docs(openwebui): frank-dragoon enabled in the picker beside frank-zealot

Live DB trio applied on Dankclaw (enable idx 0, model row + global
access_grant, restart); verified /api/models as admin and role=user,
chat answers pong. Battery rewritten to gate on /api/models —
/api/v1/models/list lists only workspace models on 0.6.x.

AGENTS.md

@@ -12,8 +12,9 @@ merge absorbed the former `vllmScripts` repo, history preserved).
   Calibre-Web, Jellyfin, Plex) plus the `frank-primary` LLM service.
 - **Dankclaw** (192.168.0.143) — AI/search/dev/observability stack (SearXNG
   via Gluetun, ComfyUI, OpenWebUI, Hermes, Gitea, Grafana, Prometheus, Loki,
-  Alloy, Pi-hole) plus TWO LLM services: `frank-large` (halogen container on
-  the gfx1151 iGPU) and `frank-small` (llama.cpp CUDA on the RTX 3090). Caddy on
+  Alloy, Pi-hole) plus TWO LLM services: `frank-large` (flash-server
+  container on the gfx1151 iGPU) and `frank-secondary` (llama.cpp CUDA on the
+  RTX 3090). Caddy on
   Dankclaw reverse-proxies both hosts.
 - **FrankBook-Pro** (192.168.0.112) — macOS mini running the *arr stack
   (Prowlarr, Sonarr, Radarr, Lidarr, Readarr, qBittorrent) as quadlets inside
@@ -24,9 +25,9 @@ merge absorbed the former `vllmScripts` repo, history preserved).
 
 | Target | Service | Runtime | Model | Port |
 |--------|---------|---------|-------|------|
-| Dankserver | `frank-primary` | **FreeToken PR #587** (branch `rocm714_torch214`, head `77a7201`; local image `frank-primary-freetoken:pr587-77a7201` built on the host from `scripts/docker/frank-primary-freetoken.Dockerfile` — digest-pinned rocm/pytorch rocm7.14 torch-2.11.0 base, plain `podman run`, root systemd unit) | `orcarouter/Qwen3.8-Flash-Next-Uncensored-FP8` (~172.8 GiB, 131 shards, modelopt block-FP8, loaded straight from safetensors), **ONE R9700 (second card idle)**, all experts in **pinned host RAM** (~120 GiB) behind a GPU LRU (`--moe-cache-rate 0.10`), PLE table 47.7 GiB pinned (root unit + `LimitMEMLOCK=infinity`), **KV pool `--num-tokens 299968`** (24 KiB/token, multiple-of-64 law of the `qsa_sparse` auto backend), 2 slots, `--max-output-tokens 36864`, **parsers AUTO** (thinking → `reasoning_content`, tools qwen3.8 dialect), vision tower host-streamed. Measured: boot ~2.3 min cold / 92 s warm, decode **17.1 tok/s** single-stream; **TP=2 does not boot on ROCm** (pynccl JIT); `/metrics` 404 (Prometheus scrape reads down). Deploy gate reads the `/health` BODY (`status:"ok"`) — FreeToken 200s while loading. Rollback tag `frank-primary-vllm-shapeG-20261003` |7770|
-| Dankclaw | `frank-large` | **halogen container** (`ghcr.io/peonist-ai/halogen@sha256:dc0a39a0…`, digest-pinned, plain `podman run` via `scripts/serve-frank-large.sh`, root systemd unit) | `peonist-ai/halogen-qwen3.8-27b` **`qwen3.8-27b-p1w4d-d2.hgn`** (35,865,565,184 B; the `.hgn` format loads in NO other engine — llama.cpp, vLLM and transformers all refuse it), **DFlash2 spec decode (`HALOGEN_DRAFTER=2`)**, **1 slot × 200,700 ctx** — `HALOGEN_KV_SLOTS=1` is load-bearing: batching XOR speculation, slots>1 silently disables the drafter (measured: 8 slots at serial ~6 t/s/stream), d2 KV (2× fp8, 64 KiB/token → 12.2 GiB pool, engine sizes it and prints a boot ledger naming any clamp), **TEXT-ONLY** (the engine ignores the vision encoder — vision is frank-small's job), **censored base** (the ROCmFPX AGENT rollback is the refusal escape hatch), **no server-side sampler: absent temperature is GREEDY**, so the card's thinking recipe (temp 1.0 / top_p 0.95) rides the wire in `models.yml`. Publisher card 31.7 t/s mean across eight text workloads vs the retired ROCmFPX endpoint's measured 27.6–28.1. Prompt over the slot AND `max_tokens` over `HALOGEN_MAX_TOKENS_CAP` are both 400s naming the limit, never truncations. Serve passes NO `--ulimit nofile` (rootless runc cannot raise Dankclaw's 524288 hard limit — see Key Lessons). Cutover 2026-10-05|7770|
-| Dankclaw | `frank-small` | llama.cpp master, CUDA sm_86 build (root systemd unit, relinked) | bartowski/MiMo-V2.6-Distill-Qwen-9B-GGUF **Q6_K** (9B dense qwen3_5 hybrid, 7.26 GiB GGUF, no MTP weights so no spec decode) + the repo's f16 mmproj for **vision**, on the RTX 3090, **2 × 200,704** (pool 401,408 — NOT the native window) with **fully unquantized KV (f16 K + f16 V)** under flash attention, **batch 4096 / ubatch 2048** (doubled 2026-10-05: measured +4.6 % prefill at 8,928 tokens for 616 MiB), 21,846 used / **2,279 free** of 24,576 MiB, 94.4 tok/s decode. Resized 2026-10-03 (3 × 180,224 + q8_0 V → 2 × 200,704 + f16 V → + Q6_K rung: the freed slots paid for exact values AND the higher quant; the rung costs a measured −10 % decode — weight-bandwidth-bound); rollback `MODEL_FILE=…/MiMo-Q5KM/…Q5_K_M.gguf` (buys the 10 % back) or `CTX_SIZE=540672 PARALLEL=3 CACHE_TYPE_V=q8_0` (Q5 only) | 7771 |
+| Dankserver | `frank-primary` | **radiance** (StillDeadcode, **pin 1.0.13** digest-pinned `stilldeadcode/radiance@sha256:4c3f484564…`, plain `podman run`, root systemd unit) | ukisai/Swift1.5-Qwen3.8-Flash-Next BF16 (335.3 GiB, 397/397 sha256-verified) converted by `scripts/convert-swift15-w4dn8.sh` to `qwen4exp-swift15-w4dn8-i8-hc8m.rad` (**116,416 MiB**): **all 49,152 experts GPTQ w4** (10M-token calibration on engine 1.2.0) + **24 whole bf16-protected experts/layer** (energy-share law), int8 trunk/lm_head, fp8 HC, PLE n-gram fp8 fixed-scale, on **BOTH R9700s TP=2** (`--tp-wire wht6`), experts in a **40 GiB/rank pinned host pool** + VRAM cache (`--placement expert_tiered`, explicit `--vram-weights-mib 26071 --vram-kv-mib 3543`), **47.7 GiB n-gram copy pinned to host RAM** (`--ngram-placement ram`; root unit + `LimitMEMLOCK=infinity`), **fp8 KV** (inherited; pool 3.46 GiB), **MTP spec decode depth 2**, `--max-num-seqs 3`, `--max-model-len 200000`, MNBT 2048, Froggeric v22.5 template. KLD-gated (mean KL 0.0131, top-1 97.92 % vs BF16); decode ~190 t/s live (gate bar 30.8). Cutover 2026-10-08 from fp8-native (payload on disk = rollback). | 7770 |
+| Dankclaw | `frank-large` | **halogen-flash-server 0.16.2** (`ghcr.io/peonist-ai/halogen-flash-server@sha256:0c61bf84…`, digest-pinned, plain `podman run` via `scripts/serve-frank-large.sh`, root systemd unit) | Quat3rnion/halogen-swift1.5-qwen3.8-flash-next-v2-abliterated **`qwen38-flash-next-v2-swift15-abliterated.hgn`** (66,687,678,464 B = compacted r2 of the card's 89,775,868,032 B original, ~62 GiB resident; the v2 `.hgn` format loads in NO other engine), **+ REQUIRED `qwen38-flash-next-ngram.hgn`** (51,200,246,144 B, peonist @5cc17cea — read beside the checkpoint) **+ vision tower** (897,916,416 B, @e053f488 — **vision ON**, `image_url` parts only, ≥256 px probes), **MTP spec decode ALWAYS-ON (`HALOGEN_SPEC_ADAPT=0` — the adaptive default disables the drafter per request and HALVES decode)**, MTP head INSIDE the checkpoint (`HALOGEN_MTP_HEAD` unset; no sidecar fetched), **2 slots × 262,144 ctx** (KV pool 524,288 positions ≈ 14.0 GiB at ~28 KiB/position; the 27B slots>1-disables-drafting law does NOT carry over — batching and speculation coexist, the card's quickstart ships 2 with the head), harness window 217,088 = ceiling − 45k estimate headroom, **server-side sampler 1.0/0.95/20** (card recipe; models.yml extraBody matches), coupled cap/timeout 65536/7200, prompt cache ON by default (warm 100k turn ≈ 2 s vs ≈ 88 s cold)|7770|
+| Dankclaw | `frank-secondary` | llama.cpp master (0.5.0-dev, commit `5fc4f3c`), CUDA sm_86 build (root systemd unit, relinked) | HauhauCS/Qwen3.5-9B-Uncensored-HauhauCS-Aggressive **Q6_K** (7,359,259,008 B; dense `qwen35` hybrid: 32 layers, 8 full-attention + 24 Gated DeltaNet, 4 KV heads × 256 dim, vocab 248,320, native 262,144, **no MTP tensors in the file**) + the publisher's own **BF16 mmproj** (921,704,448 B) for **vision**, on the RTX 3090, **TWO 200,704-token slots** (`--ctx-size 401408 --parallel 2`), **exact f16 K + f16 V** under flash attention (32 KiB/token over the 8 full layers = 12.25 GiB pool), batch 4096 / **ubatch 2048** (measured CUDA optimum), `--log-verbosity 4`, **MTP OFF** (forced: no `nextn` tensors exist). Priced from the MiMo Q6_K boots (same geometry, same card, same shape): 21,276/2,849 at ubatch 1024, 21,846/2,279 at 2048. Cutover 2026-10-07 (late) from Gemma4-26B-A4B Q4_K_M 1×200,704. All three endpoints route harness **compaction** here. | 7771 |
 
