frank-primary: pin the -ot override set to the real tensor names

Discovery, not guesswork: dump the GGUF v3 headers of the fetched parts
(scripts/dump-gguf.py, added — a dependency-free reader that never
touches tensor data, so a 50 GiB part scans in milliseconds) before
the first load.

What the dump says (parts 2-3, 2026-09-26):

- The 51B-param PLE n-gram table is ONE tensor: per_layer_token_embd.
  weight, dims [160, 320001536], Q8_0, 54.4 GiB — exactly the whole of
  part 3. It is the same tensor the shared MTP head borrows, so the
  =CPU override is required for placement AND for the draft load
  (GPU-resident borrow: "invalid vector subscript").
- The model is qwen4exp, 512 experts, top-10 routing
  (qwen4exp.ple.ngram_size=3, heads_per_ngram=8).
- Part 1 is metadata-only (ntensors=0); parts 2-6 carry tensors.

The placeholder pattern ngram_embeddings matched nothing — dropped from
OT_OVERRIDES (env, serve-script default, seam, AGENTS.md, runbook,
LLM index) before it could masquerade as a working override at load
time. Deployed set: exps=CPU,per_layer_token_embd.weight=CPU.

Suite: 346 green.

tests/frank_primary_unit.bats

@@ -59,12 +59,12 @@ REMOTE_DEPLOY="$REPO_ROOT/scripts/deploy-remote.sh"
     # computing them in RAM does not care (20+ tok/s on the same model
     # shape). exps=CPU plus the n-gram embedding table on CPU; the PLE
     # table must NEVER carry a GPU target (the 23 -> 13.4 t/s regression).
-    grep -q '^OT_OVERRIDES=exps=CPU,ngram_embeddings=CPU,per_layer_token_embd.weight=CPU$' "$ENV_FILE"
     grep -q -- '--override-tensor "$ot"' "$SERVE"
     # ...and mmap must stay on: --no-mmap forces the 51B-param table into
     # anonymous memory and the cgroup kill is silent.
     ! grep -q -- '--no-mmap' "$SERVE"
     ! grep -q '^MMPROJ_ON_GPU' "$ENV_FILE"
+    grep -q '^OT_OVERRIDES=exps=CPU,per_layer_token_embd.weight=CPU$' "$ENV_FILE"
 }
 
 @test "the projector is explicit, never autoloaded" {