Cover Image

LittleBit: Ultra Low-Bit Quantization via Latent Factorization

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Lee, Banseok, Kim, Dongkyu, You, Youngcheon, Kim, Youngmin
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Artificial Intelligence Computation and Language
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

LittleBit-2: Maximizing the Spectral Energy Gain in Sub-1-Bit LLMs via Latent Geometry Alignment
by: Lee, Banseok, et al.
Published: (2026)

RaBiT: Residual-Aware Binarization Training for Accurate and Efficient LLMs
by: You, Youngcheon, et al.
Published: (2026)

More Than Bits: Multi-Envelope Double Binary Factorization for Extreme Quantization
by: Ichikawa, Yuma, et al.
Published: (2025)

MoBiQuant: Mixture-of-Bits Quantization for Token-Adaptive Any-Precision LLM
by: Wang, Dongwei, et al.
Published: (2026)

Outlier-Safe Pre-Training for Robust 4-Bit Quantization of Large Language Models
by: Park, Jungwoo, et al.
Published: (2025)

I-LLM: Efficient Integer-Only Inference for Fully-Quantized Low-Bit Large Language Models
by: Hu, Xing, et al.
Published: (2024)

What Makes Low-Bit Quantization-Aware Training Work for Reasoning LLMs? A Systematic Study
by: Lv, Keyu, et al.
Published: (2026)

QJL: 1-Bit Quantized JL Transform for KV Cache Quantization with Zero Overhead
by: Zandieh, Amir, et al.
Published: (2024)

LogQuant: Log-Distributed 2-Bit Quantization of KV Cache with Superior Accuracy Preservation
by: Chen, Han, et al.
Published: (2025)

RotateKV: Accurate and Robust 2-Bit KV Cache Quantization for LLMs via Outlier-Aware Adaptive Rotations
by: Su, Zunhai, et al.
Published: (2025)

To be Continuous, or to be Discrete, Those are Bits of Questions
by: Wang, Yiran, et al.
Published: (2024)

Unleashing Low-Bit Inference on Ascend NPUs: A Comprehensive Evaluation of HiFloat Formats
by: Zhao, Pengxiang, et al.
Published: (2026)

Assigning Distinct Roles to Quantized and Low-Rank Matrices Toward Optimal Weight Decomposition
by: Cho, Yoonjun, et al.
Published: (2025)

SpecQuant: Spectral Decomposition and Adaptive Truncation for Ultra-Low-Bit LLMs Quantization
by: Zhao, Zhixiong, et al.
Published: (2025)

Exploring Model Invariance with Discrete Search for Ultra-Low-Bit Quantization
by: Wen, Yuqiao, et al.
Published: (2025)

Q-Palette: Fractional-Bit Quantizers Toward Optimal Bit Allocation for Efficient LLM Deployment
by: Lee, Deokjae, et al.
Published: (2025)

MimiQ: Low-Bit Data-Free Quantization of Vision Transformers with Encouraging Inter-Head Attention Similarity
by: Choi, Kanghyun, et al.
Published: (2024)

An Extra RMSNorm is All You Need for Fine Tuning to 1.58 Bits
by: Steinmetz, Cody, et al.
Published: (2025)

QUICK: Quantization-aware Interleaving and Conflict-free Kernel for efficient LLM inference
by: Kim, Taesu, et al.
Published: (2024)

Unlocking the Theory Behind Scaling 1-Bit Neural Networks
by: Daliri, Majid, et al.
Published: (2024)

FrameQuant: Flexible Low-Bit Quantization for Transformers
by: Adepu, Harshavardhan, et al.
Published: (2024)

LUQ: Layerwise Ultra-Low Bit Quantization for Multimodal Large Language Models
by: Bhatnagar, Shubhang, et al.
Published: (2025)

Quantization Meets Reasoning: Exploring LLM Low-Bit Quantization Degradation for Mathematical Reasoning
by: Li, Zhen, et al.
Published: (2025)

Layer-Wise Quantization: A Pragmatic and Effective Method for Quantizing LLMs Beyond Integer Bit-Levels
by: Dumitru, Razvan-Gabriel, et al.
Published: (2024)

Learning Grouped Lattice Vector Quantizers for Low-Bit LLM Compression
by: Zhang, Xi, et al.
Published: (2025)

Why Do Some Inputs Break Low-Bit LLM Quantization?
by: Chang, Ting-Yun, et al.
Published: (2025)

SplitQuant: Layer Splitting for Low-Bit Neural Network Quantization
by: Song, Jaewoo, et al.
Published: (2025)

InfoQuant: Shaping Activation Distributions for Low-Bit LLM Quantization
by: Li, Ke, et al.
Published: (2026)

MARR: Module-Adaptive Residual Reconstruction for Low-Bit Post-Training Quantization
by: Su, Le, et al.
Published: (2026)

Quantization Variation: A New Perspective on Training Transformers with Low-Bit Precision
by: Huang, Xijie, et al.
Published: (2023)

NanoQuant: Efficient Sub-1-Bit Quantization of Large Language Models
by: Chong, Hyochan, et al.
Published: (2026)

ECQ$^{\text{x}}$: Explainability-Driven Quantization for Low-Bit and Sparse DNNs
by: Becking, Daniel, et al.
Published: (2021)

When Bits Break Recourse: Counterfactual-Faithful Quantization
by: Yahyati, Chaymae, et al.
Published: (2026)

BitsMoE: Efficient Spectral Energy-Guided Bit Allocation for MoE LLM Quantization
by: Zhao, Jiayu, et al.
Published: (2026)

Enhancing Effectiveness and Robustness in a Low-Resource Regime via Decision-Boundary-aware Data Augmentation
by: Jin, Kyohoon, et al.
Published: (2024)

UltraSketchLLM: Saliency-Driven Sketching for Ultra-Low Bit LLM Compression
by: Zou, Sunan, et al.
Published: (2025)

HeRo-Q: A General Framework for Stable Low Bit Quantization via Hessian Conditioning
by: Zhang, Jinhao Zhang Yunquan, et al.
Published: (2026)

SplitQuantV2: Enhancing Low-Bit Quantization of LLMs Without GPUs
by: Song, Jaewoo, et al.
Published: (2025)

Quantization Meets Reasoning: Exploring and Mitigating Degradation of Low-Bit LLMs in Mathematical Reasoning
by: Li, Zhen, et al.
Published: (2025)

Low-Bit Quantization Favors Undertrained LLMs: Scaling Laws for Quantized LLMs with 100T Training Tokens
by: Ouyang, Xu, et al.
Published: (2024)