL: Efficient Post-Training by Co-Scaling Data and Computation
by: Chen, Yutong, et al.
Published: (2026)

PTQTP: Post-Training Quantization to Trit-Planes for Large Language Models
by: Xiao, He, et al.
Published: (2025)

EasyQuant: An Efficient Data-free Quantization Algorithm for LLMs
by: Tang, Hanlin, et al.
Published: (2024)

Achieving binary weight and activation for LLMs using Post-Training Quantization
by: Song, Siqing, et al.
Published: (2025)

MagR: Weight Magnitude Reduction for Enhancing Post-Training Quantization
by: Zhang, Aozhong, et al.
Published: (2024)

Beacon: Post-Training Quantization with Integrated Grid Selection
by: Zhang, Shihao, et al.
Published: (2025)

Pushing the Limits of Block Rotations in Post-Training Quantization
by: Sanjeet, Sai, et al.
Published: (2026)

BWLA: Breaking the Barrier of W1AX Post-Training Quantization for LLMs
by: Zhao, Zhixiong, et al.
Published: (2026)

Learning-Zone Energy: Online Data Selection for Efficient RL Post-Training
by: Cui, Peng, et al.
Published: (2026)

Quamba: A Post-Training Quantization Recipe for Selective State Space Models
by: Chiang, Hung-Yueh, et al.
Published: (2024)

Revisiting RaBitQ and TurboQuant: A Symmetric Comparison of Methods, Theory, and Experiments
by: Gao, Jianyang, et al.
Published: (2026)

Activation Sensitivity as a Unifying Principle for Post-Training Quantization
by: Xu, Bruce Changlong
Published: (2026)

Post Training Quantization of Large Language Models with Microscaling Formats
by: Sharify, Sayeh, et al.
Published: (2024)

CrossQuant: A Post-Training Quantization Method with Smaller Quantization Kernel for Precise Large Language Model Compression
by: Liu, Wenyuan, et al.
Published: (2024)

Efficient Post-training Quantization with FP8 Formats
by: Shen, Haihao, et al.
Published: (2023)

SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models
by: Xiao, Guangxuan, et al.
Published: (2022)

DAQ: Density-Aware Post-Training Weight-Only Quantization For LLMs
by: Luo, Yingsong, et al.
Published: (2024)

Towards Next-Level Post-Training Quantization of Hyper-Scale Transformers
by: Kim, Junhan, et al.
Published: (2024)

DAQ: Delta-Aware Quantization for Post-Training LLM Weight Compression
by: Yu, Xiaoming, et al.
Published: (2026)

BiLLM: Pushing the Limit of Post-Training Quantization for LLMs
by: Huang, Wei, et al.
Published: (2024)

Exploring Layer-wise Information Effectiveness for Post-Training Quantization in Small Language Models
by: Xiao, He, et al.
Published: (2025)

Improving Quantization with Post-Training Model Expansion
by: Franco, Giuseppe, et al.
Published: (2025)

QaRL: Rollout-Aligned Quantization-Aware RL for Fast and Stable Training under Training--Inference Mismatch
by: Gu, Hao, et al.
Published: (2026)

LRQ: Optimizing Post-Training Quantization for Large Language Models by Learning Low-Rank Weight-Scaling Matrices
by: Lee, Jung Hyun, et al.
Published: (2024)

EfficientQAT: Efficient Quantization-Aware Training for Large Language Models
by: Chen, Mengzhao, et al.
Published: (2024)

Fast-DataShapley: Neural Modeling for Training Data Valuation
by: Sun, Haifeng, et al.
Published: (2025)

Benchmarking Post-Training Quantization in LLMs: Comprehensive Taxonomy, Unified Evaluation, and Comparative Analysis
by: Zhao, Jiaqi, et al.
Published: (2025)

FlexRound: Learnable Rounding based on Element-wise Division for Post-Training Quantization
by: Lee, Jung Hyun, et al.
Published: (2023)

D$^2$Quant: Accurate Low-bit Post-Training Weight Quantization for LLMs
by: Yan, Xianglong, et al.
Published: (2026)

Astro: Activation-guided Structured Regularization for Outlier-Robust LLM Post-Training Quantization
by: Chen, Xi, et al.
Published: (2026)

Athena: Efficient Block-Wise Post-Training Quantization for Large Language Models Using Second-Order Matrix Derivative Information
by: Wang, Yanshu, et al.
Published: (2024)

Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness
by: Rahaman, Md Mushfiqur, et al.
Published: (2025)

OptRot: Mitigating Weight Outliers via Data-Free Rotations for Post-Training Quantization
by: Gadhikar, Advait, et al.
Published: (2025)

AnyBCQ: Hardware Efficient Flexible Binary-Coded Quantization for Multi-Precision LLMs
by: Park, Gunho, et al.
Published: (2025)

Qronos: Correcting the Past by Shaping the Future... in Post-Training Quantization
by: Zhang, Shihao, et al.
Published: (2025)