Arabic function calling requires models to identify appropriate tools and reproduce arguments in the exact representation expected by evaluators. This is challenging due to dialectal variation, lexical alternatives, and surface-form normalization. We present a hybrid system based on Qwen2.5-7B-Instruct adapted with 4-bit QLoRA, followed by conservative deterministic post-processing. The neural model generates flexible tool calls, while a lightweight deterministic layer parses, validates, and casts the output to improve compliance with the exact-match evaluation format. On the official blind evaluation, our system obtains 93.24% Function Accuracy and 58.38% Argument Exact Match (Track A: 72.32%, Track B: 71.37%). Error analysis indicates that many remaining failures arise from argument-surface mismatches rather than function-selection errors, supporting a design where generative modeling handles semantics while deterministic processing controls exact realization.