Author

Runqi Wang

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#machine learning Jul 2025

Squeeze10-LLM: Squeezing LLMs' Weights by 10 Times via a Staged Mixed-Precision Quantization Method

Squeeze10-LLM is a staged mixed-precision post-training quantization (PTQ) framework and achieves state-of-the-art performance for sub-2bit weight-only quantization, improving average accuracy from 43% to 56% on six zero-shot classification tasks--a significant boost over existing PTQ methods.

Qingcheng Zhu, Yang-Yang Ren, Lin-Lin Yang et al. · arXiv.org · 1 citation

Jul 2026

P4Q: Learning to Prompt for Quantization in Low-Bit CLIP

The “Prompt for Quantization” (P4Q) is proposed, by integrating PTQ with Parameter-Efficient Fine-Tuning (PEFT) techniques, and demonstrates that P4Q significantly enhances the performance of low-bit CLIP while reducing deployment costs.

H. Sun, Runqi Wang, Yanjing Li et al. · ACM Transactions on Multimed... · 0 citations