A Comprehensive Evaluation on Quantization Techniques for Large Language Models
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
The Flow has not summarised this story yet — read it at arXiv Machine Learning.
arXiv:2606. 04620v1 Announce Type: cross Abstract: LLMs have become the state-of-the-art algorithms for solving NLP tasks.
arXiv:2606. 07819v1 Announce Type: new Abstract: Recently, the efficiency of Large Language Models (LLMs) deployment has become a critical concern in practical applications.
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.
InfoQuant is a train‑free method that reshapes activation distributions to be more compatible with low‑bit quantizers. It uses Peak Suppression Orthogonal Transformation (PSOT) and adaptive outlier‑token selection to reduce numerical range while maintaining dispersion, thereby lowering quantization error. Across several large language model families, InfoQuant outperforms existing post‑training quantization and end‑to‑end training baselines, preserving 97% of floating‑point accuracy on average and narrowing the LLaMA‑2 13B performance gap by 42%.
arXiv:2606. 15652v1 Announce Type: new Abstract: 4-bit quantization significantly reduces the memory footprint and accelerates the inference of large language models (LLMs).
arXiv:2510. 26690v3 Announce Type: replace Abstract: Low-Rank Adaptation (LoRA) has become a popular technique for parameter-efficient fine-tuning of large language models (LLMs).