@inproceedings{hu-etal-2025-mlwq,
    title = "{MLWQ}: Efficient Small Language Model Deployment via Multi-Level Weight Quantization",
    author = "Hu, Chun  and
      He, Junhui  and
      Wu, Shangyu  and
      He, Yuxin  and
      Xue, Chun Jason  and
      Li, Qingan",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.408/",
    pages = "8078--8088",
    ISBN = "979-8-89176-332-6",
    abstract = "Small language models (SLMs) are gaining attention for their lower computational and memory needs while maintaining strong performance. However, efficiently deploying SLMs on resource-constrained devices remains a significant challenge. Post-training quantization(PTQ) is a widely used compression technique that reduces memory usage and inference computation, yet existing methods face challenges in inefficient bit-width allocation and insufficient fine-grained quantization adjustments, leading to suboptimal performance, particularly at lower bit-widths. To address these challenges, we propose multi-level weight quantization (MLWQ), which facilitates the efficient deployment of SLMs. Our method enables more effective bit-width allocation by jointly considering inter-layer loss and intra-layer salience. Furthermore, we propose a fine-grained partitioning of intra-layer salience to support the tweaking of quantization parameters within each group. Experimental results indicate that MLWQ achieves competitive performance compared to state-of-the-art methods, providing an effective approach for the efficient deployment of SLMs while maintaining model accuracy."
}