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Core
LoRA and Parameter-Efficient Fine-Tuning
Full fine-tuning updates all of a model's weights, which is expensive in compute and memory and produces a full-size copy per task. LoRA freezes the base model and trains small low-rank adapter matrices, cutting trainable parameters by orders of magnitude while matching most of full fine-tuning's quality. QLoRA adds 4-bit base quantization to fit huge models on one GPU. Applied-AI interviews probe it because PEFT is how teams actually fine-tune, and because LoRA adapters enable serving hundreds of variants cheaply.
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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 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?→ML Infrastructure & GPUsHow do you serve many fine-tuned model variants efficiently (multi-LoRA serving)?→LLM & GenAI FundamentalsWhen do you choose prompting vs RAG vs fine-tuning for a customer problem?→
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