Large language models (LLM) have undergone significant transformations through the application of knowledge distillation techniques aimed at enhancing performance on complex benchmarks like the MMLU. The research detailed herein introduces a novel two-stage distillation process designed to refine the capabilities of Mistral Large, resulting in marked improvements in both accuracy and contextual understanding. Initially, the model undergoes a teacher-student training phase where a high-performing teacher model imparts its knowledge to a less complex student model, utilizing both soft and hard target training methods to optimize knowledge transfer. This is followed by a specialized refinement stage where the student model is further fine-tuned on tasks that require advanced cognitive skills, specifically tailored to the challenges presented by the MMLU benchmark. Quantitative results indicate a substantial increase in accuracy across various tasks within the benchmark, while qualitative analyses show enhanced linguistic sophistication and contextual relevance in the model's responses. Comparisons with baseline models confirm that the distilled Mistral Large significantly outperforms traditional training approaches, setting new performance standards for language models. The implications of our findings suggest that the structured application of knowledge distillation can fundamentally alter the development trajectory of language models, making them more efficient and effective across diverse applications. The study's approach offers a scalable framework for future enhancements and has the potential to influence a wide range of applications in artificial intelligence, from automated conversational systems to sophisticated analytical tools.