The increasing scale and complexity of large language models (LLMs) present significant computational and memory challenges, limiting their widespread deployment. Post-training quantization (PTQ) has emerged as a key technique for mitigating these challenges without costly retraining. However, compressing models to ultra-low bitwidths (e.g., 2-3 bits) while maintaining accuracy remains a major challenge. In this study, we present a comprehensive PTQ framework that addresses this problem by compressing LLM weights through three core innovations: (1) a calibration process guided by Kullback-Leibler divergence minimization to preserve the original weight distribution, (2) a learnable codebook optimization mechanism employing noise substitution for vector quantization to enable robust gradient estimation, and (3) a layer-grouping strategy based on statistical distribution similarity to improve parameter efficiency. Experimental evaluations on large-scale models show that the proposed framework achieves competitive performance compared with state-of-the-art quantization techniques. Importantly, these results are obtained without any post-quantization fine-tuning, highlighting the efficiency and practical applicability of our approach for deploying highly compressed LLMs.