peft

PEFT is how everyone fine-tunes large models now. What matters is the low-rank insight behind LoRA, why it cuts memory so sharply, and what 4-bit QLoRA adds. Here is the answer that goes past 'it's efficient fine-tuning.'

Fine-tune a model on your domain and it may lose the ability to do everything else. What earns the signal is explaining why shared weights cause it and listing the concrete mitigations that all boil down to one principle.

PEFT is a family, not only LoRA, and interviewers test whether you know how each one injects trainable parameters. The signal is where each method adds capacity and the latency and quality tradeoffs that result.

Fine-tuning a 65B model once demanded a node of A100s. QLoRA compresses it onto one card with three specific tricks. The signal is knowing what gets quantized, what stays trainable, and why quality barely shifts. Here is the answer.

Everyone can name learning rate and epochs. The signal is knowing that LoRA wants a learning rate roughly 10x higher than full fine-tuning, why alpha/r is the only scaling that matters, and which curve tells you your rank is too big.

Both work, so the decision is made by four numbers: how many examples you have, how far the capability has to move, how many variants you must serve, and how many GPUs you own. The serving axis is the one candidates never mention.