 Retired endpoints (`frank-npu`, and every prior runtime) are in
 the [runbook index](#runbook-index). The full swap history lives in
@@ -72,7 +73,7 @@ All edits flow through git. Never edit files on a remote machine directly.
 The planner maps changed paths to services; the remote runner restarts only
 the selected service and records its revision outside the checkout.
 Documentation-only changes are a no-op. LLM services are selected only by
-their own files — every endpoint (`frank-large`/`frank-small`/`frank-primary`)
+their own files — every endpoint (`frank-large`/`frank-secondary`/`frank-primary`)
 by its unit/env/serve trio in `scripts/` — so
 unrelated
 container changes cannot interrupt inference. Set `FRANK_SUDO_PW` for headless
@@ -228,13 +229,16 @@ archive.
 **STOP the production service BEFORE benchmarking** — these servers serve
 real users, and a bench beside a live service starves the GPU and corrupts
 results. Use the runtime's own harness: `llama-bench` for the llama.cpp
-endpoints; a live llama.cpp server also reports honest per-request numbers in the `/completion` `timings` object (prefill/decode rates, `draft_n`, `draft_n_accepted`), which is how frank-large and frank-small were measured. Rolling your own bench
+endpoints; a live llama.cpp server also reports honest per-request numbers in the `/completion` `timings` object (prefill/decode rates, `draft_n`, `draft_n_accepted`), which is how frank-large and frank-secondary were measured. Rolling your own bench
 requires asking first.
 Scratch ports are not used on LLM hosts — a
 test process cannot share the single GPU anyway; validate against the service
 itself.
 
-(A gufo-era law; frank-large ran ROCmFPX llama.cpp 2026-10-02 → 10-05 and is the halogen container since, but the rollback tags keep it live.) **`gufo bench` cannot rank speculative-decode settings, and will invert the ranking.**
+(A gufo-era law; frank-large ran ROCmFPX llama.cpp 2026-10-02 → 10-05, the
+halogen 27B container 2026-10-05 → 10-06, and is flash-server since, but the
+rollback tags keep it live.) **`gufo bench` cannot rank speculative-decode
+settings, and will invert the ranking.**
 Measured 2026-09-28 on frank-large at depth 32768: its corpus
 reports `acceptance=0.933 ± 0.000` with an **identical trace sha256** across
 draft widths, i.e. the same highly-predictable text every run, and 93 % of
@@ -262,9 +266,9 @@ merely wedged kernel usually still answers ARP.
 1. Stop and mask (self-healing would restart it):
    - Dankclaw `frank-large` (root unit, halogen podman on the iGPU):
      `sudo systemctl stop frank-large && sudo systemctl mask frank-large`
-   - Dankclaw `frank-small` (root unit, llama.cpp on the 3090):
-     `sudo systemctl stop frank-small && sudo systemctl mask frank-small`
-   - Dankserver `frank-primary` (root unit, FreeToken on one R9700):
+   - Dankclaw `frank-secondary` (root unit, llama.cpp on the 3090):
+     `sudo systemctl stop frank-secondary && sudo systemctl mask frank-secondary`
+  - Dankserver `frank-primary` (root unit, radiance on both R9700s, TP=2):
      `sudo systemctl stop frank-primary && sudo systemctl mask frank-primary`
 2. Bench.
    NOTE: `systemctl mask` FAILS on a relinked unit (the unit file in
@@ -282,9 +286,17 @@ deletes the unit file from `/etc/systemd/system/`, not just the symlink.
 The harness reaches the fleet through `.omp/agent/models.yml` in the
 operator's home — `/home/blank/.omp/agent/models.yml` on the dev box, which is
 where the harness runs; there is no copy on the servers. Each provider is one
-HOST:PORT; each model `id` is the served alias: `frank-primary` →
-`192.168.0.235:7770`, `frank-large` → `192.168.0.143:7770`, `frank-small` →
-`modelRoles`.
+HOST:PORT; each model `id` is the served alias. **Official model ids are
+the StarCraft unit names (2026-10-08) and since 2026-10-09 they are also the
+ids on the wire:** `frank-archon` → `192.168.0.235:7770`
+(`FRANK_PRIMARY_SERVED_NAME`), `frank-dragoon` → `192.168.0.143:7770`
+(`HALOGEN_MODEL_ID`), `frank-zealot` → `192.168.0.143:7771` (`ALIAS`). The
+SERVICES, units, scripts, env files and runbooks keep the old names
+(`frank-primary`, `frank-large`, `frank-secondary`); only the served alias,
+the `models.yml`/`config.yml` ids, the homepage cards, the OpenWebUI grants
+and the Hermes routing were renamed. Verify a rename on `/v1/models`, never
+on a chat response: radiance echoes the **requested** model name, so a 200
+proves nothing about the served id (llama.cpp and halogen ignore it).
 
 Resolution rules: the default model resolves as a `defaultProvider` +
 `defaultModel` pair (the provider must serve the model); a subagent override
@@ -296,17 +308,21 @@ When renaming a service, retiring an endpoint, or moving a model: update the
 provider block, model `id`, `contextWindow`, `maxTokens`, `input` in
 `models.yml`, then fix every `modelRoles` entry that named the old endpoint —
 a role pointing at a provider the file does not define fails at call time. As
-of 2026-10-05 **two of the three endpoints accept images** (`input: [text,
-image]`) — `frank-large` went TEXT-ONLY with the halogen swap (the engine
-ignores the vision encoder; the BF16-mmproj era ended 2026-10-05) — while
-`frank-primary` takes them natively (FreeToken builds the qwen3.8 vision
-tower and streams its block weights from host RAM two at a time by default),
-and `frank-small` through
-`mmproj-MiMo-V2.6-Distill-Qwen-9B-f16.gguf` passed as `--mmproj` — the MiMo
-GGUF itself carries no vision tensors. `frank-small`'s `contextWindow` is
-**200704** (resliced from 180224 on 2026-10-03), the per-slot window, not the
-model's native 262144: the harness must compact before llama.cpp answers a
-longer prompt with a 400. Note also
+of 2026-10-06 **all three endpoints accept images** (`input: [text,
+image]`) — `frank-large` returned to vision with the flash-server v2 swap
+(the v2 vision tower loads beside the checkpoint; the text-only halogen
+27B window closed 2026-10-06) — while
+`frank-primary` takes them natively (radiance runs the checkpoint's
+27-layer Qwen-VL tower — patching, pad-token expansion and 3-part rotary
+are the engine core; `--mm-max-patches` available),
+and `frank-secondary` through
+the publisher's own BF16 mmproj passed as `--mmproj` — the model is natively
+multimodal but the Q6_K GGUF carries no vision tensors (same arrangement the
+Gemma4 era used). `frank-secondary`'s `contextWindow` is
+**200704** — the PER-SLOT window (`--ctx-size 401408 --parallel 2` since the
+2026-10-07 Qwen3.5-9B swap), not the pool and not the model's native 262144:
+the harness must compact before llama.cpp answers a longer prompt with a 400.
+Note also
 that
 the reasoning channel differs by runtime: llama.cpp AND SGLang (qwen3
 parser, live-verified) emit `reasoning_content`, vLLM emits `reasoning` —
@@ -315,195 +331,248 @@ There is **no audio**
 input anywhere in the fleet: that capability left with the FLM NPU
 endpoint on 2026-09-11, returned 2026-09-17 with the Gemma4 projector's
 audio encoder, and left again with the Gemma4 retirement on 2026-10-01
-(the MiMo tower is vision-only).
+(neither the MiMo nor the Qwen3.5 tower carries audio).
 
-### frank-large — halogen container, Qwen3.8-27B + DFlash2 (Dankclaw, ACTIVE)
+### frank-large — halogen-flash-server 0.16.2, Qwen3.8-Flash-Next v2 Swift1.5 ABLITERATED (Dankclaw, ACTIVE)
 
-The **halogen engine** (`ghcr.io/peonist-ai/halogen@sha256:dc0a39a0…`,
-digest-pinned, published tag 0.1.4) as a podman container driven by
-`scripts/serve-frank-large.sh` under a **root systemd unit**
-(`scripts/frank-large.service`, `User=dank`, `Restart=always`, `ExecStop=podman
-stop -t 120 frank-large-halogen`, the script `exec podman wait` so the restart
-policy is real) on port 7770 from the Strix Halo iGPU. Engine stdout goes to
-`/home/dank/logs/frank_large.log` — **the boot ledger (KV pool, drafter state,
-any clamp) lives there, not in the journal**. Serving
-`peonist-ai/halogen-qwen3.8-27b`: `qwen3.8-27b-p1w4d-d2.hgn` (35,865,565,184 B
-≈ 33.4 GiB, sha256 `274c3fc7…`, 1352 tensors; p1 weights / w4 activations /
-d2 KV) + the flat `tokenizer/` directory (5 files). Cutover 2026-10-05 from the ROCmFPX llama.cpp fork
-([[frank-large-rocmfpx-q6agent-uncensored-mtp]], measured 27.6–28.1 t/s prose),
-chosen for the publisher's 31.7 t/s DFlash2 mean.
+The **flash-server engine** (`ghcr.io/peonist-ai/halogen-flash-server@sha256:0c61bf84…`,
+digest-pinned, published tag 0.16.2, 3.6 GB image) as a podman container
+driven by `scripts/serve-frank-large.sh` under a **root systemd unit**
+(`scripts/frank-large.service`, `User=dank`, `Restart=always`,
+`ExecStop=podman stop -t 60 frank-large-halogen`, the script runs `podman
+run` FOREGROUND — the container's exit code is the service's exit so
+`Restart=always` stays real, and the engine ledger lands on disk) on port
+7770 from the Strix Halo iGPU. Engine stdout goes to
+`/home/dank/logs/frank_large.log` — **the boot ledger (weights resident, KV
+pool, MTP head, prompt cache, any clamp) lives there, not in the journal**.
+Cutover 2026-10-06 from the halogen 27B-dense era
+([[frank-large-halogen27b-dflash2]], retired the same window).
 
