Hugging Face Trending Papers June 11, 2026

TWLA: Achieving Ternary Weights and Low-Bit Activations for LLMs via Post-Training Quantization

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Large language models (LLMs) exhibit exceptional general language processing capabilities, but their memory and compute costs hinder deployment. Ternarization has emerged as a promising compression technique, offering significant reductions in model size and inference complexity.

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arXiv AI

Jun 12

TWLA: Achieving Ternary Weights and Low-Bit Activations for LLMs via Post-Training Quantization

arXiv:2606. 13054v1 Announce Type: cross Abstract: Large language models (LLMs) exhibit exceptional general language processing capabilities, but their memory and compute costs hinder deployment.

By Zhixiong Zhao, Zukang Xu, Zhixuan Chen, Xing Hu, Zhe Jiang, Dawei Yang

llms efficiency

arXiv AI

Jun 9

Joint Structural Pruning and Mixed-Precision Quantization for LLM Compression

arXiv:2606. 07819v1 Announce Type: new Abstract: Recently, the efficiency of Large Language Models (LLMs) deployment has become a critical concern in practical applications.

By Hoang-Loc La, Truong-Thanh Le, Amir Taherkordi, Phuong Hoai Ha

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arXiv Machine Learning

Jul 10

KronQ: LLM Quantization via Kronecker-Factored Hessian

arXiv:2607. 07964v1 Announce Type: new Abstract: Post-training quantization (PTQ) is a widely adopted technique for compressing large language models (LLMs) without retraining.

By Donghyun Lee, Yuhang Li, Ruokai Yin, Priyadarshini Panda

llms efficiency

arXiv Machine Learning

Jun 8

AAAC: Activation-Aware Adaptive Codebooks for 4-bit LLM Weight Quantization

arXiv:2605. 08692v2 Announce Type: replace Abstract: Post-training weight-only quantization to 4 bits is widely used to reduce the memory and compute costs of large language model inference.

By Beshr IslamBouli, David Jin

llms efficiency

arXiv AI

Jun 4

QuBLAST: A Framework for Quantizing Large Language Models with Block-Level Compression Approach and Activation Scaling Strategy

arXiv:2606. 04620v1 Announce Type: cross Abstract: LLMs have become the state-of-the-art algorithms for solving NLP tasks.

By Pasindu Wickramasinghe, Achyuta Muthuvelan, Rachmad Vidya Wicaksana Putra, Minghao Shao, Muhammad Shafique

llms nlp efficiency benchmarks

arXiv AI

Jun 26

CAT-Q: Cost-efficient and Accurate Ternary Quantization for LLMs

arXiv:2606. 26650v1 Announce Type: cross Abstract: In this paper, we present CAT-Q, Cost-efficient and Accurate Ternary Quantization, for compressing and accelerating LLMs.

By Shigeng Wang, Chao Li, Yangyuxuan Kang, Jiawei Fan, Anbang Yao

llms efficiency benchmarks

Related stories

TWLA: Achieving Ternary Weights and Low-Bit Activations for LLMs via Post-Training Quantization

Joint Structural Pruning and Mixed-Precision Quantization for LLM Compression

KronQ: LLM Quantization via Kronecker-Factored Hessian

AAAC: Activation-Aware Adaptive Codebooks for 4-bit LLM Weight Quantization

QuBLAST: A Framework for Quantizing Large Language Models with Block-Level Compression Approach and Activation Scaling Strategy

CAT-Q: Cost-efficient and Accurate Ternary Quantization for LLMs