AI0-001 Implementing AI Solutions Practice Question
A developer is fine-tuning a large language model for a code generation task. The available GPU has only 8GB of VRAM, and the base model is 7B parameters. Which fine-tuning technique is MOST feasible?
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 (Quantized Low-Rank Adaptation)
QLoRA (Quantized Low-Rank Adaptation) combines quantization and LoRA to fine-tune large models on limited VRAM.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
QLoRA (Quantized Low-Rank Adaptation)
Why this is correct
QLoRA quantizes the base model to 4-bit and uses LoRA adapters, making it possible to fine-tune a 7B model on 8GB VRAM.
- ✗
LoRA (Low-Rank Adaptation)
Why it's wrong here
LoRA reduces trainable parameters but still requires significant VRAM for the base model; 8GB may be insufficient for a 7B model without quantization.
- ✗
Instruction tuning with a smaller model
Why it's wrong here
While using a smaller model is an alternative, the question asks about fine-tuning the given 7B model.
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Full fine-tuning of all parameters
Why it's wrong here
Full fine-tuning of a 7B model requires far more than 8GB VRAM on a single GPU.
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