-**The format is the lock.** `.hgn` loads in nothing else — the runtime is
-chosen by the FILE, as ROCmFPX's tensor types were before it. This is NOT the
-2026-09 `halogen-flash-server` image (different repo, Flash-Next w4b era); the
-halogen-server repo restarted versioning at 0.1.x.
+Serving `Quat3rnion/halogen-swift1.5-qwen3.8-flash-next-v2-abliterated`:
+`qwen38-flash-next-v2-swift15-abliterated.hgn` (66,687,678,464 B ≈ 62.1
+GiB, sha256 `9c880b8b…` — a compacted r2 of the card's 89,775,868,032 B
+append-only original; the abliteration workflow appends and the repo's
+tooling drops dead padding, live tensors byte-identical) + the **REQUIRED**
+n-gram lookup table `qwen38-flash-next-ngram.hgn` (51,200,246,144 B,
+peonist @5cc17cea) + the vision tower `qwen38-flash-next-vision.hgn`
+(897,916,416 B, @e053f488) + the flat `tokenizer/` directory (6 files,
+fetched fresh from peonist). Cutover 2026-10-06 from the halogen
+27B-dense shape, chosen by Frank for the ABLITERATED base.
 
-**Two product regressions, both accepted by Frank before deploy:** the
-endpoint is **text-only** (README honest limits: the vision encoder is
-ignored — frank-small owns images now) and the checkpoint is a finetune of the
-**censored** `Qwen/Qwen3.8-27B` base (refusals can return; the rollback is one
-commit-revert away with the AGENT payload still on disk).
+**The format is the lock.** The v2 `.hgn` loads ONLY in the
+flash-server line — the 27B-era halogen 0.1.4 container refuses it, so the
+engine moved with the model. The MTP head is INSIDE the checkpoint
+(`HALOGEN_MTP_HEAD` stays unset; the separate `qwen38-flash-next-mtp.hgn`
+sidecar is deliberately not fetched).
 
-**Engine laws that shape the config** (all in `scripts/frank-large.env` with
-evidence): `HALOGEN_KV_SLOTS=1` — batch XOR speculation, slots>1 turns the
-drafter off silently. `HALOGEN_SLOT_CTX=200700` — the per-conversation HARD
-ceiling (400 naming the limit, never truncation); `models.yml`
-`contextWindow: 200700` must match or the harness discovers the wall as a 400.
-`HALOGEN_MAX_TOKENS_CAP=65536` + `HALOGEN_QUEUE_TIMEOUT=7200` are a coupled
-pair; the harness `maxTokens: 57344` stays under the cap (over-cap is a 400
-naming the limit — README, never a 413). Thinking tokens COUNT against the
-budget: exhausted mid-thought is `finish_reason: "length"` with empty
-`content` and the reasoning in `reasoning_content`.
-`HALOGEN_DRAFTER=2` = DFlash2. **No server-side sampler**: an absent
-temperature is greedy, so the thinking recipe exists only in `models.yml`
-`extraBody`. `response_format` is unsupported (no constrained decoding).
-Thinking lands in **`reasoning_content`** (harness field unchanged); the same
-Qwen3.8 template rejects `reasoning_effort` outside `xhigh|medium|low`, so the
-`models.yml` fold stays mandatory.
+**Product facts vs the 27B era, both regressions FIXED:** the base is
+**abliterated** (the 27B was a censored-base finetune; refusals were an
+accepted trade there) and **vision is ON** (the 27B engine silently
+ignored its encoder — frank-secondary owned images for exactly one day at this
+endpoint).
 
-Fetch: `scripts/download-frank-large-halogen.sh` into
-`/home/dank/models/halogen-qwen3.8-27b/` (`-f` + byte count + LFS sha256 +
-`--verify-only`, no token on the command line; resume goes against the
-RESOLVED CDN URL because curl drops `Range` across HF's 302). The deployer
-and serve script both refuse a missing tokenizer file or **any symlink** —
-an HF cache snapshot symlinks each entry into `blobs/` and those links
-dangle inside the container mount.
+**Engine laws that shape the config** (all in `scripts/frank-large.env`
+with evidence): `HALOGEN_SPEC_ADAPT=0` is MANDATORY — the default adaptive
+policy disables the MTP head for whole requests after low acceptance and
+HALVES decode; the head stays on and temp-1.0 traffic pays some acceptance
+for it (accepted). `HALOGEN_CTX=262144` is the PER-SLOT hard ceiling (400
+naming the limit, never truncation) — RAISED from 200192 on 2026-10-07
+under the 400-zone law: the harness clamps max_tokens to
+`min(45056, contextWindow − est − 4096)`, its chars/4 est UNDER-counts
+the rendered prompt by ~3 % (measured: est 152,938 → server 157,388,
+request 43,158 vs room 42,804), so a harness window == engine ceiling
+made every request over ~146k est a guaranteed 400. `models.yml`
+`contextWindow: 217088` leaves the 45k gap that absorbs the estimate
+error (worst-case request ≈ 219k ≤ 262144); compaction fires at 85 % of
+the harness window = 184,524 est ≈ 190k measured (Frank's 180k target).
+`HALOGEN_KV_SLOTS=2` is LEGAL here — the 27B "slots>1 disables the drafter"
+law does NOT carry over; batching and speculation coexist (the card's
+quickstart ships 2 slots with the head), so 2 is concurrent capacity.
+`HALOGEN_KV_POOL_POSITIONS=524288` (explicit 2 × ctx; ~28 KiB/position ⇒
+14.0 GiB). `HALOGEN_MAX_TOK=8192` is only the per-request DEFAULT (the
+harness sends at most its `maxTokens` 45056); `HALOGEN_MAX_TOKENS_CAP=65536` +
+`HALOGEN_QUEUE_TIMEOUT=7200` are a coupled pair (FLAGS.md table).
+**A server-side sampler EXISTS** (`HALOGEN_TEMPERATURE/TOP_P/TOP_K` =
+1.0/0.95/20, the card's thinking recipe) — an absent temperature is no
+longer greedy; `models.yml` extraBody carries the same values, consistent
+not load-bearing. Prompt cache is ON by default (`PROMPT_CACHE=2`): a warm
+100k follow-up costs ~2 s vs ~88 s cold — the 27B engine's manual
+`HALOGEN_CACHE_MB` pin era is over. `response_format` is unsupported.
+Thinking lands in **`reasoning_content`**; the v2 chat template rejects
+`reasoning_effort` outside `xhigh|medium|low`, so the `models.yml` fold
+stays mandatory. Thinking tokens COUNT against the budget (empty content +
+`finish_reason: "length"` when exhausted mid-thought). Vision: OpenAI
+`image_url` content parts only; ≥256 px probe images or the model answers
+"video".
 
-Runbook: `serverAppConfigs-docs/LLM/frank-large-halogen27b-dflash2.md`.
-Rollback: revert the swap commit and redeploy — the ROCmFPX payload
-(`/home/dank/models/kingjones-Qwen3.8-27B-ROCmFP4-STRIX-MTP/`), the fork build
-and `scripts/build-rocmfpx.sh` (pin `36e9acd`) all remain on disk, seam-tested.
-The older gufo tag `frank-large-gufo-q4kxl-20261001` stays a **re-download**
-rollback (payloads removed 2026-10-03; `scripts/build-gufo.sh` stays).
+**Memory ledger** (121.2 GiB unified; the referee is the boot ledger):
+weights ~62 + KV 14.0 + arena ~8 (`3.8 + 8.4 × MAX_TOK/16384`) + vision
+~2 ≈ 86 GiB engine-side + `HALOGEN_HOST_RESERVE_GIB=20` → ~15 GiB
+margin over baseline co-tenancy. The 51.2 GB n-gram table rides as mmap'd
+page cache beside the checkpoint — the card's quickstart arithmetic does
+not bill it as resident, but READ the boot ledger and re-size if the
+engine prints a clamp.
 
-### frank-small — llama.cpp on the RTX 3090 (Dankclaw, ACTIVE)
+Fetch: `scripts/download-frank-large-flashnext.sh` into
+`/home/dank/models/halogen-flashnext-v2/` (`-f` + exact byte counts + LFS
+sha256 + `--verify-only`, no token on the command line; resume goes
+against the RESOLVED CDN URL because curl drops `Range` across HF's 302).
+The serve gates mirror the deployer EXACTLY (byte-exact checkpoint +
+n-gram + vision tower, flat tokenizer with all six files as regular
+files) — the 2026-10-01 crash-loop lesson.
 
