`_is_bge_m3` keys off `sparse_linear.pt` -- the definitive signal for a
multi-vector embedder, since plain dense models do not ship one. It asked
`_resolve_repo_file`, which answers from the local disk and NEVER the
network. Right for "give me the path", wrong for "does this exist", and
the difference is invisible until the model has never been downloaded.

On a rented pod it has never been downloaded. The pod is disposable and
its cache starts empty, so the check ran before anything could satisfy
it: on every pod, every time, bge-m3 loaded through the DENSE
sentence-transformers path. The request for sparse was then refused with
a 400 telling the caller to serve the model on the coderai-embeddings
image -- which is the image it was already running on. Measured end to
end on a real A40: 379 seconds, then that.

`_repo_has_file` keeps the local answer first and, only when the file is
not on disk, asks the Hub for the repo's file LIST: metadata, no
download, cached once per process. Offline installs short-circuit on
HF_HUB_OFFLINE, a local directory is taken as the whole answer (asking
the Hub about a filesystem path would look up a stranger's model of the
same name), and any failure answers False exactly as before -- a metadata
lookup must never be able to fail a load.

Needs a coderai-embeddings rebuild: this code runs on the pod.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01HXRMe9ZPUQCCCtBai9uqt3