Fine-Tuning LLMs: A Comprehensive Tutorial
SMRTR summary
Fine-tuning lets developers adapt pre-trained AI language models to specific tasks in hours or days, using a fraction of the resources needed to build one from scratch. This tutorial walks through four core methods — supervised fine-tuning, unsupervised training, Direct Preference Optimization, and reinforcement learning — then demonstrates a complete Python pipeline using a math-problem-solving model.
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