Instruction Fine-Tuning (IFT)
Fine-tune on (instruction, response) pairs to transform a next-token-prediction base model into one that follows natural language directives.
Intent & Description
🎯 Intent
A base LLM predicts next tokens — it doesn’t answer questions or follow instructions. Instruction fine-tuning teaches it to interpret directives and produce task-appropriate responses.
📋 Context
GPT-3 base, Llama-2 base — powerful completion models that don’t inherently respond to “summarize this” or “write a function that does X.” The underlying capability exists; the alignment to the instruction-following format does not.
💡 Solution
Curate or generate a dataset of (instruction, optional-input, output) triplets covering diverse task types — summarization, QA, coding, reasoning, creative writing, formatting. Fine-tune the base model on these examples using standard supervised learning. Data quality matters far more than quantity — 10K high-quality curated examples consistently outperform 1M low-quality scraped ones.
Real-world Use Case
📌 TL;DR
Teach the base model that instructions are for following, not completing. Quality of training examples is everything — a small curated dataset beats a massive noisy one every time.
Advantages
- Transforms a completion model into an instruction-following model at relatively low data and compute cost
- Serves as the required SFT foundation for further RLHF or DPO alignment
- Data quality over quantity — curated small datasets beat massive noisy ones
Disadvantages
- Low-quality instruction data produces compliant but incorrect or unsafe responses
- Model may learn to follow the surface format of examples without generalizing the underlying intent
- Does not guarantee alignment with human preferences — RLHF or DPO typically follows