Write a model adaptation experiment report

An experiment report should let a reviewer understand the decision and a practitioner reproduce the run. A list of hyperparameters without data, environment, adverse results and acceptance criteria is incomplete.

Validate the evidence index

The adaptation lab creates a deliberately non-releaseable fixture report.

from adaptation_cases import experiment_report_case

result = experiment_report_case()
assert result["complete"] is True
assert result["report"]["adverse_results"] == ["safety regression B3"]
assert result["report"]["decision"] == "do not release"
assert result["release_approved"] is False
assert result["actual_experiment"] is False

The report has every required field while clearly stating that no experiment ran and license review remains pending. Completeness does not imply approval.

Lead with the decision question

State the behavioural hypothesis, baseline and predeclared acceptance rules. Identify base checkpoint/revision, adapter or derived artifact, prompt, tools, retrieval, code commit and dependency lock. Link data provenance, grouped splits, exclusions and sealed holdout.

Record hardware, precision, resource limits, seed/settings, hyperparameters, checkpoints and run logs. Report paired task/slice results, confidence or repeated-run variation, critical failures, peak memory, duration and cost boundaries. Include negative and null results; do not select only the best trial.

Document license and privacy review, known limitations, unresolved questions, rollback requirements and the release/continue/stop decision with owner. Provide commands from a clean environment and hashes for artifacts. Separate actual measurements, estimates and authored examples.

The Generative & Agentic AI course uses the report as the capstone evidence index for model adaptation.

Exercise

Write the report before a small safe experiment, filling unknowns as pending. Run the baseline and one candidate, attach raw result files, then make a decision that follows the predeclared gate.

Continue learning

This article is part of the Model adaptation and multimodal tasks sequence. Use the neighbouring tasks when you need the prerequisite or the next application.

• Review the preceding task in Preference tuning: distinguish ranking examples from factual supervision.
• Return to the cluster foundation in Fine-tuning versus retrieval for a changing knowledge base.

References: Hugging Face model cards and OpenAI Evals API.