Large language models (LLMs) are revolutionizing how we interact with technology, but fine-tuning them for specific tasks can be tricky. A new research paper explores a novel technique called Minor SFT, designed to improve performance and reduce model deviation during the fine-tuning process. Why is model deviation a problem? Imagine training an LLM on medical data. You want it to specialize, but not to the point where it forgets general language principles. This "drift" is known as deviation. Minor SFT introduces a sample-level dynamic coefficient to the training process. This coefficient acts like a personalized learning plan for each data point, giving more attention to complex examples while easing up on simpler ones. The result? The model becomes more adept at its new specialization while retaining its core language abilities. Experiments using the Qwen2-7B-Instruction model on datasets like FinanceIQ and C-Eval-Exam showed that Minor SFT outperformed traditional fine-tuning methods. It achieved higher accuracy with a lower deviation score, effectively balancing specialized knowledge with general language skills. This approach could be a game-changer for adapting LLMs to a variety of fields, from medicine and finance to customer service and creative writing. While the method requires fine-tuning the learning rate and an extra parameter, the gains in performance suggest a worthy trade-off. Further research into evaluating model fit during training will help refine Minor SFT and unlock even greater potential for LLMs.