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.

scripts/frank-primary.env

@@ -43,19 +43,23 @@ SPEC_DRAFT_N_MAX=2
 # RAM (computed there — the x4-pair measurement: streaming over PCIe loses,
 # computing in RAM does not). OT_OVERRIDES is comma-separated
 # <tensor-pattern>=<backend> pairs passed as repeated --override-tensor
-# flags:
-#   * exps=CPU — the routed experts. ~100 GiB at Q8, read once per token;
-#     the CPU backend computes them from page cache.
-#   * the n-gram embedding block (the 51B-param PLE table) — sparse row
-#     lookups, tiny PCIe traffic from CPU RAM; NEVER pin with a GPU target
-#     (the 23 -> 13.4 t/s regression).
-#   * per_layer_token_embd.weight=CPU — REQUIRED by the shared MTP head's
-#     borrow path: GPU-resident, it crashes the draft load with
-#     "invalid vector subscript" (llama.cpp PR #28243).
-# The exact tensor pattern for the n-gram block was discovered at load time
-# on 2026-09-26 (see the runbook); this list is the deployed set.
+# flags. Both overrides are pinned by the header dump of parts 2-3
+# (2026-09-26, /home/dank/models/dump-gguf.py — GGUF v3, 512 experts
+# top-10, qwen4exp.ple.ngram_size=3, heads_per_ngram=8):
+#   * exps=CPU — the routed experts (blk.*.ffn_*_exps.weight, ~100 GiB at
+#     Q8_0), read once per token; the CPU backend computes them from page
+#     cache.
+#   * per_layer_token_embd.weight=CPU — the 51.2B-element PLE n-gram table
+#     (dims [160, 320001536], Q8_0, 54.4 GiB — the whole of part 3):
+#     sparse row lookups, tiny PCIe traffic from CPU RAM. It doubles as
+#     the shared MTP head's borrow tensor — GPU-resident, the draft load
+#     crashes with "invalid vector subscript" (llama.cpp PR #28243) — so
+#     this override is REQUIRED, not just a placement choice. NEVER give
+#     it a GPU target (the 23 -> 13.4 t/s regression) and never --no-mmap
+#     (the table must stay page-cache-backed; anonymous memory
+#     cgroup-kills silently).
 NGL=999
-OT_OVERRIDES=exps=CPU,ngram_embeddings=CPU,per_layer_token_embd.weight=CPU
+OT_OVERRIDES=exps=CPU,per_layer_token_embd.weight=CPU
 TENSOR_SPLIT=1,1
 
 # The drafter lives on the first R9700 (the card the load log shows holding