Explains fixture-case input, native runtime execution, exact expected/actual comparison, sidecar output, case ledgers, and scope limitations.
Implementation evidence: this topic is grounded in the reviewed GGUF.MiRust.com source snapshot. It documents observed code and artifacts without claiming broad deployment, model quality, or production readiness.
Inputs
The tinyrustlm-eval
Execution
It loads the same runtime model path used by the library, executes each scoped prompt with deterministic settings, captures actual output, compares it with expected output, and writes totals plus per-case evidence.
Gate requirements
Assistant-quality fixture gating requires positive case count, zero failed cases, exact expected/actual values, and no missing or stray case keys.
Scope
The supplied cases are fixture-scoped and validate conversion/runtime agreement. Exact deterministic strings are not a broad language-quality benchmark, safety evaluation, robustness test, or human preference assessment.
Next evaluation layer
Add immutable datasets, task-specific metrics, negative and adversarial cases, tokenizer coverage, quantization deltas, latency/memory data, evaluator version, and independent review.
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?