Welcome to InvarLock

Post: What InvarLock is, what it checks, and how to try it.

Highlights

• Evaluate edited weights against a baseline with paired metrics and confidence intervals.
• GuardChain checks for “unsafe to compare” measurement mismatches and quality drift.
• Evidence packs capture the artifacts you need to verify and share results.

If you edit model weights (quantization, pruning, fine-tuning, merges), you eventually hit the same question: did this change silently break anything that matters? “It loads” isn’t enough, and single-number metrics often miss the failure modes you’ll regret later.

InvarLock is designed for that moment. It produces an evaluation report that is both human-readable and machine-verifiable, so you can make upgrade decisions with evidence—not vibes.

Quickstart

Install InvarLock via pip:

pip install "invarlock[hf]"

Run your first evaluation:

INVARLOCK_ALLOW_NETWORK=1 invarlock evaluate \\
  --baseline gpt2 \\
  --subject gpt2 \\
  --adapter auto \\
  --profile dev

That produces an evaluation report and (optionally) an evidence pack you can archive, verify, and share.

What’s next

• Design-partner refinement of the private deployment review workflow
• Broader adapter and framework coverage
• Better “what changed?” analytics over time

To go deeper, start with the docs. For questions and feedback, email [email protected].

If your team wants to help shape the private deployment path, start with design partners.