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Alternatives Comparison

Overview

Aspect Details
Purpose Position InvarLock against adjacent evaluation, MLOps, and compression tools.
Audience Prospective users, evaluators, maintainers writing integration guidance.
Scope Current project positioning and handoff guidance for adjacent toolchains.
Source of truth docs/assurance/14-trust-model.md, docs/reference/reports.md, docs/reference/guards.md.

InvarLock is a paired, verifier-friendly regression assurance workflow for edited weights. It fits after edit or compression tooling has produced a subject checkpoint and alongside broader benchmark, monitoring, and registry systems.

When To Use InvarLock

  • You produced an edited checkpoint (quantization, pruning, fine-tune) and need a machine-verifiable artifact showing it stayed within configured bounds.
  • A strict verification workflow needs a fail-closed evaluate -> verify contract with pairing, guard evidence, and runtime provenance.
  • Auditors expect a self-contained report bundle they can re-verify offline.

When To Reach For Something Else

  • You want broad downstream benchmark scores (use lm-evaluation-harness, LightEval, or an in-house evaluation stack).
  • You want production drift monitoring, dashboards, or experiment tracking (use MLflow, Evidently, Deepchecks).
  • You need the tool to produce the compressed checkpoint (use Optimum, Intel Neural Compressor, GPTQModel, etc.); InvarLock validates the artifact afterwards.

Tool Comparison

Tool family Use it for How InvarLock differs
lm-evaluation-harness, LightEval, custom eval runners Broad benchmark quality and task scores. InvarLock focuses on paired baseline-vs-subject windows, guard evidence, runtime provenance, and a standalone report verifier.
OpenAI Evals Custom LLM and system evaluations. InvarLock operates on local checkpoint comparisons and weight-edit evidence.
MLflow, Evidently, Deepchecks Experiment validation, monitoring, drift, and dashboards. InvarLock adds a narrow fail-closed artifact contract for weight edits.
Hugging Face Optimum, Intel Neural Compressor, GPTQModel Producing optimized or compressed model artifacts. InvarLock validates the produced artifact after the edit.
  1. Use compression or edit tooling to produce the subject checkpoint.
  2. Use lm-eval, LightEval, or a custom eval stack for broad downstream benchmark confidence.
  3. Use an MLOps platform for tracking, monitoring, and dashboards.
  4. Use InvarLock in the strict verification workflow to produce a machine-verifiable report that says a specific edited checkpoint stayed within configured regression and guard thresholds relative to a fixed baseline.
  5. Export the InvarLock result into existing workflow surfaces with invarlock report export --format mlflow-tags, --format model-card-md, or --format release-review-md.