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Core
LoRA and Parameter-Efficient Fine-Tuning
Full fine-tuning updates all of a model's weights, costly in compute and memory and leaving a full-size copy per task. LoRA freezes the base model and trains small low-rank adapter matrices, dropping trainable parameters by orders of magnitude while matching most of full fine-tuning's quality. QLoRA layers on 4-bit base quantization to fit huge models on one GPU. AI, ML, and GenAI interviews probe it because PEFT is how teams actually fine-tune, and because LoRA adapters make serving hundreds of variants cheap.
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LLM & GenAI FundamentalsExplain LoRA, QLoRA, and parameter-efficient fine-tuning. Why train a fraction of the parameters?→LLM & GenAI FundamentalsWhat is QLoRA, and how does it make fine-tuning large models feasible on one GPU?→LLM & GenAI FundamentalsWhat are the key hyperparameters for fine-tuning an LLM, and how do you actually set them?→LLM & GenAI FundamentalsYou must choose between LoRA and full fine-tuning for a domain assistant. How do you decide?→LLM & GenAI FundamentalsWhat is catastrophic forgetting, and how do you prevent it when fine-tuning or continually training an LLM?→LLM & GenAI FundamentalsCompare LoRA, prefix tuning, prompt tuning, and adapters. How do PEFT methods differ?→
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