wunder beta📘 Keep fine-tuning from meaning scratch
Fine-tuning adapts a pretrained model to a task — it is not training from scratch. Full updates, adapters, and alignment each spend different budgets.
3
lessons
~15 min
to learn
Adults
level
What you’ll learn
- Reuse then specializeFrame fine-tuning as adapt-after-pretrain.Reuse general features; spend a smaller task budget.
- Update budgets and forgettingCompare update scopes and forgetting risk.Full tune, PEFT, and evals for old skills.
- Alignment as another adapt stepPlace instruction/RLHF as further adaptation.Behavior under prompts is also a fine-tune story.
Grounded in trusted sources
- Howard & Ruder (2018), ULMFiT
- Pretrain-then-adapt transfer learning primers
- Parameter-efficient fine-tuning overviews
- Catastrophic forgetting teaching notes
- Instruction tuning / RLHF survey primers
- Task eval + regression suite practices
Every Wunder lesson is built from real, reputable sources — never invented.
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