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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COURSES COVERING THIS TOPIC

No lesson covers this one directly yet. These teach the surrounding topic from the beginning.

Applied AI Engineering·The interviewPremium12mTurning this course into a study planA concrete four-week plan mapping the seven modules onto the question bank, plus what to do differently if your interview is next week rather than next month.

RELATED CONCEPTS

Prompting vs RAG vs Fine-Tuning DPO and Preference-Optimization Variants Quantization and Low Precision Multi-LoRA Serving

PRACTICE THIS IN REAL QUESTIONS

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?→

COMPANIES THAT ASSUME THIS

OpenAI Anthropic Google Microsoft Scale AI Decagon

NEXT IN FOUNDATIONS OF LLMS & GENAIDPO and Preference-Optimization Variants→