Open Model Lab
A public decision log for scope, naming, eval-first sequencing, and claim boundaries.
Decision: Use Open Model Research Harness for the project and Open Model Lab for the public section.
Rationale: The project borrows from rigorous research workflow patterns, but the public name should not imply that this is itself a frontier AI lab.
Consequence: Public routes, tags, titles, and navigation should use open-model language, not frontier-lab branding.
Decision: The first month focuses on eval infrastructure before SFT, DPO, agents, or safety work.
Rationale: Without reproducible evals, every later model change is anecdotal.
Consequence: July is successful only when multiple models can be compared on the same tasks with score, cost, latency, and failure-mode reporting.
Decision: The site will not publish a leaderboard without real, reproducible runs.
Rationale: The project is a research-engineering portfolio, not benchmark marketing.
Consequence: Dashboards and reports remain planned/empty until configs, runs, and reports exist.
Decision: Every report must state what it does and does not claim.
Rationale: Small experiments are valuable, but overclaiming makes them less credible.
Consequence: All planned report pages include a claim-boundary section.
Decision: The public site should be static/content-driven unless actual run data requires otherwise.
Rationale: The site should be easy to maintain and hard to break.
Consequence: Initial pages are generated from structured content, not a database.