Why it matters

Transfer learning is why LLMs are practical. Without it, everyone would need to train from scratch. Understanding it enables cost-effective ML.

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The architecture

Pretrain: train big model on generic huge data. Learn general representations.

Fine-tune: continue training on task-specific data. Adapt to task.

Transfer learning pipelinePretrainhuge generic dataFine-tunetask-specific dataDeploytask-specific modelPEFT methods (LoRA) make fine-tuning very cheap; standard modern practice
Transfer learning steps.
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How it works end to end

Full fine-tune: update all weights. Best quality; expensive.

Frozen features: freeze base, train only new head. Fast, less quality.

PEFT (LoRA, adapters): parameter-efficient fine-tuning. Best cost-quality trade-off.

Prompt engineering / few-shot: no training. Only prompt the pretrained model. Simplest.