Fine-Tuning & Model Customization · TRACK 02/04
LoRA & Efficient Methods
Parameter-efficient tuning that fits on a single GPU.
// THE TRACK
01 · START HERELoRAGrasp the one matrix trick that lets you customize a billion-parameter model by training less than 1% of its weights.BEGINNERQLoRAUnderstand how QLoRA slashes fine-tuning memory by 75% using 4-bit NF4 quantization, so you can adapt a 65-billion-parameter model on a single GPU.INTERMEDIATERank & AlphaStop cargo-culting r=16: understand what rank and alpha actually do to your adapter and how to choose values for your task.INTERMEDIATEMerging LoRA AdaptersDecide whether to keep your LoRA as a hot-swappable adapter or bake it into the weights, and avoid the precision pitfall that silently degrades merged QLoRA models.INTERMEDIATEMulti-LoRA ServingUnderstand how keeping one base model resident and swapping tiny adapters lets a single GPU serve dozens of fine-tuned variants at near-zero extra cost.INTERMEDIATELoRA Target ModulesLearn what LoRA's target_modules setting actually controls and how choosing attention-only versus all-linear layers trades quality against training cost.INTERMEDIATE