In this video, I break down LLM distillation the way I like to think about it in system-design terms: you’ve got a “teacher” model (think GPT-4 / DeepSeek / Gemini) that’s powerful but expensive, slow, and hard to deploy, and you want a “student” model that’s small enough to actually ship—especially for startups, mid-sized teams, and on-device use cases. The key idea is: don’t just copy final answers. If you only mimic outputs, you lose the real intelligence. So I explain why classic distillation hits limits and why we need a better training signal. Then I walk through “Distilling Step-by-Step” (the paper I link), where we extract rationales using Chain-of-Thought style prompting and train the student to learn both the reasoning steps and the final answer. This is where distillation gets interesting: you can end up with a model that’s dramatically smaller (even orders of magnitude) while staying strong on reasoning—sometimes even outperforming the teacher in specific settings. In the second half, I implement the workflow in Python using Hugging Face AutoTrain, and I explain the code line-by-line so you can reproduce the pipeline and adapt it to your own datasets and deployment constraints.