LoRA, QLoRA, and PEFT

Overview

• PEFT means parameter-efficient fine-tuning: update a smaller subset of parameters instead of the whole model.
• LoRA trains small low-rank adapter matrices attached to selected model operations.
• QLoRA combines adapter training with a quantized frozen base model to reduce training memory.

Why this matters

Full-model training can require far more memory and compute than an application team can justify.

An everyday example

Teach a specialist add-on procedure instead of retraining an entire organization from scratch.

A technical example

Train a task adapter for claim extraction on a supported open model, then evaluate the base model plus adapter together.

How it works

1. Freeze base weights
2. Select target modules
3. Attach/train adapters
4. Validate
5. Deploy compatible base/tokenizer/adapter versions.

Architecture and flow

Frozen base + trainable adapter + examples → training → base/adapter bundle → evaluation.

The insurance assistant example

Maintain one reviewed extraction adapter while keeping policy knowledge in external searchable stores.

When to use it

A justified fine-tuning task where supported adapter techniques meet quality and resource requirements.

When to avoid it

Do not assume adapters fix missing facts or that every quantized model supports the same training method.

Interview answer

“LoRA trains small adapters; QLoRA reduces memory further by quantizing the frozen base. Both still require careful data preparation and evaluation.”

Benefits, trade-offs, and production considerations

• Adapter rank, target modules, precision, and serving compatibility affect outcomes.
• Track base revision and preprocessing; an adapter is not portable across arbitrary base models.
• Hugging Face PEFT, quantization guidance.