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Knowledge distillation is a model training technique where a smaller student model learns from a larger teacher model, improving efficiency while maintaining performance. This article analyzes whether DeepSeek models were distilled from GPT, examining data, logits, and feature distillation methods. (知识蒸馏是一种模型训练技术，通过教师-学生架构让小模型从大模型中学习知识，在提升效率的同时保持性能。本文深入分析DeepSeek是否从GPT蒸馏而来，探讨数据蒸馏、Logits蒸馏和特征蒸馏三种方法。)