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LoRA and Parameter-Efficient Fine-tuning

Full fine-tuning updates every weight in a model, which is expensive to train and produces a full-size checkpoint per task. LoRA freezes the base model and trains small low-rank adapter matrices instead, giving tiny swappable checkpoints; QLoRA adds a quantized frozen base so the whole thing fits on a single GPU. FDE loops probe it because it is how you adapt a model on a customer's data without their budget or their hardware blowing up.

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RELATED CONCEPTS

Fine-tuning vs RAG vs Prompting RLHF (Alignment)Quantization

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ML System Design (Product)Design a music recommendation system (Spotify-style).→AI Security, Privacy & GovernanceWhat is data/model poisoning, and how would you detect a backdoor introduced through fine-tuning or embedding data?→LLM & GenAI FundamentalsA customer wants the model to 'know our docs.' Prompting, RAG, or fine-tuning: how do you choose?→RAG & Agent System DesignRetrieval fails on the customer's internal jargon. Fine-tune embeddings, add a reranker, or rewrite queries?→ML System Design (Product)Design the 'For You' ranking system for a short-video feed.→LLM & GenAI FundamentalsWhen does fine-tuning beat few-shot prompting? How much data do you need, and how do you prevent regressions?→