LoRA and QLoRA concepts in Prompt Engineering / GenAI - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does LoRA stand for in machine learning?

LoRA stands for Low-Rank Adaptation. It is a method to efficiently fine-tune large models by updating only small, low-rank matrices instead of the full model.

intermediate

How does LoRA reduce the number of parameters to update during fine-tuning?

LoRA inserts small low-rank matrices into the model layers and only trains these matrices. This reduces the number of parameters updated compared to training the entire model.

intermediate

What is QLoRA and how does it differ from LoRA?

QLoRA stands for Quantized LoRA. It combines LoRA's low-rank adaptation with quantization, which reduces model size by using fewer bits per parameter, enabling fine-tuning on smaller hardware.

beginner

Why is quantization useful in QLoRA?

Quantization reduces the memory and compute needed by representing model weights with fewer bits. This makes it possible to fine-tune large models on less powerful devices.

beginner

In simple terms, how would you explain the benefit of using LoRA or QLoRA?

They let you update a big model quickly and with less computer power by only changing small parts of it, making it easier and cheaper to customize AI models.

What is the main goal of LoRA in model fine-tuning?

A To update only small low-rank matrices instead of the full model

B To increase the size of the model

C To remove layers from the model

D To train the entire model from scratch

LoRA focuses on updating small low-rank matrices to reduce the number of parameters trained.

What does QLoRA add to the LoRA method?

A Quantization to reduce model size and memory use

B More layers to the model

C A new optimizer

D Data augmentation techniques

QLoRA combines LoRA with quantization to make fine-tuning more memory efficient.

Why is quantization helpful for fine-tuning large models?

A It makes the model bigger

B It increases model accuracy automatically

C It removes the need for training data

D It reduces memory and compute requirements

Quantization reduces the size of model weights, lowering memory and compute needs.

Which of these is NOT a benefit of LoRA?

A Faster fine-tuning with fewer parameters

B Trains the entire model from scratch

C Easier to customize large models

D Requires less computer memory

LoRA does not train the entire model from scratch; it updates small parts only.

LoRA is best described as a method to:

A Generate new training data

B Compress data before training

C Adapt large models efficiently by training small matrices

D Replace neural networks with decision trees

LoRA adapts large models by training small low-rank matrices efficiently.

Explain in your own words what LoRA is and why it helps with fine-tuning large AI models.

Think about how updating fewer parts of a big model can save time and memory.

Describe how QLoRA improves on LoRA and what problem it solves.

Focus on how using fewer bits per parameter helps with hardware limits.