APTQ: Attention-aware Post-Training Mixed-Precision Quantization for Large Language Models
Fuente:
arXiv
Saved in:
| Main Authors: | Guan, Ziyi, Huang, Hantao, Su, Yupeng, Huang, Hong, Wong, Ngai, Yu, Hao |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
LLM-Barber: Block-Aware Rebuilder for Sparsity Mask in One-Shot for Large Language Models
by: Su, Yupeng, et al.
Published: (2024)
by: Su, Yupeng, et al.
Published: (2024)
PTQTP: Post-Training Quantization to Trit-Planes for Large Language Models
by: Xiao, He, et al.
Published: (2025)
by: Xiao, He, et al.
Published: (2025)
Channel-Wise Mixed-Precision Quantization for Large Language Models
by: Chen, Zihan, et al.
Published: (2024)
by: Chen, Zihan, et al.
Published: (2024)
Quantization Meets Reasoning: Exploring LLM Low-Bit Quantization Degradation for Mathematical Reasoning
by: Li, Zhen, et al.
Published: (2025)
by: Li, Zhen, et al.
Published: (2025)
SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models
by: Xiao, Guangxuan, et al.
Published: (2022)
by: Xiao, Guangxuan, et al.
Published: (2022)
Benchmarking Post-Training Quantization of Large Language Models under Microscaling Floating Point Formats
by: Zhang, Manyi, et al.
Published: (2026)
by: Zhang, Manyi, et al.
Published: (2026)
Interactions Across Blocks in Post-Training Quantization of Large Language Models
by: Shabanovi, Khasmamad, et al.
Published: (2024)
by: Shabanovi, Khasmamad, et al.
Published: (2024)
LoRETTA: Low-Rank Economic Tensor-Train Adaptation for Ultra-Low-Parameter Fine-Tuning of Large Language Models
by: Yang, Yifan, et al.
Published: (2024)
by: Yang, Yifan, et al.
Published: (2024)
Task-Stratified Knowledge Scaling Laws for Post-Training Quantized Large Language Models
by: Zhou, Chenxi, et al.
Published: (2025)
by: Zhou, Chenxi, et al.
Published: (2025)
On the Compressibility of Quantized Large Language Models
by: Mao, Yu, et al.
Published: (2024)
by: Mao, Yu, et al.
Published: (2024)
Rethinking Kullback-Leibler Divergence in Knowledge Distillation for Large Language Models
by: Wu, Taiqiang, et al.
Published: (2024)
by: Wu, Taiqiang, et al.
Published: (2024)
Exploring Layer-wise Information Effectiveness for Post-Training Quantization in Small Language Models
by: Xiao, He, et al.
Published: (2025)
by: Xiao, He, et al.
Published: (2025)
Conversational Disease Diagnosis via External Planner-Controlled Large Language Models
by: Sun, Zhoujian, et al.
Published: (2024)
by: Sun, Zhoujian, et al.
Published: (2024)
Self-Attention Limits Working Memory Capacity of Transformer-Based Models
by: Gong, Dongyu, et al.
Published: (2024)
by: Gong, Dongyu, et al.
Published: (2024)
Evaluating Quantized Large Language Models
by: Li, Shiyao, et al.
Published: (2024)
by: Li, Shiyao, et al.
Published: (2024)
A Comprehensive Evaluation of Quantization Strategies for Large Language Models
by: Jin, Renren, et al.
Published: (2024)
by: Jin, Renren, et al.
Published: (2024)
Param$Δ$ for Direct Weight Mixing: Post-Train Large Language Model at Zero Cost
by: Cao, Sheng, et al.
Published: (2025)
by: Cao, Sheng, et al.
Published: (2025)
Evolving Subnetwork Training for Large Language Models
by: Li, Hanqi, et al.
Published: (2024)
by: Li, Hanqi, et al.
Published: (2024)
A Two-Stage Proactive Dialogue Generator for Efficient Clinical Information Collection Using Large Language Model
by: Li, Xueshen, et al.
Published: (2024)
by: Li, Xueshen, et al.
Published: (2024)
Aggressive Post-Training Compression on Extremely Large Language Models
by: Zhang, Zining, et al.
Published: (2024)
by: Zhang, Zining, et al.
Published: (2024)
A Comprehensive Study on Quantization Techniques for Large Language Models
by: Lang, Jiedong, et al.
Published: (2024)
by: Lang, Jiedong, et al.
