fox asked: JIT-trained, gossip-pulled, or repo-embedded?
Added §2.4 — three shapes, two dead ends:
- JIT-trained per deployment: ❌ — GPU-hours over MNLI/SNLI/ANLI, not
a per-deployment step; non-deterministic across runs → different
audit_mode → breaks the verifier's reproducibility invariant.
"Training" is a one-time act by someone; the distributed thing is
the resulting fixed blob, not the recipe.
- Embedded in the git repo as data: ❌ — a few-hundred-MB binary in
.git blows the standing "fresh clone = python3.12 + venv + sqlite3"
invariant; Git LFS adds a dep and still bloats; the repo is source
+ tiny fixtures + docs, not a model registry.
- One-time obtained, content-addressed, fetched on demand, mesh-
distributable: ✓ — the repo carries a tiny manifest
(arborist/qa/nli/manifest.json: {checkpoint_sha256, source_url,
license, nli_model_version}); the weights live under
~/.arborist/models/nli/<hash>/, fetched on first use (make
fetch-nli or auto-fetch on first --enable-nli query) with the
sha256 verified fail-closed against the manifest; nli_model_version
folds into governance_policy_hash (a swap stales the cache, like
chunking_version / canonicalization_version), keeping audit_mode a
deterministic function of (answer, source, policy, pinned hash);
and once arborist's mesh grows blob-sync, a deployment with peers
pulls the checkpoint from a peer (AXFR-style, like ClouDNS slaves
pulling a zone, like a shard rehydrating from snapshot.db) instead
of the origin URL — content-addressing makes peer-pull and
origin-pull interchangeable, the mesh is the optimization not the
canonical source, cold start falls back to origin. Source: a
published off-the-shelf NLI checkpoint (MIT/Apache, recorded) or a
one-time fox/blackops-trained-and-published one.
Bottom line: not JIT-trained, not repo-embedded weights — a
content-addressed hash-pinned checkpoint, manifest in the repo,
weights under ~/.arborist/, fetched on demand (origin or peer), hash
in governance_policy_hash. Precedents cited: the textbook manifest
(pointers + licenses not texts), the [vec] extra (fastembed's
bge-small fetched not committed — #000039), ~/.arborist/ operator-
state, docs/mesh.md + snapshot rehydrate. All presupposes the §2.2/§5
discipline call came back "yes" (is a fixed NLI model allowed in the
audit_mode path?); if "no", none of it builds. Also wired §2.2 con
(b)/(c), §3, §5, §6 to point at §2.4. Doc-only.
russell@unturf.com
e391f1f01c