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qlora

Applied AI interview questions tagged qlora, across every topic.

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Concepts behind "qlora"

The curriculum that explains the ideas these questions test.

Core
🧠 Foundations of LLMs & GenAISign in
LoRA and Parameter-Efficient Fine-TuningFull 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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