-**llama.cpp master** (CUDA sm_86 build
-`/home/dank/GitPackages/llama-master/build-cuda/bin/llama-server`) as a
-**root systemd unit** (`scripts/frank-small.service`, `relink_unit`ed into
+Runbook:
+`serverAppConfigs-docs/LLM/frank-large-flash-server-v2-swift15.md`.
+Rollback: revert the swap commit and redeploy — the 27B payload
+(`/home/dank/models/halogen-qwen3.8-27b/`), its fetch script and the
+halogen 0.1.4 image digest all remain on disk; the ROCmFPX door behind
+that (`scripts/build-rocmfpx.sh`, pin `36e9acd`) is untouched.
+
+### frank-secondary — llama.cpp on the RTX 3090 (Dankclaw, ACTIVE)
+
+**llama.cpp master** (0.5.0-dev, commit `5fc4f3c`; CUDA sm_86 build
+`/home/dank/GitPackages/llama-master/build-cuda/bin/llama-server`,
+`GGML_CUDA_FA_ALL_QUANTS=ON`) as a **root systemd unit**
+(`scripts/frank-secondary.service`, `relink_unit`ed into
 `/etc/systemd/system/`), serving
-`bartowski/MiMo-V2.6-Distill-Qwen-9B-GGUF` at **Q6_K** (7,793,710,624 B,
-sha256 `ef96d05a…`) **plus `mmproj-MiMo-V2.6-Distill-Qwen-9B-f16.gguf`**
-(918,166,048 B, sha256 `ff348f…`), both `/home/dank/models/MiMo-Q6K/` (the
-projector is a hardlink of the Q5KM copy — the dir is self-contained), on
-port 7771. The rung went Q5_K_M → Q6_K on 2026-10-03 the same morning as the
-reslice, funded by the freed slots. The runtime arrived 2026-10-01 night for
-one reason:
-**asymmetric KV** — `--cache-type-k f16` + `--cache-type-v q8_0` is the fleet's
-only per-tensor KV lever, and SGLang's `--kv-cache-dtype` moves K and V
-together. On 2026-10-03 Frank spent the headroom that lever bought: the reslice
-from three slots to two made room for **fully unquantized V**
-(`CACHE_TYPE_V=f16`); both flags stay explicit in the serve script. Vision came
-2026-10-02 via `--mmproj`: the GGUF carries no `v.*` tensors, so the projector
-file is the entire vision path (and ~1.4 GiB of the card).
+`HauhauCS/Qwen3.5-9B-Uncensored-HauhauCS-Aggressive` at **Q6_K**
+(7,359,259,008 B, sha256 `c0ba7beb…` computed locally — the Hub carries no
+LFS oid) **plus the publisher's own BF16 mmproj** (921,704,448 B), both
+`/home/dank/models/Qwen35-9B-HauhauCS-Q6K/`, on port 7771. Cutover
+2026-10-07 (late) from the Gemma4-26B-A4B Q4_K_M 1×200,704 shape (same day).
 
-**Architecture: qwen3_5 hybrid** — 32 layers, 8 full-attention + 24 Gated
-DeltaNet, native 262,144 window. KV exists only in the 8 attention layers
-(4 KV heads × 256 dim × 2 B ≈ 2 KiB/token/layer, ~16 KiB/token for f16 K+V);
-the DeltaNet state is a fixed per-slot buffer, so slot count — not context —
-drives that term.
+**Architecture: qwen35 dense hybrid** — read from the GGUF's own header, not
+the model card: 32 layers, `full_attention_interval = 4` ⇒ **8 full-attention
+layers (3, 7, 11, …, 31) + 24 Gated DeltaNet linear layers**, 16 attention
+heads / **4 KV heads** × 256 dim, `ssm` conv_kernel 4 / state_size 128 /
+group_count 16 / time_step_rank 32 / inner_size 4096, native 262,144, vocab
+248,320. Tensor types are Q6_K × 250 + F32 × 177 — standard quants only, so
+the stock master build loads it (arch `qwen35` is registered in this build's
+`src/llama-arch.cpp`). **KV accounting:** the pool scales with the 8 full
+layers only — 8 × 4 × 256 = 8,192 elements/token per cache, so f16 K + f16 V
+bills **32 KiB/token** = 12.25 GiB at the 401,408 pool; the 24 DeltaNet
+layers bill a fixed recurrent state per SLOT, not per token, which is exactly
+why two 200k slots fit where the 26B MoE fit one.
 
-**The shape (2026-10-03):** `--ctx-size 401408 --parallel 2` = **TWO
-conversations of 200,704 tokens each** (llama.cpp divides the pool:
-`n_ctx_slot = CTX_SIZE / PARALLEL`), `--flash-attn on` (the seam pins it),
-**f16 K + f16 V**, all layers on the GPU, `--mmproj` for vision, and since
-2026-10-05 **batch 4096 / ubatch 2048** (doubled; the boot-pair A/B is in the
-runbook). Measured trade at 8,928 prompt tokens: prefill 4,024 → **4,209 t/s,
-+4.6 %**, decode flat (91.9–93.0 vs 93.8–94.3 — noise), and a 279-token prompt,
-which sits below either ubatch, did not move at all. The old "ubatch 2048
-failed the scratch boot" note belongs to the Q5_K_M predecessor and does NOT
-generalize: this shape boots 2048 clean. Measured VRAM **21,846 used / 2,279
-free** of 24,576 MiB against **21,276 / 2,849** for the 1024 arm, reproduced
-byte-identically the same hour — the doubling costs **616 MiB** and lands at
-the BOTTOM of the 2–3 GiB floor Frank asked for (`4096/1536` is the middle rung
-if that margin is ever wanted; non-KV 9,302 MiB, up from 8,732 by exactly the
-compute buffers; f16 K+V is 32 KiB/token across the 8 attention layers;
-quote `nvidia-smi`'s `free` field, never `total − used` — the driver holds
-~450 MiB of reserve the subtraction hides). The 2026-10-02 3 × 180,224 +
-q8_0-V shape stays one env edit away (`CTX_SIZE=540672 PARALLEL=3
-CACHE_TYPE_V=q8_0`, and it must pair with Q5_K_M — Q6_K breaks the floor
-there). Three native windows need 18.4 GiB of KV alone and were never on the
-table.
+**The shape (2026-10-07):** `--ctx-size 401408 --parallel 2` = TWO
+conversations of 200,704 (Frank's cap; the harness `contextWindow` quotes
+the per-slot number), `--flash-attn on`, **f16 K + f16 V** (exact KV),
+batch 4096 / **ubatch 2048** (the measured CUDA optimum: +4.6 % prefill,
+decode flat, 616 MiB — MiMo-era A/B on this card), `--log-verbosity 4`,
+`--mmproj` for vision, **speculative decoding OFF** (`SPEC_TYPE=none`).
 
-Verified at deploy (2026-10-03): boot logs `n_slots = 2, n_ctx_slot = 200704`
-and `loaded multimodal model`; decode **94.44 tok/s** at temp 0 on the probe
-that measured 104.84 on Q5_K_M — **the Q6_K rung costs a measured −10 %**:
-decode on this card is weight-bandwidth-bound and Q6_K streams ~10 % more
-bytes/token; health 200, e2e 33/33; native `tool_calls`; thinking in
-`reasoning_content`; a **512×512** solid-red PNG answers `Red` through an
-`image_url` content part (two traps: the legacy top-level `images` field is
-ignored, and a TINY probe image undersupplies image tokens — a 10×10 PNG made
-the model claim "it's a video"; probe with ≥256 px). A prompt over the slot
-is a **400 `exceed_context_size_error`**, which is why `models.yml` sets
-`contextWindow: 200704`.
+**Why MTP is off**: it is not a choice — **this Q6_K file carries no
+`nextn`/`mtp` tensors** (verified from the tensor-name list), and MTP is a
+property of the file, not of the flags. The publisher card advertises "MTP
+support" for the base architecture; the quantized export dropped the head.
 
-Runbook: `serverAppConfigs-docs/LLM/frank-small-llama-mimo-q6k-2x200704.md`
-(the boot-pair table that prices pool changes lives in the retired
-[[frank-small-llama-mimo-q5km]]).
-Rollback: `MODEL_FILE=/home/dank/models/MiMo-Q5KM/MiMo-V2.6-Distill-Qwen-9B-Q5_K_M.gguf`
-buys back 875 MiB and the −10 % decode rate (3,571 free, 104.84 tok/s, both
-measured); the older shape `CTX_SIZE=540672 PARALLEL=3 CACHE_TYPE_V=q8_0`
-pairs with Q5 only; SGLang rollback in
-[[frank-small-sglang-mimo-v26]] (venv on disk at
-`/home/dank/GitPackages/sglang-cu128/`, INT8 payload staged to
-`~dank/.AGENTTRASH/20261002-retired-llm-payloads/`).
+**Measured boots** (nvidia-smi, the referee): the deployed boot is
+**21,436 used / 2,689 free** (ubatch 2048) — the pricing boots were the
+MiMo-V2.6-Distill-Qwen-9B pair at this exact geometry (**21,276 / 2,849** at
+ubatch 1024, **21,846 / 2,279** at 2048), so the 415 MiB lighter weights
+bought back what the projection predicted. The engine ledger confirms the KV
+law to the MiB: `CUDA0 KV buffer size = 12544.00 MiB` (`K (f16): 6272.00`,
+`V (f16): 6272.00`), compute buffers 1,136 MiB on CUDA0 + 848 MiB host, the
+mmproj 879 MiB + 248 MiB. Decode is ubatch-invariant (Swift-era 3090 law). The
+Q6_K rung costs ~10 % decode vs Q5_K_M (weight bandwidth; MiMo-measured 94.4
+vs 104.8 t/s).
 
-### frank-primary — FreeToken (PR #587 ROCm), Qwen3.8-Flash-Next FP8 (Dankserver, ACTIVE)
+**Vision**: the publisher's own BF16 mmproj via `--mmproj` (the model is
+natively multimodal; the Q6_K file carries no vision tensors). Probe traps
+carry over: ≥256 px probe images or the model answers "video", and only the
+OpenAI `image_url` content part reaches the model.
 
