Cover Image

QEFT: Quantization for Efficient Fine-Tuning of LLMs

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Lee, Changhun, Jin, Jun-gyu, Cho, Younghyun, Park, Eunhyeok
Format:	Preprint
Published:	2024
Subjects:	Computation and Language Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

SEAL: Scaling to Emphasize Attention for Long-Context Retrieval
by: Lee, Changhun, et al.
Published: (2025)

PTQ4VM: Post-Training Quantization for Visual Mamba
by: Cho, Younghyun, et al.
Published: (2024)

OWQ: Outlier-Aware Weight Quantization for Efficient Fine-Tuning and Inference of Large Language Models
by: Lee, Changhun, et al.
Published: (2023)

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

HLQ: Fast and Efficient Backpropagation via Hadamard Low-rank Quantization
by: Kim, Seonggon, et al.
Published: (2024)

Memory-Efficient Structured Backpropagation for On-Device LLM Fine-Tuning
by: Park, Juneyoung, et al.
Published: (2026)

PruneCD: Contrasting Pruned Self Model to Improve Decoding Factuality
by: Yu, Byeongho, et al.
Published: (2025)

LCSB: Layer-Cyclic Selective Backpropagation for Memory-Efficient On-Device LLM Fine-Tuning
by: Park, Juneyoung, et al.
Published: (2026)

PocketLLM: Enabling On-Device Fine-Tuning for Personalized LLMs
by: Peng, Dan, et al.
Published: (2024)

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

Memory-Efficient Fine-Tuning of Transformers via Token Selection
by: Simoulin, Antoine, et al.
Published: (2025)

Direct Alignment of Draft Model for Speculative Decoding with Chat-Fine-Tuned LLMs
by: Goel, Raghavv, et al.
Published: (2024)

Parameter-Efficient Fine-Tuning of State Space Models
by: Galim, Kevin, et al.
Published: (2024)

ClusterUCB: Efficient Gradient-Based Data Selection for Targeted Fine-Tuning of LLMs
by: Wang, Zige, et al.
Published: (2025)

QFT: Quantized Full-parameter Tuning of LLMs with Affordable Resources
by: Li, Zhikai, et al.
Published: (2023)

An Efficient Sparse Fine-Tuning with Low Quantization Error via Neural Network Pruning
by: Li, Cen-Jhih, et al.
Published: (2025)

GSQ-Tuning: Group-Shared Exponents Integer in Fully Quantized Training for LLMs On-Device Fine-tuning
by: Zhou, Sifan, et al.
Published: (2025)

Hallucination Detection in LLMs: Fast and Memory-Efficient Fine-Tuned Models
by: Arteaga, Gabriel Y., et al.
Published: (2024)

Towards Robust and Parameter-Efficient Knowledge Unlearning for LLMs
by: Cha, Sungmin, et al.
Published: (2024)

Efficient Differentially Private Fine-Tuning of LLMs via Reinforcement Learning
by: Khadangi, Afshin, et al.
Published: (2025)

Parameter-Efficient Fine-Tuning of LLMs with Mixture of Space Experts
by: Zhang, Buze, et al.
Published: (2026)

Memento: Fine-tuning LLM Agents without Fine-tuning LLMs
by: Zhou, Huichi, et al.
Published: (2025)

Comparative Analysis of Different Efficient Fine Tuning Methods of Large Language Models (LLMs) in Low-Resource Setting
by: Srinivasan, Krishna Prasad Varadarajan, et al.
Published: (2024)

Prior-Informed Zeroth-Order Optimization with Adaptive Direction Alignment for Memory-Efficient LLM Fine-Tuning
by: Jin, Feihu, et al.
Published: (2026)

Parameter-Efficient Fine-Tuning via Circular Convolution
by: Chen, Aochuan, et al.
Published: (2024)

Simplify-This: A Comparative Analysis of Prompt-Based and Fine-Tuned LLMs
by: Cohen, Eilam, et al.
Published: (2026)

SAFT: Structure-Aware Fine-Tuning of LLMs for AMR-to-Text Generation
by: Kamel, Rafiq, et al.
Published: (2025)

Deconfounded Causality-aware Parameter-Efficient Fine-Tuning for Problem-Solving Improvement of LLMs
by: Wang, Ruoyu, et al.
Published: (2024)

Position-Aware Parameter Efficient Fine-Tuning Approach for Reducing Positional Bias in LLMs
by: Zhang, Zheng, et al.
Published: (2024)

Long Exposure: Accelerating Parameter-Efficient Fine-Tuning for LLMs under Shadowy Sparsity
by: Wang, Tuowei, et al.
Published: (2025)

Unlocking Efficient, Scalable, and Continual Knowledge Editing with Basis-Level Representation Fine-Tuning
by: Liu, Tianci, et al.
Published: (2025)

Token Buncher: Shielding LLMs from Harmful Reinforcement Learning Fine-Tuning
by: Feng, Weitao, et al.
Published: (2025)

Parameter-Efficient Fine-Tuning of LLaMA for the Clinical Domain
by: Gema, Aryo Pradipta, et al.
Published: (2023)

FTFT: Efficient and Robust Fine-Tuning by Transferring Training Dynamics
by: Du, Yupei, et al.
Published: (2023)

DropBP: Accelerating Fine-Tuning of Large Language Models by Dropping Backward Propagation
by: Woo, Sunghyeon, et al.
Published: (2024)

Unintended Misalignment from Agentic Fine-Tuning: Risks and Mitigation
by: Hahm, Dongyoon, et al.
Published: (2025)

Revisiting Zeroth-Order Optimization for Memory-Efficient LLM Fine-Tuning: A Benchmark
by: Zhang, Yihua, et al.
Published: (2024)

Fine-Tuning or Retrieval? Comparing Knowledge Injection in LLMs
by: Ovadia, Oded, et al.
Published: (2023)

Teaching LLMs How to Learn with Contextual Fine-Tuning
by: Choi, Younwoo, et al.
Published: (2025)

Ranking Entities along Conceptual Space Dimensions with LLMs: An Analysis of Fine-Tuning Strategies
by: Kumar, Nitesh, et al.
Published: (2024)