Run a first baseline

SAVRN Cloud · Coming soon · Public preview · Browser-local simulation · No live compute or payments

Run the local baseline

Open Evaluations and choose New evaluation. Select the model, task environment and a dataset marked Frozen evaluation set, then start the simulation. Inspect deterministic sample scores and per-case results; Export receipt retains their label. Model detail Evaluate this model preserves model context. Candidate evaluation requires a dataset different from its training dataset.

A baseline is a reference point for the same task, not an arbitrary leaderboard score. The local result is synthetic and should retain that label in exports.

Example report boundary

This illustrates the minimum provenance questions; it is not the application's import or API schema:

Copy{
  "simulation": true,
  "schema_status": "illustrative",
  "model": "Research 8B",
  "task_revision": "demo-1",
  "result_kind": "synthetic fixture",
  "measured_production_score": null
}

Inspect whether the local screen explains what ran and where to find the result. Do not fill a missing measured score with the fixture value.

Before connected services launch

Freeze approved inputs, rubric, model offer and settings. Run a bounded sample, retain per-case outcomes and reconcile usage. Include failures, exclusions and uncertainty. Establish the minimum quality and operational thresholds before testing a candidate. A baseline should remain reproducible after later environment edits, model promotions and provider changes; preserve the original revision rather than overwriting its record.