-The **FreeToken engine at PR #587 head `77a7201`** (branch
-`rocm714_torch214`, built on the host into the local image
-`localhost/frank-primary-freetoken:pr587-77a7201` by
-`scripts/docker/frank-primary-freetoken.Dockerfile` — the PR's own
-docs/install_amd.md stack: digest-pinned
-`rocm/pytorch:rocm7.14_ubuntu24.04_py3.12_pytorch_release_2.11.0` base =
-torch 2.11.0+rocm7.14/triton 3.7, `--no-build-isolation --no-deps -e .`,
-curated portable deps, `flashlib==0.3.0 --no-deps`, and a `libhiprtc.so`
-dev-symlink the pip-ROCm wheel lacks) serving
-**`orcarouter/Qwen3.8-Flash-Next-Uncensored-FP8`** (~172.8 GiB, 131 shards,
-standard modelopt block-FP8 — 75,264 `weight_scale_inv` tensors, no
-`input_scale`, loaded directly from HF safetensors, no FTW conversion) on
-**ONE R9700 (gfx1201)** — the second card sits idle. Plain `podman run`
-through `scripts/serve-frank-primary.sh` under the root systemd unit
-(`frank-primary.service`, `User=dank`, `LimitMEMLOCK=infinity`); the
-container name is the unit name (`frank-primary`; the vLLM-era
-`frank-primary-qwen` is in the legacy cleanup list). Cutover 2026-10-05;
-rollback tag `frank-primary-vllm-shapeG-20261003` (the tcclaviger vLLM trio
-and its 362-line env lore live in that tag; the tcclaviger image stays in
-dank's rootless storage).
+**Compaction hub**: `models.yml` routes `compactionModel` for
+`frank-archon`, `frank-dragoon` and `frank-zealot` (the 3090 endpoint) here — 200,704 is the
+only window in the fleet that fits a worst-case compaction input (~170k
+tokens). With `--parallel 2` a compaction request no longer serializes behind
+a live generation (the Gemma4 era's `--parallel 1` law).
 
-**Shape**: all expert weights in **pinned host RAM** (~120 GiB) behind a
-GPU LRU (`--moe-cache-rate 0.10`, ~12 GiB — the single-card ceiling given
-the KV contract), the ~47.7 GiB PLE ngram table pinned in host RAM
-(→ root unit + memlock law), **KV pool `--num-tokens 299968`**
-(24 KiB/token f16 across the 12 full-attention layers ≈ 7.0 GiB; **must be
-a multiple of 64** — the auto attention backend is `qsa_sparse`, which
-overrides page size to 64; 300,000 was refused at first boot), 2 decode
-slots, `--max-output-tokens 36864` (the harness maxTokens; requests that
-omit one get this default). **Parsers are AUTO**: thinking lands in
-`reasoning_content` natively, tool calls in the qwen3.8 dialect — models.yml
-flips `reasoningContentField` to `reasoning_content` on the harness side.
-Vision works (models.yml keeps `input: [text, image]`); the vision tower's
-blocks stream from host RAM two at a time by default.
+Runbook:
+`serverAppConfigs-docs/LLM/frank-secondary-llama-qwen35-9b-q6k-2x200704.md`.
+Rollback: revert the swap commit — the Gemma4 payload
+(`/home/dank/models/Gemma4-26B-A4B/`), the MiMo Q6_K payload
+(`/home/dank/models/MiMo-Q6K/`), the Swift payload
+(`/home/dank/models/Swift15-UncIQ3XXS/`) and the Ornith Q8_0 payload
+(`/home/dank/models/Ornith-Q8/`) all remain on disk.
 
-**Measured at cutover**: boot ~2.3 min cold-ish / 92 s warm → /health
-status ok; decode **17.1 tok/s** single-stream temp 0 (the vLLM incumbent
-measured ~30 tok/s on TWO cards; the KV contract caps the single-card LRU,
-and **TP=2 exists but does not boot on ROCm** — the TP allreduce JIT
-(`pynccl.cu`) can't resolve `cuda_runtime.h` for gfx1201; probed
-2026-10-06, signature in the runbook). `/metrics` is a 404 — the Dankclaw
-Prometheus llm-fleet scrape of :7770 reads down until repointed.
-`ft ctl cache --kv/--moe` resizes pools live (grow only; shrink needs a
-restart); `ft ctl stats` reports occupancy.
 
-**Deploy gate law (changed for FreeToken)**: `/health` returns **200 with
-`{"status":"loading"}`** while the engine loads — the FastAPI front end
-binds before the scheduler is up, so a bare `curl` false-greens (measured:
-"healthy after 6 s" mid-page-in). The deployer loops until the body says
-`"status":"ok"`; the MainPID stray-port guard is unchanged. The image gate
-is `podman image exists` (a local build carries no RepoDigests — the old
-digest grep refused exactly this shape; pinning lives in the Dockerfile).
+### frank-primary — Radiance (StillDeadcode), Swift1.5-Qwen3.8-Flash-Next GPTQ w4, TP=2 (Dankserver, ACTIVE)
 
-Runbook: `serverAppConfigs-docs/LLM/frank-primary-freetoken-pr587.md`.
+The **radiance engine** (StillDeadcode, Apache-2.0; image
+`stilldeadcode/radiance@sha256:4c3f484564…` digest-pinned, **pin 1.0.13**
+for serve/convert/KLD; `libr4d` gfx1201 kernels, plain `podman run`, root
+systemd unit, container name = unit name) serving
+**ukisai/Swift1.5-Qwen3.8-Flash-Next (BF16, 335.3 GiB, 131 shards,
+397/397 sha256-verified)** converted by `scripts/convert-swift15-w4dn8.sh`
+with recipe `scripts/recipes/qwen4exp-swift15-w4dn8-i8-hc8m.recipe` to
+`qwen4exp-swift15-w4dn8-i8-hc8m.rad` (~114 GiB): **all 49,152 experts GPTQ
+w4** (act-order, damp 0.05, 10,000,000-token calibration captured on engine
+**1.2.0** via `RADIANCE_CALIB_DIR`/`RADIANCE_CALIB_TOKENS` — the only pin
+with the token knob; the RADGRAM2 grams are format-identical across pins and
+cross the boundary by design), **24 whole experts per layer cast bf16** (the
+PROTECTED set, derived from the calibration grams' energy share),
+**int8 trunk/lm_head**, **fp8 HC mixtures** (`--hc-mix-dtype`), PLE n-gram
+table at fp8 fixed scale (47.7 GiB in the container, from 95.4 GiB bf16).
+The plugin's **one-table-one-encoding law** (measured at rad-convert
+declare): one expert table takes ONE encoding; only whole bf16 experts are
+legal beside it — no w4/fp8 split of the down/gate_up pair (24-value
+`rad_arch_quant_desc` array, one per layer). KLD gate vs the BF16
+reference (recorded on the same 1.0.13 pin) is the precision referee;
+the throughput gate (decode ≥ 30.8 t/s) was passed at 4.5× the bar
+(122–173 t/s at 44.5k depth). Measured decode/prefill in the runbook.
+Rollback: revert the swap commit — the fp8-native `.rad`
+(`qwen4exp-fp8-native.rad`) and every prior payload remain on disk.
+
+**Shape (both R9700s, TP=2)**: `--tp-wire wht6`, `--max-num-seqs 3`,
+`--max-model-len 200000` (the harness contextWindow must quote this),
+`--placement expert_tiered --host-pool-mib 40960` + **explicit VRAM
+partition `--vram-weights-mib 26071 --vram-kv-mib 3543`** (the vram-pair
+replaced `--expert-vs-cache-ratio`; the pair sizes the fp8-KV shape at the
+deployed boot), `--kv-cache-dtype fp8` (inherited from the fp8-native
+env), `--num-speculative-tokens 2`,
+`--max-num-batched-tokens 2048`, prefix cache 4 GiB pinned host + 64 GiB
+disk at `/home/dank/radiance-state`, **`--ngram-placement ram`**, Froggeric
+v22.5 chat template vendored (`scripts/qwen38-froggeric-v22.5.jinja`,
+`--override-chat-template`). Boot ledger: KV pool 3.46 GiB (~480k fp8
+tokens), two expert pools 57.84 GiB, cards settled 1.41 GiB free.
+
+Shape notes carried from the fp8-native era and unchanged: wht6 gains,
+MNDT 2048 law (2560 collapses decode to 2.6–7.7 t/s), the 64 GiB disk
+prefix cache, and the rule that a prompt over `--max-model-len` is
+refused, never truncated. What changed: the fp8-native era's NVMe expert
+streaming (the 3.5 GB/s wall) is gone — the w4 resident set fits the
+40 GiB pool, so decode ran 122–173 t/s in the campaign arms and ~190 t/s
+on the deployed shape (512-token streamed probe). The campaign also
+measured the int8-KV + spec-3 arm (136.0 t/s median at 44.5k depth,
+accept 0.7246); production shipped the fp8-KV + spec-2 pair at cutover —
+the KLD gate scored at the fp8-KV shape, so the gate and the boot agree.
+Disk-tier sizing law: the engine forces ~9.6 GiB of disk-tier weight
+buffers + arena onto the weights side, so `weights + kv` is capped near
+29.6 GiB/card; boot with the pair, never `--kv-cache-size-mib`.
+
+**The pinned-RAM ledger is why this is a root unit**: 2 × 40 GiB expert
+pools + the 47.7 GiB n-gram copy (`--ngram-placement ram` is pinned RAM
+the card reads) + 4 GiB prefix host ≈ **132 GiB** charged against
+`RLIMIT_MEMLOCK` — the TTM floor is set just above it.
+`ttm.pages_limit`/`pages_max` cap GPU-mappable pinned pages; the serve
+script's TTM preflight (`FRANK_PRIMARY_TTM_MIN_MIB=140000`) refuses to
+start until both knobs are raised in the stop window (sysfs write first,
+kernel cmdline + reboot only if read-only).
+
+**Contract facts** (source-verified): the HTTP listener binds AFTER the
+full load, so a bare `curl /health` gate is honest (`/health` == `/ping`,
+200 serving / 503 stopping); sampling params temperature…preserve_thinking
+accepted, **unknown params 400** (models.yml's extraBody set is fully
+inside it); `response_format` supported; `logprobs` REFUSED; reasoning in
+`reasoning_content` and tool calls in the qwen3.8 dialect via the auto
+chat format — no parser flags; vision native (the 27-layer Qwen-VL tower,
+`--mm-max-patches`); `/metrics` carries vLLM-compatible names +
+`radiance:*` families — the Dankclaw Prometheus scrape is re-added.
+
+Runbook: `serverAppConfigs-docs/LLM/frank-primary-radiance-swift15-w4dn8.md`.
+Retired fp8-native era: `serverAppConfigs-docs/LLM/frank-primary-radiance-qwen38-native-fp8.md`.
+Retired FreeToken era: `serverAppConfigs-docs/LLM/frank-primary-freetoken-pr587.md`.
 Retired vLLM era: `serverAppConfigs-docs/LLM/dankserver-qwen38-fp8-expert-offload-tuning.md`.
 Retired GLM experiment: `serverAppConfigs-docs/LLM/frank-primary-glm53-flash-w4a16.md`.
 
