MLA-C01 ML Model Development Practice Question
A company is fine-tuning a large language model using LoRA on SageMaker. They want to reduce GPU memory usage during training. Which configuration change would help?
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
✓
Use QLoRA (quantized LoRA) with 4-bit quantization
LoRA reduces trainable parameters, and when combined with QLoRA (quantized LoRA), it further reduces memory by quantizing the base model to 4-bit or 8-bit. Increasing batch size or sequence length typically increases memory usage. Gradient accumulation also increases memory as it requires storing gradients for multiple steps. QLoRA is specifically designed for memory 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.
- ✓
Use QLoRA (quantized LoRA) with 4-bit quantization
Why this is correct
QLoRA combines LoRA with quantization, significantly reducing memory footprint while maintaining performance.
- ✗
Enable gradient accumulation
Why it's wrong here
Gradient accumulation simulates larger batch sizes without increasing memory per step, but it does not reduce peak memory usage; it may even require storing gradients for multiple micro-batches.
- ✗
Increase the sequence length
Why it's wrong here
Longer sequences require more memory for attention computations and hidden states.
- ✗
Increase the batch size
Why it's wrong here
Increasing batch size increases memory usage because more samples are processed simultaneously.
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