Models, artifacts, and runtimes · Lesson 2 of 16 · 8 min

Read a model manifest

A manifest records an artifact contract: format, architecture, tokenizer and context assumptions. Read the manifest before selecting a runtime or an inference envelope.

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learner@clairos:~ $

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Your objectives

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1
Read the case evidence
Read /opt/ai-lab/cases/aiops-models-runtimes-002.json. Separate the recorded facts from your assumptions.
Hint & worked example
A manifest records an artifact contract: format, architecture, tokenizer and context assumptions. Read the manifest before selecting a runtime or an inference envelope.
The complete case JSON is read successfully.
```
cat /opt/ai-lab/cases/aiops-models-runtimes-002.json
```
Common mistake to avoid: Any tokenizer is interchangeable with these weights.
2
Isolate the deciding signal
Select the tokenizer field from the case JSON. What does it establish—and what does it not establish?
Hint & worked example
Use the tokenizer and architecture named in this manifest when choosing a compatible runtime.
A successful JSON query isolates tokenizer.
```
jq .tokenizer /opt/ai-lab/cases/aiops-models-runtimes-002.json
```
Common mistake to avoid: Using a different case or an unrelated signal as the justification.
3
Record your operational decision
Choose the justified code: match-tokenizer, change-without-evidence, or ignore-the-signal. Save that one code followed by a newline in /home/learner/lab/aiops-models-runtimes-002-decision.txt.
Hint & worked example
Use the tokenizer and architecture named in this manifest when choosing a compatible runtime.
The decision artifact contains the correct code, not just a matching command in history.
```
printf '%s\n' 'match-tokenizer' > /home/learner/lab/aiops-models-runtimes-002-decision.txt
```
Common mistake to avoid: Any tokenizer is interchangeable with these weights.

Understand the pattern

A manifest records an artifact contract: format, architecture, tokenizer and context assumptions. Read the manifest before selecting a runtime or an inference envelope.

Interpret the evidence

Use the tokenizer and architecture named in this manifest when choosing a compatible runtime.

A tempting mistake

Any tokenizer is interchangeable with these weights.

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-002.json

{
  "fixture": "Fictional training data; not a live measurement.",
  "format": "gguf",
  "architecture": "training-decoder",
  "tokenizer": "tok-v2",
  "context_tokens": 8192
}

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

$ jq .tokenizer /opt/ai-lab/cases/aiops-models-runtimes-002.json

"tok-v2"

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

$ printf '%s\n' 'match-tokenizer' > /home/learner/lab/aiops-models-runtimes-002-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 “match-tokenizer” to stop being the justified action. Do not silently replace missing evidence with a guess.

Explain it

What does the evidence in “Read a model manifest” justify?Use the tokenizer and architecture named in this manifest when choosing a compatible runtime.Any tokenizer is interchangeable with these weights.Make the change now and gather the missing evidence afterward.

Apply it elsewhere

A colleague concludes: “Any tokenizer is interchangeable with these weights.” What should you do?Accept it because the training command completed.Treat this fixture as a live measurement.Reject that conclusion; return to the case evidence and its stated limits.

Keep the momentum.

Complete the lab, then explain and apply the pattern.

Next: Compare model versions Try an incident drill

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Keep learning

Case methodology: evidence, limits, decision