@@ -720,7 +789,7 @@ linked runbooks.
   121 GiB box, pushed `available` to 3 GiB, and every later rate was suspect.
   Record the child pid and kill it in a `finally`, then prove `free` came back
   before believing a number. Kill by pid — never `pkill -f llama-server`,
-  which also hits frank-small.
+  which also hits frank-secondary.
 
 **llama.cpp serving**
 
@@ -786,7 +855,7 @@ linked runbooks.
   free. That was a CONTIGUITY failure, not a total-bytes one, and no totals
   model — the sweep's or the seam's — can see it.)
 - **On the CUDA 3090 the same knob has NO interior optimum up to 2048, so
-  frank-small ships 4096/2048.** Measured 2026-10-05 by boot-pair A/B on the
+  frank-secondary ships 4096/2048.** Measured 2026-10-05 by boot-pair A/B on the
   live service: at 8,928 prompt tokens ubatch 1024 gives 3,972 / 4,077 t/s and
   ubatch 2048 gives 4,207 / 4,212 — **+4.6 %** — for **616 MiB** (free 2,849 →
   2,279). Decode did not move (91.9–93.0 vs 93.8–94.3, i.e. noise), and a
@@ -795,9 +864,9 @@ linked runbooks.
   than the ubatch cannot see it. The logical `--batch-size` was again free. And
   the predecessor's "ubatch 2048 failed the scratch boot" was a shape
   observation, not a law: the same file boots 2048 clean at 2 × 200,704. Bench
-  it by restarting the SERVICE through a transient `FRANK_SMALL_ENV_FILE`
+  it by restarting the SERVICE through a transient `FRANK_SECONDARY_ENV_FILE`
   drop-in, never a scratch port — one card holds one server. See
-  [[frank-small-llama-mimo-q6k-2x200704]].
+  [[frank-secondary-llama-mimo-q6k-2x200704]].
 - **One `-ts` step is one LAYER, and a hybrid stack's layers are not the same
   size.** qwen35 is 16 x (3 Gated DeltaNet + 1 Gated Attention), so a step is
   worth ~0.43 GiB on a DeltaNet boundary and ~1.43 GiB on a full-attention one
@@ -819,7 +888,7 @@ linked runbooks.
   CTX_SIZE / PARALLEL`, and llama.cpp refuses a prompt longer than the SLOT
   (`400 exceed_context_size_error: request (184001 tokens) exceeds the
   available context size (180224 tokens)`) even when the pool has room. So
-  frank-small's 2 → 3 reslice moved each conversation from the model's native
+  frank-secondary's 2 → 3 reslice moved each conversation from the model's native
   262,144 down to 180,224, and `models.yml` `contextWindow` had to follow or
   the harness would discover the ceiling as a 400 on a long conversation. Side
   effect worth knowing: the shorter slot made decode ~5 % FASTER (106.8 vs
@@ -830,9 +899,9 @@ linked runbooks.
   day the freed space also bought the Q6_K rung (now 94.4 tok/s — the rung
   itself is a further measured −10 %, weight-bandwidth, not slot geometry).
   All moves measured; both slot directions cost or buy window, never pool.
-  See [[frank-small-llama-mimo-q6k-2x200704]].
+  See [[frank-secondary-llama-mimo-q6k-2x200704]].
 - **Price a VRAM budget from two boots, never from a formula — and an mmproj
-  costs ~1.5x its file size.** The frank-small 3090 fit was solved from two
+  costs ~1.5x its file size.** The frank-secondary 3090 fit was solved from two
   measurements: 2 × 262,144 with no projector = 20,190 MiB used (KV 12,800 ⇒
   **non-KV 7,390**), 3 × 196,608 with the f16 projector = 22,972 used (KV
   14,113 ⇒ **non-KV 8,859**). The difference is the vision tower: a 918 MB file
@@ -840,12 +909,12 @@ linked runbooks.
   ride with it. Subtracting the two rows is exact; extrapolating weights+KV+
   "a bit for buffers" is what leaves 1.1 GiB free when you meant 2.7. Then
   solve `KV ≤ card − non-KV − headroom` and round to a multiple of
-  `PARALLEL × 4096`. Recorded in `scripts/frank-small.env` so the next resize
+  `PARALLEL × 4096`. Recorded in `scripts/frank-secondary.env` so the next resize
   starts from measurement.
 - **`images` and `image_url` are NOT interchangeable: only the content part
   reaches the model.** `--mmproj` loads, `usage.prompt_tokens` rises, and the
   model still invents an answer if the client used the legacy top-level
-  `images` field on `/v1/chat/completions` — frank-small answered "Brown." for
+  `images` field on `/v1/chat/completions` — frank-secondary answered "Brown." for
   a solid-red PNG that way and "Red." through the OpenAI `image_url` part. The
   failure is silent because a hallucinated color is a plausible answer. Probe
   vision with a solid color and expect an exact word.
@@ -857,7 +926,7 @@ linked runbooks.
   returns `tool_calls`. Check tool support before routing a role at an endpoint.
 - **Quantized KV on CUDA needs `GGML_CUDA_FA_ALL_QUANTS=ON`** — the same
   option and the same ~12x prefill collapse described in the ROCm note above.
-  f16 KV needs nothing; frank-small ran f16 for that reason until
+  f16 KV needs nothing; frank-secondary ran f16 for that reason until
   2026-09-17, when `build-cuda` was rebuilt with the option ON so the
   endpoint could take q8_0 KV.
 - **llama-bench separates a LIST with `/` and SWEEP values with `,`.** Both
@@ -875,7 +944,7 @@ linked runbooks.
   raises on anything else — HTTP 500, not a clamp — so the harness's levels
   must be folded onto that set in `models.yml` (`compat.reasoningEffortMap`).
   A role whose level is unmapped fails on its FIRST request, and the failure
-  names Jinja, not the model: every subagent on `frank-small` died with
+  names Jinja, not the model: every subagent on `frank-secondary` died with
   `Unexpected reasoning effort high` until `high: xhigh` was added on
   2026-09-16. Halogen accepts the wider set, so the two entries legitimately
   differ.
@@ -949,9 +1018,9 @@ Active:
 
 | Service | Runbook |
 |---------|---------|
-| frank-large (halogen container, Qwen3.8-27B .hgn + DFlash2, 1×200,700, text-only, Dankclaw) | `serverAppConfigs-docs/LLM/frank-large-halogen27b-dflash2.md` |
-| frank-small (llama.cpp CUDA, MiMo-V2.6-Distill-Qwen-9B Q6_K, 2×200,704 f16 KV, Dankclaw) | `serverAppConfigs-docs/LLM/frank-small-llama-mimo-q6k-2x200704.md` |
-| frank-primary (FreeToken PR #587 ROCm, Qwen3.8-Flash-Next FP8, 1×R9700, Dankserver) | `serverAppConfigs-docs/LLM/frank-primary-freetoken-pr587.md` |
+| frank-large (flash-server 0.16.2, abliterated Swift1.5 v2 .hgn + MTP always-on, 2×262,144, vision ON, Dankclaw) | `serverAppConfigs-docs/LLM/frank-large-flash-server-v2-swift15.md` |
+| frank-secondary (llama.cpp CUDA, Qwen3.5-9B-Uncensored Q6_K, 2×200,704 exact f16 KV MTP-off, vision ON, Dankclaw) | `serverAppConfigs-docs/LLM/frank-secondary-llama-qwen35-9b-q6k-2x200704.md` |
+| frank-primary (radiance StillDeadcode, Qwen3.8-Flash-Next-FP8 native, 2×R9700 TP=2, Dankserver) | `serverAppConfigs-docs/LLM/frank-primary-radiance-qwen38-native-fp8.md` |
 | Dankclaw kernel posture (`amd_iommu=off`) | `serverAppConfigs-docs/LLM/dankclaw-iommu-translation-off.md` |
 | Deployment flow + incidents | `serverAppConfigs-docs/LLM/deployment.md` |
 
