The rapid evolution of language models has significantly enhanced their ability to generate human-like text; however, challenges persist in achieving complex syntactic and contextual understanding. Introducing Dynamic Syntax Transfer (DST), a novel methodology that dynamically adjusts syntactic structures in response to contextual variations, thereby enabling large language models to produce text with improved grammatical accuracy and contextual relevance. The implementation of DST within an open-source language model architecture has demonstrated notable advancements in syntactic precision and contextual coherence, as evidenced by comprehensive quantitative and qualitative analyses. These findings demonstrate DST's potential to advance context-aware syntax modeling, offering a promising avenue for future research and applications in sophisticated language generation systems.