Building an LLM Stack Part 3: The art and magic of Fine-tuning

Large language models are first pre-trained on enormous unlabeled text corpora to develop broad language knowledge, then fine-tuned on much smaller task-specific datasets to make them useful for applications such as conversation, question answering, and summarization. Fine-tuning is far less costly than pre-training and commonly begins with supervised fine-tuning, which trains on curated prompt-response pairs using next-token prediction, though results depend heavily on data quality and can risk catastrophic forgetting. Further alignment can use reinforcement learning from human feedback, in which people rank model outputs to train a reward model that guides optimization, or Direct Preference Optimization, which directly learns from preferred and rejected responses without requiring a separate reward model or reinforcement-learning loop. The discussion also highlights parameter-efficient fine-tuning methods such as LoRA, which learns small low-rank weight updates instead of modifying all model parameters, and QLoRA, which adds quantization and memory-management techniques to make large-model fine-tuning feasible on more modest hardware.

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