> ## Documentation Index
> Fetch the complete documentation index at: https://facetai.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Eval and Exports Workflow

> Test your fine-tuned models and run comprehensive evaluations to assess performance

Evaluate a trained model with quick, interactive inference or run a batch job against a dataset to get objective metrics. Review results, compare to a baseline, and export the best checkpoint for deployment.

<Steps>
  <Step title="Select a model">
    Pick a completed training job or a previously exported model.

    <Check>
      Have the model source path and base model ID handy.
    </Check>
  </Step>

  <Step title="Choose evaluation mode">
    Start with single‑prompt inference to sanity‑check behavior, then move to batch evaluation on a labeled dataset.
  </Step>

  <Step title="Pick metrics">
    Select task‑appropriate metrics (e.g., BERTScore/ROUGE for generation, EM/F1 for QA, accuracy/F1 for classification).
  </Step>

  <Step title="Run and monitor">
    Launch the job and watch progress; large datasets take longer.
  </Step>

  <Step title="Review and compare">
    Inspect metrics and samples, compare against a baseline, and note failure patterns to guide the next training round.
  </Step>

  <Step title="Export for serving">
    When satisfied, export the model in your preferred format for deployment.
  </Step>
</Steps>

Next: see the [Inference & Export guide](/evaluation/inference_export) for detailed setup and options.