MLA-C01 ML Model Development Practice Question
A data scientist wants to fine-tune a large language model for a question-answering task. They want to reduce memory usage during training by using a low-rank approximation of the weight updates. Which technique should they use?
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
✓
LoRA
LoRA (Low-Rank Adaptation) adds low-rank matrices to model weights, significantly reducing memory footprint while achieving competitive performance. QLoRA adds quantization for further reduction.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Full fine-tuning
Why it's wrong here
Full fine-tuning updates all weights, requiring more memory.
- ✗
Instruction tuning
Why it's wrong here
Instruction tuning is a fine-tuning method but does not specifically reduce memory via low-rank updates.
- ✓
LoRA
Why this is correct
LoRA uses low-rank decomposition to update weights efficiently, reducing memory usage.
- ✗
RLHF
Why it's wrong here
Reinforcement Learning from Human Feedback is a training paradigm, not a memory reduction technique.
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