peft
Applied AI interview questions tagged peft, across every topic.
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Concepts behind "peft"
The curriculum that explains the ideas these questions test.
Core
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.🧠 Foundations of LLMs & GenAISign in
Core
Multi-LoRA ServingLoRA adapters are tiny weight deltas on top of a shared base model, so you can serve hundreds of fine-tuned variants from one set of base weights instead of one full model per tenant. The serving challenge is batching requests that use different adapters in the same forward pass, swapping adapters in and out of GPU memory on demand, and sharing the base model's KV cache machinery. Applied-AI interviews probe it because it is the economics behind per-tenant and per-task customization and the serving-side complement to LoRA training.🖥️ ML Infrastructure & ServingSign in