️ Fine-Tuning

Fine-tuning does not have to be a heavy, complex project. This series shows why parameter-efficient methods like LoRA are useful, what sits behind settings such as rank and alpha, and which common mistakes render a fine-tuning run ineffective. The focus is on understanding rather than ready-made recipes: once it is clear what each variable does, you can adapt a model to your need deliberately and at minimal cost.