Micro language models and small language models

Explains practical small-model categories without inventing universal parameter thresholds, and distinguishes parameters, artifact size, context, and runtime memory.

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Explains practical small-model categories without inventing universal parameter thresholds, and distinguishes parameters, artifact size, context, and runtime memory.

Scope

This starter page defines the questions, boundaries, evidence, and failure modes that should be recorded before a capability is presented as supported.

Engineering considerations

  • Identify the source, version, target environment, and owner.
  • Separate observed values from estimates and externally reported values.
  • Record trade-offs, unsupported cases, and fallback behavior.
  • Link performance statements to a compatible benchmark methodology.

Verification questions

  • What exact artifact, revision, backend, and environment were reviewed?
  • Which assumptions could change the result?
  • Which data should be retained so another engineer can reproduce the conclusion?