Practice · Established · Intermediate

Instruction Tuning

Fine-tuning a base model on instruction-and-response pairs so it follows requests rather than merely continuing text.

What Instruction Tuning is

A raw pretrained model completes documents. Instruction tuning is what turns that into something that answers a question when asked one.

How it works

Curated demonstrations covering many task types — summarise, classify, rewrite, explain, refuse — are used for supervised fine-tuning, usually before preference tuning.

Why it matters

It is the step that made language models usable by non-specialists, and the reason a small well-tuned model can feel more helpful than a larger untuned one.

Common uses

→Building chat assistants
→Task-specific instruction sets
→Open-weight model post-training

Strengths

✓Massive usability improvement
✓Modest data requirements

Watch for

✓Quality of demonstrations caps behaviour
✓Can narrow creative range

Continue exploring

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