How to fine-tune an LLM: a developer's guide

Fine-tuning adapts a pretrained language model to a specific task, style, format, or behavior by continuing training on curated examples, whereas prompting changes instructions without altering weights and retrieval-augmented generation is generally better for current or factual knowledge. It is most useful when prompting cannot reliably enforce output schemas, brand voice, tool use, domain-specific workflows, or cost-efficient performance at high request volumes, but it requires substantial investment in clean, representative training data, held-out evaluations, deployment, and ongoing maintenance as base models improve. The recommended starting approach is supervised fine-tuning with LoRA, which trains small adapter weights rather than all model parameters, while full fine-tuning is reserved for cases where LoRA cannot reach the required quality and QLoRA helps fit larger models on limited hardware. Effective training depends more on dataset quality, correct chat templates, loss masking, sequence lengths, and validation splits than on hyperparameter tuning, with hundreds to thousands of examples and an untouched test set commonly needed. Preference methods such as DPO are best used after supervised training to refine subjective behaviors, while task-specific metrics, baseline comparisons, and regression testing are needed to detect overfitting and catastrophic forgetting. Fine-tuned models can be served as merged weights or lightweight adapters, and although training is billed by processed tokens, long-term hosting costs and potential inference savings should determine whether the effort is economically worthwhile.

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