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Learn how to adapt pretrained LLMs to specific tasks and domains. Master the spectrum of fine-tuning approaches from full parameter updates to parameter-efficient methods like LoRA and QLoRA, understand instruction tuning that transforms base models into helpful assistants, and learn best practices for data preparation and evaluation.
Pretraining gives an LLM broad language understanding, but a pretrained model is just a next-token predictor---it does not follow instructions, answer questions helpfully, or refuse harmful requests. Fine-tuning bridges this gap by adapting the model to specific behaviors or domains using curated datasets.
There are two major paradigms of fine-tuning. Full fine-tuning updates all model parameters, achieving the highest quality but requiring significant compute (often 10-100x less than pretraining, but still substantial for large models). Parameter-efficient fine-tuning (PEFT) methods like LoRA update only a small fraction of parameters, making it possible to fine-tune 70B+ models on a single GPU.
The most impactful form of fine-tuning for modern LLMs is instruction tuning: training on (instruction, response) pairs to make the model follow user requests. This is what transforms a base model like Llama into a chat model like Llama-Chat. Combined with RLHF (Chapter 6), instruction tuning produces the helpful, harmless assistants we interact with today.
This chapter covers:
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Why pretrained knowledge transfers to downstream tasks — catastrophic forgetting, feature extraction, and domain adaptation.
Full updates vs parameter-efficient methods
Updating all parameters with SFT — when maximum quality justifies the compute cost and overfitting risks.
Low-rank adaptation that trains <1% of parameters — the method that democratized fine-tuning on consumer GPUs.
Instruction formats and data quality
Transforming base models into helpful assistants with instruction-response pairs and chat templates.
Quality over quantity — decontamination, formatting consistency, and augmentation for fine-tuning datasets.
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