Published: (2024)
BiLLM: Pushing the Limit of Post-Training Quantization for LLMs
by: Huang, Wei, et al.
Published: (2024)
by: Huang, Wei, et al.
Published: (2024)
SASQ: Static Activation Scaling for Quantization-Aware Training in Large Language Models
by: Mao, Shizhuo, et al.
Published: (2025)
by: Mao, Shizhuo, et al.
Published: (2025)
On the Effectiveness of Incremental Training of Large Language Models
by: Li, Miles Q., et al.
Published: (2024)
by: Li, Miles Q., et al.
Published: (2024)
Timber: Training-free Instruct Model Refining with Base via Effective Rank
by: Wu, Taiqiang, et al.
Published: (2025)
by: Wu, Taiqiang, et al.
Published: (2025)
Typhoon-S: Minimal Open Post-Training for Sovereign Large Language Models
by: Pipatanakul, Kunat, et al.
Published: (2026)
by: Pipatanakul, Kunat, et al.
Published: (2026)
Efficient Post-Training Refinement of Latent Reasoning in Large Language Models
by: Wang, Xinyuan, et al.
Published: (2025)
by: Wang, Xinyuan, et al.
Published: (2025)
Sparsity Induction for Accurate Post-Training Pruning of Large Language Models
by: Jiang, Minhao, et al.
Published: (2026)
by: Jiang, Minhao, et al.
Published: (2026)
Scatter-Based Innovation Propagation in Large Language Models for Multi-Stage Process Adaptation
by: Su, Hong
Published: (2025)
by: Su, Hong
Published: (2025)
DIDS: Domain Impact-aware Data Sampling for Large Language Model Training
by: Shi, Weijie, et al.
Published: (2025)
by: Shi, Weijie, et al.
Published: (2025)
Medical MLLM is Vulnerable: Cross-Modality Jailbreak and Mismatched Attacks on Medical Multimodal Large Language Models
by: Huang, Xijie, et al.
Published: (2024)
by: Huang, Xijie, et al.
Published: (2024)
VLMQ: Token Saliency-Driven Post-Training Quantization for Vision-language Models
by: Xue, Yufei, et al.
Published: (2025)
by: Xue, Yufei, et al.
Published: (2025)
Post-Completion Learning for Language Models
by: Fei, Xiang, et al.
Published: (2025)
by: Fei, Xiang, et al.
Published: (2025)
ELPO: Ensemble Learning Based Prompt Optimization for Large Language Models
by: Zhang, Qing, et al.
Published: (2025)
by: Zhang, Qing, et al.
Published: (2025)
Shadow-FT: Tuning Instruct Model via Training on Paired Base Model
by: Wu, Taiqiang, et al.
Published: (2025)
by: Wu, Taiqiang, et al.
Published: (2025)
Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities
by: Sun, Hao, et al.
Published: (2025)
by: Sun, Hao, et al.
Published: (2025)
Athena: Efficient Block-Wise Post-Training Quantization for Large Language Models Using Second-Order Matrix Derivative Information
by: Wang, Yanshu, et al.
Published: (2024)
by: Wang, Yanshu, et al.
Published: (2024)
Quantized Large Language Models in Biomedical Natural Language Processing: Evaluation and Recommendation
by: Zhan, Zaifu, et al.
Published: (2025)
by: Zhan, Zaifu, et al.
Published: (2025)
Human-Inspired Learning for Large Language Models via Obvious Record and Maximum-Entropy Method Discovery
by: Su, Hong
Published: (2025)
by: Su, Hong
Published: (2025)
EfficientQAT: Efficient Quantization-Aware Training for Large Language Models
by: Chen, Mengzhao, et al.
Published: (2024)
by: Chen, Mengzhao, et al.
Published: (2024)
Similar Items
-
LLM-Barber: Block-Aware Rebuilder for Sparsity Mask in One-Shot for Large Language Models
by: Su, Yupeng, et al.
Published: (2024) -
PTQTP: Post-Training Quantization to Trit-Planes for Large Language Models
by: Xiao, He, et al.
Published: (2025) -
Channel-Wise Mixed-Precision Quantization for Large Language Models
by: Chen, Zihan, et al.
Published: (2024) -
Quantization Meets Reasoning: Exploring LLM Low-Bit Quantization Degradation for Mathematical Reasoning
by: Li, Zhen, et al.
Published: (2025) -
SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models
by: Xiao, Guangxuan, et al.
Published: (2022)