LLM, et al: Instruction Fine-Tuning

Here’s a tight summary of the Instruction Fine-Tuning lesson:

What it is & why it matters

Instruction fine-tuning = a form of fine-tuning that teaches an LLM to follow instructions and chat (how GPT-3 became ChatGPT).
It changes behavior (instruction-following, dialogue turns, helpful tone), not just facts—making models a better UI for people.

Sources: existing FAQs, customer support chats, Slack messages, other dialogue or Q/A style data.
If you lack data: convert docs to Q/A with prompt templates or use synthetic data (e.g., Alpaca approach using another LLM).

After instruction tuning, the model often generalizes the instruction-following skill to domains not explicitly in the fine-tune set (e.g., answering code questions even without code Q/A pairs).

Data prep → Training → Evaluation → Iterate.
The big differences across fine-tuning styles are mostly in data prep (how you structure prompts/answers); training & eval are similar.

Dataset: Alpaca (loaded/streamed; examples show two prompt templates):
- With input (instruction + extra fields), and without input.
- Prompts are “hydrated” and written to JSONL; also available on hubs for reuse.
Model comparisons on prompts like “Tell me how to train my dog to sit”:
- Base LLaMA-2 (not instruction-tuned): confused, repeats or drifts.
- LLaMA-2-Chat (instruction-tuned): coherent, step-by-step answer.
- ChatGPT: strong, detailed response (much larger model).
Small model case:
- Pythia-70M (no instruction tuning): off-target answers, misses task behavior.
- Instruction-tuned version: answers correctly and in the expected format.

Instruction fine-tuning instills instruction-following/chat behavior, boosting usefulness and reliability.
Good prompt templates and well-structured dialogue/Q&A data are crucial.
Even tiny models benefit markedly; larger instruction-tuned models behave closest to production chat systems.