- Register meta-ai as a live-first picker provider so the /v1/models catalog
leads the picker; new models appear without a PR
- Override fetch_models to exclude non-chat models (muse-image-*, muse-voice-*)
from the picker; new chat model families pass through automatically
- Slim fallback_models to a single safety-net entry (muse-spark-1.2), shown
only when the live fetch fails
- Make data-policy contributor warning model-generic (not hardcoded to 1.2)
so it covers any future -contributor model
- Update test assertion to match generic warning text
LOCAL ONLY — pre-launch, not for push.
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.
