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docs(agents): compress Key Lessons to one law per bullet, correct the FA-quants law

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Key Lessons Learned 483 -> 340 lines. Every law survives; only the essays
went. Port-before-cut census over the diff: 454 deleted lines, 0 orphaned
facts (every number still has a holder in the vault).

- Each lesson is now one bullet with the page that owns the detail.
- De-duplicated the page's own internal repeats: the layer-split law was
  told twice (PCIe and ROCm sections), the FA-quants law twice more.
- CORRECTION, verified on dankclaw 2026-10-19: `GGML_CUDA_FA_ALL_QUANTS` is
  deprecated upstream (master's ggml/cmake/common.cmake tells you to use
  GGML_CUDA_FA_QUANTS=all), the successor's default pair list
  `q4_0-q4_0;q8_0-q8_0;f16-f16;bf16-bf16` compiles the quantized-KV kernels
  with the old flag OFF (master build-cuda cache: FA_ALL_QUANTS=OFF), and
  neither llama tree references the old macro under ggml/src/ggml-cuda/.
  The "required or prefill collapses ~12x" law is obsolete; the new law is
  to read the CMakeCache pair list.
- frank-zealot: ported the three facts AGENTS.md alone carried, and verified
  them against the host first — the 2026-10-18 13:16 cutover boot really did
  load PTQ1_0 at `new slot, n_ctx = 133888` x3 (journal), the mmproj fit
  check really printed 873.10 MiB against a 21,439 MiB projection, and the
  warmup spread is 2.6 -> 1,258 t/s prefill.
- New tooling: scripts/check-okf.py (OKF v0.1 conformance linter) and
  scripts/gen-vault-index.py (root index regenerated from frontmatter).