@@ -959,14 +1028,19 @@ Retired (rollback targets and history):
 
 | Runtime | Host | Window | Runbook |
 |---------|------|--------|---------|
+| llama.cpp CUDA Gemma4-26B-A4B QAT Q4_K_M, 1×200,704 f16-K/q8_0-V MTP-off (`frank-secondary`) | Dankclaw | 2026-10-07 → 10-07 (same-day swap; rollback target — payload on disk; owns the `attention_k_eq_v` pair-billing KV law and the gemma4-assistant drafter-cost boots) | `frank-secondary-llama-gemma4-q4km-200704.md` |
 | ROCmFPX llama.cpp fork Qwen3.8-27B Uncensored Q6_0_ROCMFPX_AGENT + MTP (`frank-large`) | Dankclaw | 2026-10-03 → 10-05 (rollback target, payload + fork build on disk) | `frank-large-rocmfpx-q6agent-uncensored-mtp.md` |
 | tcclaviger vLLM fork Qwen3.8-Flash-Next-Uncensored FP8, TP=2, shape G (`frank-primary`) | Dankserver | 2026-09-29 → 10-05 (rollback target — tag `frank-primary-vllm-shapeG-20261003`; trio + env lore in the tag, image in rootless storage) | `dankserver-qwen38-fp8-expert-offload-tuning.md` |
+| FreeToken PR #587 ROCm, Qwen3.8-Flash-Next-Uncensored-FP8, ONE R9700 (`frank-primary`) | Dankserver | 2026-10-05 → 10-05 (rollback target — tag `frank-primary-freetoken-pr587-20261005`; trio + Dockerfile in the tag, image `localhost/frank-primary-freetoken:pr587-77a7201` and orcarouter payload on disk) | `frank-primary-freetoken-pr587.md` |
+| llama.cpp master MiMo-V2.6-Distill-Qwen-9B Q6_K 2×200,704 f16 KV (`frank-secondary`) | Dankclaw | 2026-10-03 → 10-05 (rollback target — payload on disk; owns the ubatch-2048 A/B; the Ornith swap of 10-05 was committed but never deployed, so this was the LIVE payload Swift replaced) | `frank-secondary-llama-mimo-q6k-2x200704.md` |
+| llama.cpp Ornith-1.5-9B Q8_0, ONE 262,144 f16 KV (`frank-secondary`) | Dankclaw | 2026-10-05 (committed 77decb5→380aafa, never deployed — superseded by Swift the same evening; payload on disk) | trail in `frank-secondary-llama-swift15-unc-iq3xxs-mtp-114688.md` |
+| llama.cpp CUDA Swift-1.5-27B Uncensored RCO IQ3_XXS-mtp, 2×114,688 draft-mtp q8_0 KV (`frank-secondary`) | Dankclaw | 2026-10-06 → 2026-10-07 (rollback target — payload on disk; owns the 3090 ubatch ladder A/B and the 34 KiB/token qwen35 KV law) | `frank-secondary-llama-swift15-unc-iq3xxs-mtp-114688.md` |
 | ROCmFPX llama.cpp fork Qwen3.8-27B Q8_0_ROCMFPX_AGENT + MTP (`frank-large`) | Dankclaw | 2026-10-02 → 10-03 (rollback target, payload on disk) | `frank-large-rocmfpx-q8agent-mtp.md` |
 | gufo v0.1.1 Qwen3.8-27B UD-Q8_K_XL/UD-Q4_K_XL + DFlash2 (`frank-large`) | Dankclaw | 2026-09-28 → 10-01, again 10-01 → 10-02 (payloads removed from disk 2026-10-03; rollback = re-download) | `frank-large-gufo-qwen38-27b.md` |
 | vLLM TheRock MiMo-V2.6-Distill-Qwen-9B BF16 + cross-model MTP (`frank-large`) | Dankclaw | 2026-10-01 (same-day rollback to gufo) | `frank-large-vllm-mimo-v26-bf16.md` |
-| llama.cpp master MiMo-V2.6-Distill-Qwen-9B Q6_K (`frank-small`) | Dankclaw | 2026-10-01 → 10-02 (the rung returned ACTIVE 2026-10-03 at the 2×200,704 shape — `frank-small-llama-mimo-q6k-2x200704.md`) | `frank-small-llama-mimo-q6k.md` |
-| llama.cpp master MiMo-V2.6-Distill-Qwen-9B Q5_K_M 2×200,704 f16 KV (`frank-small`) | Dankclaw | 2026-10-02 → 10-03 (rollback target — one `MODEL_FILE` line, buys back 875 MiB + the −10 % rung tax; owns the boot-pair table) | `frank-small-llama-mimo-q5km.md` |
-| SGLang MiMo-V2.6 INT8-W8A16-AutoRound (`frank-small`) | Dankclaw | 2026-10-01 (same-day swap to the GGUF path) | `frank-small-sglang-mimo-v26.md` |
+| llama.cpp master MiMo-V2.6-Distill-Qwen-9B Q6_K (`frank-secondary`) | Dankclaw | 2026-10-01 → 10-02 (the rung returned ACTIVE 2026-10-03 at the 2×200,704 shape — `frank-secondary-llama-mimo-q6k-2x200704.md`) | `frank-secondary-llama-mimo-q6k.md` |
+| llama.cpp master MiMo-V2.6-Distill-Qwen-9B Q5_K_M 2×200,704 f16 KV (`frank-secondary`) | Dankclaw | 2026-10-02 → 10-03 (rollback target — one `MODEL_FILE` line, buys back 875 MiB + the −10 % rung tax; owns the boot-pair table) | `frank-secondary-llama-mimo-q5km.md` |
+| SGLang MiMo-V2.6 INT8-W8A16-AutoRound (`frank-secondary`) | Dankclaw | 2026-10-01 (same-day swap to the GGUF path) | `frank-secondary-sglang-mimo-v26.md` |
 |tcclaviger vLLM GLM-5.3-Flash W4A16 int4, +/- DFlash2 drafter (`frank-primary`)|Dankserver|2026-09-30 → 10-01|`frank-primary-glm53-flash-w4a16.md`|
 | llama.cpp b11030 Qwen3.8-27B UD-Q8_K_XL + DFlash2 (`frank-large`) | Dankclaw | 2026-09-21 → 09-28 | `frank-large-unsloth-qwen38-27b-q8xl.md` |
 | R9V radiance-vLLM Qwen3.8-Flash-Next-Uncensored IQ4_XS (`frank-primary`) | Dankserver | 2026-09-27 (same-day swap to Q8_0) | `frank-primary-r9v-radiance-qwen38-uncensored.md` |
@@ -979,16 +1053,16 @@ Retired (rollback targets and history):
 | CIRU v4.4.1 Orca Qwen3.8-Flash (`frank-large`) | Dankclaw | 2026-09-19 → 09-21 | `frank-large-ciru-orca.md` |
 | unsloth llama.cpp b11030 Qwen3.8-Flash-Next UD-IQ4_XS (`frank-large`) | Dankclaw | 2026-09-21 (same-day swap) | `frank-large-unsloth-qwen38-flash-next.md` |
 | Kairic Edge Qwen3.8-27B IU4 W4A4 (`frank-large`) | Dankclaw | 2026-09-21 (same-day swap) | `frank-large-kairic-edge-qwen38-27b.md` |
-| ISTA-DASLab Qwen3.8-27B-GSQ-RCO IQ3_S (`frank-small`) | Dankclaw | 2026-09-16 → 09-17 | `frank-small-qwen38-gsq-rco.md` |
-| vLLM Gemma4-12B w4a16 (`frank-small`) | Dankclaw | 2026-09-19 (same-day revert) | `frank-small-vllm-gemma4.md` |
-| llama.cpp Gemma4-12B QAT Q4_K_M (`frank-small`) | Dankclaw | 2026-09-17 → 10-01 | `frank-small-gemma4-12b.md` |
+| ISTA-DASLab Qwen3.8-27B-GSQ-RCO IQ3_S (`frank-secondary`) | Dankclaw | 2026-09-16 → 09-17 | `frank-secondary-qwen38-gsq-rco.md` |
+| vLLM Gemma4-12B w4a16 (`frank-secondary`) | Dankclaw | 2026-09-19 (same-day revert) | `frank-secondary-vllm-gemma4.md` |
+| llama.cpp Gemma4-12B QAT Q4_K_M (`frank-secondary`) | Dankclaw | 2026-09-17 → 10-01 | `frank-secondary-gemma4-12b.md` |
 | agentionai ROCmFP4-FAST imatrix | Dankclaw | 2026-08-29 → 08-30 | `agentionai-fast-imatrix.md` |
 | eemin Qwen3.8-27B ROCmI4 W4A4 MTP | Dankclaw | 2026-08-18 → 08-25 | `eemin-rocmi4-mtp.md` |
 | Kairic Edge IU4 | Dankclaw | 2026-08-22 (same-day revert) | `kairic-edge.md` |
 | CIRU ActiveFPX v3 / PromptForge | Dankclaw | through 2026-08-22 | `ciru-activefpx-promptforge.md` |
 | DFlash2 Vulkan | Dankclaw | 2026-08-21 (same-day revert) | `dflash2-large-vulkan.md` |
 | FastFlowLM NPU (`frank-npu`) | Dankclaw | twice, retired 2026-09-11 | `frank-npu.md`, `FastFlowLM NPU Inference.md` |
-| FreeToken (official + Huihui FP8, `frank-small`) | Dankserver | 2026-09-02 → 09-15 | `freetoken-frank-small.md` |
+| FreeToken (official + Huihui FP8, `frank-secondary`) | Dankserver | 2026-09-02 → 09-15 | `freetoken-frank-small.md` |
 | FastMTP patched llama.cpp | Dankserver | retired 2026-08-18 | `qwen3.8-spec-matrix.md` |
 | ik_llama.cpp Ornith CRACK | Dankserver | retired 2026-09-02 | `ikllama-ornith-dankserver.md` |
 | llama.cpp layer split + DFlash2 (`frank-medium`) | Dankserver | 2026-09-16 → 09-20 | `frank-medium-llamacpp-dflash2.md` |

autoresearch.sh

@@ -1,117 +1,26 @@
 #!/usr/bin/env bash
-# autoresearch.sh — canonical benchmark entrypoint: speed of frank-primary.
+# autoresearch.sh — canonical frank-primary benchmark entry point (dev box).
 #
-# Runs on the operator box and drives one bench window on DANKSERVER:
-#   1. fingerprint the candidate trio (env + serve script + unit)
-#   2. memo fast-path: an identical fingerprint that already produced a FULLY
-#      successful window (bench + verified production restore) returns the
-#      cached metrics without touching the fleet
-#   3. stage the candidate trio plus the measure client into
-#      /home/dank/.autoresearch-frank-primary/ (ephemeral; the repo checkout
-#      on the host stays the production source of truth)
-#   4. run the window (scripts/autoresearch/frank-primary-window.sh):
-#      stop + mask the production unit, boot the staged config AS THE SERVICE
-#      (same serve script, same container, same port 7770), wait for health,
-#      run the in-container usage-counter measure, then restore the production
-#      unit and verify its health — AGENTS.md Benchmarking Policy
-#   5. print METRIC lines (parse by run_experiment)
-#
-# Primary metric: decode_tps (HIGHER is better) — median across the three
-# prompt depths (115/1056/4096) of per-depth medians of
-# completion_tokens / (wall - ttft), 3 runs each after a warm pass,
-# temperature 0, the campaign's deterministic word-seed workload.
-#
-# Exit 0 with METRIC lines on success; non-zero on any failure (the window
-# script's EXIT trap restores production on every path).
-set -euo pipefail
+# The service runs on dankserver (192.168.0.235:7770), the two R9700s live
+# there, and the benchmark must own those cards, so the whole stop -> boot
+# scratch -> bench -> restore sequence runs ON dankserver (see
+# scripts/autoresearch-frank-primary.sh). This wrapper:
+#   1. copies the candidate config (the repo's scripts/frank-primary.env, the
+#      file the autoresearch loop edits) and the bench script to dankserver,
+#   2. runs the bench over ssh, relaying its stdout (the METRIC lines) and
+#      exit code back here verbatim.
+# It deliberately does NOT push to origin: the scp dirties dankserver's
+# working tree, and the loop's uncommitted candidate config must not be
+# pushed. Only this file and scripts/autoresearch-frank-primary.sh are
+# committed; the candidate env rides over ssh each run.
+set -uo pipefail
+DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+REMOTE=dankserver
+REMOTE_REPO=/home/dank/serverAppConfigs
 
