MLA-C01 ML Model Development Practice Question
A team is fine-tuning a foundation model using LoRA. They want to reduce memory usage during training. Which technique should they combine LoRA with to further reduce memory?
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
✓
QLoRA
QLoRA combines LoRA with quantization (e.g., 4-bit) to drastically reduce memory. Instruction tuning is a method, not a memory reduction technique. RLHF is a training process. Pruning reduces model size but is not typically combined with LoRA in this context.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Instruction tuning
Why it's wrong here
Instruction tuning is a training method for following instructions, not memory reduction.
- ✗
Pruning
Why it's wrong here
Pruning reduces number of parameters but is less effective than quantization when combined with LoRA.
- ✗
RLHF
Why it's wrong here
RLHF is a reinforcement learning process for aligning models, not memory reduction.
- ✓
QLoRA
Why this is correct
QLoRA quantizes the base model to 4-bit, reducing memory further.
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