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AI0-001 Implementing AI Solutions Practice Question

A team fine-tunes a 7B parameter LLM using LoRA on a custom instruction dataset. After training, they observe that the model's outputs are only marginally different from the base model. Which is the MOST likely cause?

Answer choices

Why each option matters

Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.

Correct answer & explanation

The LoRA rank was set too low (e.g., r=1), limiting the adapter's capacity to learn the task

LoRA has a rank hyperparameter that controls adapter expressiveness. If the rank is too low, the adapter cannot capture the desired task. Other hyperparameters like learning rate affect convergence but rank directly impacts capacity.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • The dataset contained too many examples, overfitting the adapter

    Why it's wrong here

    Overfitting would cause the model to memorize and produce outputs that differ noticeably from the base model.

  • The base model was too small to benefit from fine-tuning

    Why it's wrong here

    Even small models can benefit from fine-tuning; marginal difference suggests adapter capacity issue, not base model size.

  • The LoRA rank was set too low (e.g., r=1), limiting the adapter's capacity to learn the task

    Why this is correct

    Low rank reduces the number of trainable parameters; the adapter may not have enough capacity to alter behavior significantly.

  • The learning rate was too high, causing the model to diverge

    Why it's wrong here

    Divergence would cause poor outputs, not marginal difference; outputs would be erratic.

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Written by Johnson Ajibi, MSc IT Security

Senior Network & Security Engineer · founder of Courseiva

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