Instruction Tuning — why the transformation works

Instruction tuning is the specific, most important flavor of SFT: training on (instruction → response) pairs so a base model stops continuing text and starts obeying it. It's what turns Qwen2.5-7B into Qwen2.5-7B-Instruct — the difference you saw with your own eyes in the Lesson 2 exercise.

Why does a few thousand examples flip such a fundamental behavior? Think about what the base model learned from the internet: among trillions of tokens, it saw forums where questions get answers, tutorials, Q&A sites, documentation. So the skill of answering is already inside — but it's one persona among thousands (it equally learned to continue rants, spam, and lists of exam questions). Instruction tuning doesn't teach answering; it collapses the persona distribution — massively raising the probability that "question-shaped input → helpful-answer-shaped output" wins over all the other learned continuations. That's why LIMA's 1,000 examples were enough: they were selecting a behavior, not installing one.

This gives you a sharp lens for what instruction tuning can and cannot do:

• ✅ Can: unlock and standardize behaviors the base model already latently has — formats, task-following, tone, refusing gracefully, using domain vocabulary it saw in pretraining.
• ❌ Can't: create capabilities absent from pretraining. If the base model never saw meaningful amounts of Kotlin, no amount of instruction tuning produces a good Kotlin assistant.
• ⚠️ Dangerous: training it to answer questions whose answers it doesn't actually know. Read that twice, because it's a genuinely advanced insight most practitioners miss: if your SFT data contains answers built from facts outside the model's knowledge, the gradient can't install those facts — what it can install is the behavior pattern "when asked obscure things, produce confident-sounding specific answers." You are literally training hallucination. Research on this (e.g., work following Gekhman et al., 2024) shows fine-tuning on unknown-to-the-model facts measurably increases hallucination. This is a core reason "fine-tune facts in" fails, and it previews our hallucination topic next lesson. The fix: SFT answers should either draw on knowledge the model plausibly has, or demonstrate saying "I don't know" / consulting provided context.

Practical training-process notes (full mechanics in Module 3, but these belong to the data story):

• Epochs = full passes over your dataset. Instruction tuning typically uses 1–3. More passes on a small dataset → the model starts memorizing your examples verbatim instead of learning the pattern — overfitting. Symptom: it parrots training answers word-for-word even for slightly different questions.
• Always fine-tune from the instruct version of a model when your data is small and chat-shaped — you inherit all the alignment work. Start from the base model only when you have lots of data and want full control of the persona.

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