Model Customization and Fine-Tuning Concepts
Free NVIDIA-Certified Associate: AI Model & Application Development practice — 6 questions on Model Customization and Fine-Tuning Concepts, with explanations. No sign-up. Full 12-question mixed test →
A team has 500 labeled examples of input-output pairs where outputs must follow a strict JSON schema. They have access to a single A100 GPU and need the model to reliably produce schema-compliant output within two weeks. Which approach BEST meets these constraints?
LoRA/PEFT can reliably adapt output behavior and formatting with a small labeled dataset and limited compute, updating only a small set of adapter weights.
Which statement accurately differentiates Direct Preference Optimization (DPO) from classic RLHF in model alignment pipelines?
DPO reformulates the RLHF objective into a single supervised-style loss computed directly on preference pairs, removing the need for a separate reward model and the PPO reinforcement learning loop.
In a LoRA configuration, increasing the rank (r) parameter from 8 to 64 primarily results in which trade-off?
Rank controls the size of the low-rank adapter matrices; a higher rank increases trainable parameters and adaptation capacity, at the cost of more compute/memory and a higher chance of overfitting on small datasets.
A healthcare startup wants to fine-tune a foundation model on proprietary clinical notes while minimizing the risk of the model losing its general reasoning ability (catastrophic forgetting). Compute budget allows only partial retraining. Which technique best addresses this requirement?
PEFT methods like LoRA freeze the base model weights and train only small adapter layers, which limits catastrophic forgetting of general capabilities while adapting to the clinical domain within a limited compute budget.
An application must answer customer questions using product documentation that changes daily (prices, inventory, specs). Latency must stay low and the team cannot retrain the model every day. Which customization strategy BEST fits this requirement?
RAG decouples factual knowledge updates from the model's weights by retrieving current information from an updated index at query time, keeping answers fresh without any retraining and with low added latency.
QLoRA enables fine-tuning of large language models on a single consumer or data-center GPU primarily by which mechanism?
QLoRA quantizes the frozen base model weights (commonly to 4-bit NF4) to drastically reduce memory footprint, while the LoRA adapters remain trained in higher precision (e.g., bfloat16) to preserve gradient quality during fine-tuning.
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