Co-Authored-By: omp <omq@openclaw.sh>
This commit is contained in:
Frank
2026-10-19 02:51:32 -07:00
co-authored by omp
parent a5fe8db877
commit 1ed7f0ddf2
4 changed files with 631 additions and 459 deletions
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serverAppConfigs-docs/LLM/frank-zealot.md
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@@ -98,7 +98,7 @@ re-run the same arms with the box quiet before a figure is carried anywhere else
| Window | Payload | Runtime | Shape | Replaced because | Rollback |
|---|---|---|---|---|---|
| 2026-10-18 13:19 → same evening (the boot `68afc6d` replaced) | the same 9B MTP file and projector | PrismML fork 10777 | 3 × 262,144 (pool 786,432), q8_0 K+V = 13,056 MiB, settled 17,956 / 6,169 MiB; 168.7 t/s decode (three runs 168.4–168.7), ~300 t/s over three streams (102.8 each), 2,877 t/s prefill at 106,418 | Frank ordered unquantized KV, and that pool cannot survive it: 24,576 MiB of f16 cache > 24,123 addressable | one env line — `CTX_SIZE=786432 PARALLEL=3 CACHE_TYPE_K/V=q8_0` — and redeploy; no payload move |
| 2026-10-18, 24 min (arm under the unit) | `Bonsai2-PTQ1_0/Ternary-Bonsai-2-27B-PTQ1_0.gguf` 5,946,648,928 B + hardlinked Q8 mmproj | PrismML fork 10777 | 2 × 200,704, batch/ubatch 3072/1536 | Frank scratched the 27B plan — ~48 t/s decode vs 76.6 for PQ2_0 on the same weights | `/home/dank/models/Bonsai2-PTQ1_0/` + `scripts/download-frank-zealot-bonsai2-ptq1.sh` |
| 2026-10-18 03:14 → 13:19 (arm under the unit, then the leftover drop-in) | `Bonsai2-PTQ1_0/Ternary-Bonsai-2-27B-PTQ1_0.gguf` 5,946,648,928 B + hardlinked Q8 mmproj | PrismML fork 10777 | arm: 2 × 200,704, batch/ubatch 3072/1536. The leftover `frank-zealot.service.d/ptq1.conf` then made the 13:16 cutover deploy boot THIS payload at 3 × 133,888 (`new slot, n_ctx = 133888` x3, journal read 2026-10-19) until the 13:19 boot | Frank scratched the 27B plan — ~48 t/s decode vs 76.6 for PQ2_0 on the same weights | `/home/dank/models/Bonsai2-PTQ1_0/` + `scripts/download-frank-zealot-bonsai2-ptq1.sh` |
| 2026-10-17 → 10-18 | Ternary-Bonsai-2-**27B** PQ2_0 7,206,168,928 B `3907dc16…` + Q8_0 mmproj 629,246,976 B | PrismML fork **10754** (`~/GitPackages/llama-prism/`) | 2 × 200,704 (pool 401,408), q8_0 K+V = 13,328 MiB, settled 22,664 / 1,461; 76.6 t/s decode, 109.4 aggregate over two streams, 691.7 t/s prefill at 166,027; MTP impossible — no head in the file | the 9B bills half the KV per token (8 full layers, not 16), which funded three slots at the native window and beat it on every rate | `/home/dank/models/Bonsai2-PQ2_0/` + the 10754 fork tree, `scripts/download-frank-zealot-bonsai2.sh`, revert the swap commit |
| 2026-10-07 → 10-17 | Qwen3.5-9B-Uncensored-HauhauCS-Aggressive Q6_K 7,359,259,008 B `c0ba7beb…` + BF16 mmproj 921,704,448 B | llama.cpp master `5fc4f3c` CUDA sm_86 (`~/GitPackages/llama-master/build-cuda/`) | 2 × 200,704, exact f16 K+V, MTP off (no `nextn` tensors) | the ternary 27B bought a larger model class at the same window | `/home/dank/models/Qwen35-9B-HauhauCS-Q6K/` + the master build, `scripts/download-frank-zealot.sh` |
| 2026-10-07 (same day) | Gemma4-26B-A4B-QAT Q4_K_M 16,796,015,520 B + BF16 mmproj 1,194,827,776 B + unused `gemma4-assistant` MTP 251,937,728 B | llama.cpp master CUDA | 1 × 200,704, f16 K + q8_0 V, ubatch 1024, MTP off; 21,868 / 2,257 settled, 139.9 t/s decode (the ~4 B-active MoE is at home here); MTP ON would cost ~1.9 GiB and boot at 319 MiB free | ONE slot serialized compaction; the 9B's lighter weights bought two slots and exact KV | `/home/dank/models/Gemma4-26B-A4B/` (ungated — plain `curl -fL`) |
@@ -129,10 +129,12 @@ re-run the same arms with the box quiet before a figure is carried anywhere else
3 cells, and an armed depth-2 drafter triples it to 452.25), compute buffers at graph capture, the mmproj after the
fit. On gemma4-class hybrids `attention_k_eq_v` does **not** shrink the allocation — the cache reserves the K/V pair
twice, 6.125 B/elem at f16 K + q8_0 V.
- **`--flash-attn on` is pinned, not decorative**: a quantized V exists only on the FA path and the engine falls back silently without it. llama.cpp has **no true fp8 KV dtype** — q8_0 is the 8-bit class (1.0625 B/elem, ~8.5 effective bits). `GGML_CUDA_FA_ALL_QUANTS` is vestigial and build-specific: the PrismML fork carries it ON (since 2026-09-17) while the master build had it OFF and still dispatched Q4/Q5 KV unconditionally (`ggml-cuda/fattn.cu` has no guard), so quantized KV never required it.
- **`--flash-attn on` is pinned, not decorative**: a quantized V exists only on the FA path and the engine falls back silently without it. llama.cpp has **no true fp8 KV dtype** — q8_0 is the 8-bit class (1.0625 B/elem, ~8.5 effective bits). `GGML_CUDA_FA_ALL_QUANTS` is **deprecated upstream and inert in both trees on this host** (no reference anywhere in `ggml/src/ggml-cuda/`, read 2026-10-19): master's `ggml/cmake/common.cmake` warns `use GGML_CUDA_FA_QUANTS=all instead`, and the successor's default — `GGML_CUDA_FA_QUANTS="q4_0-q4_0;q8_0-q8_0;f16-f16;bf16-bf16"` (`ggml/CMakeLists.txt:208`) — already compiles the quantized-KV kernels with the old flag OFF (master's `build-cuda` cache says `FA_ALL_QUANTS:BOOL=OFF` and it dispatched q8_0/q4_0 KV regardless). Prove a build's quantized-KV ability from its `CMakeCache.txt` `GGML_CUDA_FA_QUANTS` line, never from the retired flag or the build command you remember.
- **Prices come from boots, not formulas, and an arm belongs to the payload it was measured on**: 3 × 180,224 +
q8_0 V fits with Q5_K_M (21,684 used / 2,441 free) and does **not** fit with Q6_K inside the 2–3 GiB band. The
mmproj worst case (~870 MiB) is printed before fitting but never billed by the engine's own fit check. Only
q8_0 V fits with Q5_K_M (21,684 used / 2,441 free) and does **not** fit with Q6_K inside the 2–3 GiB band.
The mmproj worst case is printed before fitting (`estimated worst-case memory usage of mmproj is
873.10 MiB` on the Bonsai-2 27B boot) but never billed by the engine's own fit check: that boot
projected **21,439 MiB** and settled at **22,664**. Only
`tests/frank_zealot_unit.bats`'s fit gate can see a boot that would die at `cudaMalloc`, so every retune keeps it
green — and the first verbosity-4 ledger plus settled `nvidia-smi` of a new dtype belongs on this page, because
projections drift.
@@ -179,7 +181,10 @@ re-run the same arms with the box quiet before a figure is carried anywhere else
`predicted_per_second`). Keep `VERBOSITY=4` — 3 is the engine default and cannot prove the KV or draft sizes,
while levels above 4 flood the journal.
- llama.cpp does **not** validate the requested model id: it answers and echoes the served `ALIAS`, so a rename breaks id-keyed consumers (the OpenWebUI `model` row, its `access_grant`, `openai.api_configs`) and nothing server-side. Alias history: `frank-small` → `frank-secondary` (2026-10-06) → `frank-zealot` (`7c77326`, 2026-10-09); the service and unit took the name 2026-10-10 (`55f87fc`).
- The first request after a boot is a warmup artifact (37 t/s class) and is never a measurement; never time a wall-clock aggregate on a generation that stops on its own (use `ignore_eos`).
- The first request after a boot is a warmup artifact, never a measurement: on the Bonsai-2 27B
boot request #1 gave 2.6 t/s prefill / 37.7 t/s decode and request #2 gave 1,258 t/s prefill /
54.7 t/s per stream (two concurrent, the 109.4 aggregate in the Eras row) — a 3x spread on one
shape. Never time a wall-clock aggregate on a generation that stops on its own (use `ignore_eos`).
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