Structure-Aware Multi-Stage Adaptation of Qwen-72B for Code Generation

Large language models show strong abilities in code generation. They still face problems in semantic understanding, structural consistency, controllability, and generalization. Existing methods often focus on one aspect, like instruction following or structural accuracy. They do not offer a unified framework that balances correctness, readability, and adaptability. This work presents CodeFusion-Qwen72B, a multi-stage structure-aware tuning framework for Qwen-72B. It includes progressive low-rank adaptation, hybrid instruction optimization, multi-context fusion, structure-preserving hybrid loss, controllable generation decoding, and adaptive prompt retrieval. By improving semantic comprehension, structural alignment, and controlled code generation, the framework increases the robustness and versatility of large-scale models for software engineering tasks. This study shows that multi-stage optimization and structure-aware learning are effective ways to advance code generation with ultra-large models.