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Fine-Tuning and LoRA: What Training on Your Examples Changes, and What It Costs
Fine-tuning trains a pre-trained model further on your examples to change its behaviour: output format, style, or skill at a narrow task. LoRA and QLoRA can reduce adaptation memory and cost by training small adapters. This guide covers what fine-tuning is good and bad at, the data it needs, the cost and maintenance it brings, and how to decide.
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