Fine-tuning Videos

Fine-tuning adapts a pretrained model to your domain, tone, or task — and techniques like LoRA and QLoRA have made it affordable enough to run on a single GPU. It is also an area where small implementation details (data formatting, learning rates, evaluation) dominate outcomes, so screen-recorded training runs and post-mortems are unusually valuable. The videos here cover parameter-efficient methods, preference tuning like RLHF and DPO, and when fine-tuning beats prompting or RAG. This page aggregates Fine-tuning videos from every creator we track, so you can compare how official labs, educators, and practitioners approach the same subject. Videos are a starting point, not the whole picture. Below the video feed you will find hand-picked learning guides that explain the underlying concepts in depth, popular open-source GitHub repositories where the ideas live as code, and the AI tools most closely associated with Fine-tuning. We also surface the latest news coverage and research related to the topic, because a release video, its paper, and its press coverage each tell a different part of the story. Together they make this page a practical hub for going from "I watched a video about Fine-tuning" to actually understanding and building with it.