LoRA and QLoRA concepts in Prompt Engineering / GenAI - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

What is the main purpose of LoRA in machine learning?

LoRA (Low-Rank Adaptation) is a technique used in fine-tuning large models. What is its main goal?

A To train the model from scratch without pre-trained weights

B To increase the size of the model by adding more layers

C To reduce the number of trainable parameters by adding low-rank matrices to existing weights

D To replace the entire model with a smaller one

💡 Hint

Think about how LoRA helps with training efficiency.

LoRA adds small low-rank matrices to existing model weights, allowing fine-tuning with fewer trainable parameters and less memory.

🧠 Conceptual

intermediate

How does QLoRA differ from LoRA?

QLoRA is an extension of LoRA. What key feature distinguishes QLoRA from standard LoRA?

A QLoRA uses quantized weights to reduce memory usage during fine-tuning

B QLoRA trains models without any pre-trained weights

C QLoRA removes low-rank matrices and trains full weights

D QLoRA increases the rank of adaptation matrices compared to LoRA

💡 Hint

Consider how QLoRA manages memory differently than LoRA.

QLoRA applies quantization to model weights, reducing memory use while still fine-tuning with low-rank adapters.

❓ Model Choice

advanced

Which model architecture is best suited for applying LoRA and QLoRA?

You want to fine-tune a large language model efficiently using LoRA or QLoRA. Which architecture is most appropriate?

A Decision trees

B Simple linear regression models

C Convolutional neural networks for image classification

D Transformer-based models with large dense layers

💡 Hint

LoRA and QLoRA are designed for large models with many parameters.

LoRA and QLoRA target large transformer models, which have many dense layers suitable for low-rank adaptation.

❓ Metrics

advanced

What metric best indicates successful fine-tuning with LoRA or QLoRA?

After fine-tuning a language model with LoRA or QLoRA, which metric best shows the model learned well without overfitting?

A Validation loss decreasing and stabilizing

B Training loss increasing steadily

C Validation accuracy dropping sharply

D Training accuracy staying constant at zero

💡 Hint

Good fine-tuning means the model improves on unseen data.

Validation loss decreasing and stabilizing means the model generalizes well after fine-tuning.

🔧 Debug

expert

Why might QLoRA fine-tuning fail with a 4-bit quantized model?

You try to fine-tune a large model using QLoRA with 4-bit quantization but get poor results. What is a likely cause?

A The model is too small to benefit from quantization

B Quantization noise is too high, degrading model performance during fine-tuning

C LoRA adapters are not compatible with quantized weights

D The optimizer does not support low-rank matrices

💡 Hint

Think about how very low-bit quantization affects model precision.

4-bit quantization can introduce noise that harms fine-tuning quality if not carefully managed.