Article
A zero-shot baseline should precede every fine-tuning experiment. Without this baseline, a team cannot determine whether adaptation improved the model or merely added training cost and system complexity.
The baseline and the adapted model must use the same holdout period, forecast horizon, target definition, metrics, and data-availability rules. For time-dependent tasks, the split must also preserve chronological order. These controls isolate the effect of adaptation and reduce the risk that different test conditions explain the observed result.
Metric selection should reflect the intended decision. Mean absolute error may suit a forecast with approximately symmetric error costs, while quantile loss can assess probabilistic forecasts. Classification tasks may require precision, recall, calibration error, or a cost-weighted metric. The team should define these criteria before the fine-tuning job starts.
An adapted model should also meet non-accuracy requirements. These requirements can include inference latency, interval calibration, memory demand, compute cost, and stability across relevant data segments. A small metric improvement may not justify adaptation if the adapted model increases latency or performs inconsistently under changed conditions.
The eomer model registry retains the base model, adapted versions, evaluation results, and experiment configuration. This record allows teams to compare versions under consistent conditions and reproduce the selected result.
If fine-tuning does not improve the predefined criteria, the base model remains the simpler choice. The unsuccessful experiment still provides useful evidence because it defines where adaptation did not add value.
