Small Model Gains 7.1 Points on Output Accuracy

This guide demonstrates a practical, low-cost method to improve structured-output compliance in a small language model. The dramatic improvement from 22.6% to 29.7% on IFStruct suggests that targeted fine-tuning can address a common weakness in smaller models, making them more viable for real-world tasks that require reliable formatting, such as generating JSON for downstream applications. The training recipe is notable for its efficiency: only about 500 samples and 100 steps, achievable on free hardware. This lowers the barrier for developers and small businesses to customize models for specific needs without large budgets. However, the fine-tuned model still does not match the performance of far larger models, indicating that for more complex tasks, larger models may still be necessary. The guide also highlights the importance of schema compliance as a distinct capability, often overlooked in broader benchmarks. By focusing on this specific skill, the authors show a path to improving model utility in production settings where output validity is critical.