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Fine-Tuning & Model Adaptation

When to fine-tune versus prompt versus retrieve, and how to do it well: LoRA, QLoRA and PEFT, data curation that decides the outcome, catastrophic forgetting and its mitigations, and evaluating a fine-tuned model without fooling yourself.

1Fine-Tune vs Prompt vs RAGA practical guide to choosing between instructions, retrieved knowledge, and trained behavior for large language model applications.Advanced

2LoRA, QLoRA & PEFTLoRA and QLoRA let teams adapt large models by training small add-ons instead of rewriting every weight.Advanced

3Data Curation for Fine-TuningFine-tuning is only as good as the examples you choose, clean, label, test, and protect.Advanced

4Catastrophic Forgetting & MitigationsWhy models lose old skills when learning new ones, and how to reduce the damage without freezing progress.Advanced

5Evaluating a Fine-Tuned ModelA fine-tuned model is only better if it proves better on the work, risks, and constraints that matter.Advanced