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Models, artifacts, and runtimes · Lesson 4 of 16 · 5 min

Inspect artifact metadata

Artifact metadata explains what was downloaded or built. Format, size and quantization are useful checks, but an artifact name alone is not a provenance guarantee.

Open the labCourse map
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learner@clairosSimulated AI Ops lab
learner@clairos:~ $
↑ history · Tab complete · Ctrl+C cancel · Esc, Tab to leaveExit 0

Understand the pattern

Artifact metadata explains what was downloaded or built. Format, size and quantization are useful checks, but an artifact name alone is not a provenance guarantee.

Interpret the evidence

Verify the artifact origin and hash before activation.

A tempting mistake

Trust the artifact solely because its filename ends in .gguf.

All names, measurements, prices, versions, and thresholds in this exercise are fictional training data. The evidence supports this bounded scenario, not a claim about a live provider.

Worked example and expected result
$ cat /opt/ai-lab/cases/aiops-models-runtimes-004.json
{
  "fixture": "Fictional training data; not a live measurement.",
  "filename": "assistant.gguf",
  "bytes": 4200000000,
  "quantization": "q4",
  "provenance_verified": false
}

Exit status: 0. The command completed successfully in the training fixture.

$ jq .provenance_verified /opt/ai-lab/cases/aiops-models-runtimes-004.json
false

Exit status: 0. The command completed successfully in the training fixture.

$ printf '%s\n' 'verify-provenance' > /home/learner/lab/aiops-models-runtimes-004-decision.txt
(no output; exit 0)

Exit status: 0. The command completed successfully in the training fixture.

Try a variation

Name the assumption that would have to change for “verify-provenance” to stop being the justified action. Do not silently replace missing evidence with a guess.

Explain it

What does the evidence in “Inspect artifact metadata” justify?

Apply it elsewhere

A colleague concludes: “Trust the artifact solely because its filename ends in .gguf.” What should you do?

Keep the momentum.

Complete the lab, then explain and apply the pattern.

Next: Verify an artifact checksumTry an incident drill

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Keep learning

  1. 01Compare model versionsA version comparison must hold the task and evaluation inputs constant. A better aggregate result from another dataset is not a controlled improvement over the current model.Practice
  2. 02Verify an artifact checksumChecksums detect byte differences relative to a reference. The reference must itself be trusted, and a mismatch should stop activation until its cause is understood.Practice
  3. 03Match model and runtimeA runtime must support the model architecture and storage format. Insufficient memory and unsupported architecture are different failures and need different responses.Practice

Case methodology: evidence, limits, decision