muse-spark-1.2-contributor is heavily discounted BECAUSE Meta uses your
prompts and completions to train future models. Selecting it for the price
without realising the data trade-off is a footgun.
Add hermes_cli/model_data_policy_guard.py (mirrors model_cost_guard):
data_training_warning(model_id, provider, base_url) -> DataTrainingWarning|None,
driven by a vendor-agnostic rule table. The status is not machine-readable on
/v1/models or models.dev, so the v1 rule keys on the documented '-contributor'
model id (fires regardless of provider, so it also covers custom/gateway
routes). Message mirrors Meta's pricing-doc language and figures
(https://dev.meta.ai/docs/pricing-rate-limits/).
Wire it into the CLI model picker's confirm flow (auth.py) as a [y/N]
disclosure, chained after the expensive-model cost guard. Fires only on the
contributor tier; silent on muse-spark-1.1/1.2 and all other models.
Teknium