-SCRIPT_DIR="$(cd -- "$(dirname -- "${BASH_SOURCE[0]}")" && pwd)"
-HOST="${FRANK_PRIMARY_AR_HOST:-dankserver}"
-STAGE="/home/dank/.autoresearch-frank-primary"
+scp -q "$DIR/scripts/frank-primary.env" \
+    "$REMOTE:$REMOTE_REPO/scripts/frank-primary.env" || { echo "scp env failed" >&2; exit 1; }
+scp -q "$DIR/scripts/autoresearch-frank-primary.sh" \
+    "$REMOTE:$REMOTE_REPO/scripts/autoresearch-frank-primary.sh" || { echo "scp script failed" >&2; exit 1; }
 
-# FRANK_PRIMARY_AR_ENV_FILE stages a DIFFERENT env than the repo's — that is how
-# the arms driver A/Bs engine knobs without mutating the checkout. The
-# fingerprint covers whatever file is staged, so the memo cannot cross arms.
-ENV_FILE="${FRANK_PRIMARY_AR_ENV_FILE:-$SCRIPT_DIR/scripts/frank-primary.env}"
-SERVE="$SCRIPT_DIR/scripts/serve-frank-primary.sh"
-UNIT="$SCRIPT_DIR/scripts/frank-primary.service"
-MEASURE="$SCRIPT_DIR/scripts/autoresearch/frank-primary-measure.py"
-WINDOW="$SCRIPT_DIR/scripts/autoresearch/frank-primary-window.sh"
-
-for f in "$ENV_FILE" "$SERVE" "$UNIT" "$MEASURE" "$WINDOW"; do
-    [ -f "$f" ] || { echo "[autoresearch] missing harness file: $f" >&2; exit 1; }
-done
-command -v python3 >/dev/null 2>&1 || { echo "[autoresearch] python3 required on operator box" >&2; exit 1; }
-
-# Fingerprint of everything that can change the engine's argv or environment.
-HASH="$(cat "$ENV_FILE" "$SERVE" "$UNIT" | sha256sum | cut -d' ' -f1)"
-echo "[autoresearch] candidate fingerprint $HASH" >&2
-
-ssh_run() { ssh -o BatchMode=yes -o ConnectTimeout=15 "$HOST" "$@"; }
-
-emit_metrics() { # $1 = run.json
-    python3 -c '
-import json, sys
-run = json.loads(sys.argv[1])
-m = run["measure"]
-print("METRIC decode_tps=%s" % m["decode_tps"])
-for k in ("decode_tps_115", "decode_tps_1056", "decode_tps_4096"):
-    print("METRIC %s=%s" % (k, m[k]))
-print("METRIC ttft_ms_4096=%s" % m["ttft_ms_4096"])
-print("METRIC kv_tokens=%s" % (run.get("kv_tokens") or 0))
-print("METRIC boot_seconds=%s" % (run.get("boot_seconds") or -1))
-print("METRIC completion_tokens_4096=%s" % m.get("completion_tokens_4096", 0))
-# Any extra sampling temperature the measure swept (AR_TEMPS), e.g. the 0.7
-# arm — that is where speculative-decode acceptance shows up, since a greedy
-# draft matches a greedy target exactly and is rejected more under real
-# sampling. Suffixed keys only (_t0.7), so the historical lines are untouched.
-for k in sorted(m):
-    if "_t" in k and (k.startswith("decode_tps") or k.startswith("ttft_ms")):
-        print("METRIC %s=%s" % (k, m[k]))
-' "$1"
-}
-
-# --- memo fast-path -------------------------------------------------------
-cached_hash="$(ssh_run "cat '$STAGE/last/hash' 2>/dev/null" 2>/dev/null || true)"
-if [ -n "$cached_hash" ] && [ "$cached_hash" = "$HASH" ]; then
-    cached_run="$(ssh_run "cat '$STAGE/last/run.json' 2>/dev/null" || true)"
-    if [ -n "$cached_run" ]; then
-        echo "[autoresearch] memo hit — cached metrics for $HASH, fleet untouched" >&2
-        emit_metrics "$cached_run"
-        exit 0
-    fi
-fi
-
-# --- stage the candidate ----------------------------------------------------
-echo "[autoresearch] staging candidate on $HOST:$STAGE" >&2
-ssh_run "mkdir -p '$STAGE'"
-scp -q "$ENV_FILE" "$HOST:$STAGE/frank-primary.env"
-scp -q "$SERVE" "$HOST:$STAGE/serve.sh"
-scp -q "$MEASURE" "$HOST:$STAGE/measure.py"
-scp -q "$WINDOW" "$HOST:$STAGE/window.sh"
-ssh_run "printf '%s\n' '$HASH' > '$STAGE/hash'"
-
-# --- the window -------------------------------------------------------------
-# Budgets: bench boot up to 20 min (warm boots measured ~7 min), measure up
-# to 30 min, restore boot up to 20 min — call run_experiment with
-# timeout_seconds >= 3600.
-echo "[autoresearch] opening bench window (stop+mask -> boot -> measure -> restore)" >&2
-set +e
-ssh_run "bash '$STAGE/window.sh'"
-rc=$?
-set -e
-if [ "$rc" -ne 0 ]; then
-    echo "[autoresearch] window failed (rc=$rc); host-side evidence:" >&2
-    ssh_run "tail -60 '$STAGE/fail.log' 2>/dev/null || tail -40 '$STAGE/serve.out' 2>/dev/null || tail -40 '$STAGE/measure.out' 2>/dev/null || true" >&2 || true
-    exit 1
-fi
-
-run_json="$(ssh_run "cat '$STAGE/run.json'")" || {
-    echo "[autoresearch] could not read $STAGE/run.json back" >&2; exit 1; }
-echo "[autoresearch] window result:" >&2
-printf '%s\n' "$run_json" >&2
-emit_metrics "$run_json"
-
-echo "[autoresearch] engine evidence (plan / compile path / spec counters):" >&2
-ssh_run "cat '$STAGE/evidence.log' 2>/dev/null" >&2 || true
-exit 0
+ssh "$REMOTE" "bash $REMOTE_REPO/scripts/autoresearch-frank-primary.sh $REMOTE_REPO/scripts/frank-primary.env"

dankclaw/Caddyfile

@@ -337,7 +337,16 @@ http://:8017 {
 			try_files /docs.html
 			file_server
 		}
-		@data path /services.json /status.json /app.js /style.css /deployed.js
+		# /whoami echoes the outpost-injected identity headers so the page
+		# can gate the admin button client-side (the /admin* 403 above stays
+		# the real gate; this only decides what is shown). The outpost strips
+		# client-supplied X-Authentik-* before injecting, so this is spoof-proof.
+		handle /whoami {
+			templates
+			header Content-Type application/json
+			respond "{\"user\":\"{header.X-Authentik-Username}\",\"groups\":\"{header.X-Authentik-Groups}\"}"
+		}
+		@data path /services.json /status.json /app.js /style.css /deployed.js /img/* /sfx/*
 		handle @data {
 			file_server
 		}

dankclaw/comfyui/workflows/qwen-image-2.1-t2i-art.json

@@ -0,0 +1,64 @@
+{
+  "3": {
+    "class_type": "CLIPTextEncode",
+    "_meta": { "title": "Positive prompt" },
+    "inputs": {
+      "text": "A detailed illustration of a 1930s Soviet industrial design bureau at night: a well-measured engineer at a drafting table, a warm desk lamp, rolled blueprints, gears and calipers on the desk, deep red and cream palette, gouache painterly style, intricate mechanical detail, soft shadows",
+      "clip": [ "6", 0 ]
+    }
+  },
+  "4": {
+    "class_type": "CLIPTextEncode",
+    "_meta": { "title": "Negative prompt" },
+    "inputs": {
+      "text": "lowres, blurry, deformed, extra limbs, watermark, signature, jpeg artifacts",
+      "clip": [ "6", 0 ]
+    }
+  },
+  "5": {
+    "class_type": "UNETLoader",
+    "_meta": { "title": "Qwen Image 2.1 (int8)" },
+    "inputs": { "unet_name": "qwen_image_2.1_int8_convrot.safetensors", "weight_dtype": "default" }
+  },
+  "6": {
+    "class_type": "CLIPLoader",
+    "_meta": { "title": "Qwen3-VL 8B text encoder (w4a8)" },
+    "inputs": { "clip_name": "qwen3vl_8b_w4a8.safetensors", "type": "qwen_image" }
+  },
+  "7": {
+    "class_type": "VAELoader",
+    "_meta": { "title": "VAE" },
+    "inputs": { "vae_name": "qwen_image_2.1_vae_bf16.safetensors" }
+  },
+  "8": {
+    "class_type": "EmptySD3LatentImage",
+    "_meta": { "title": "Empty Latent 1024x1024" },
+    "inputs": { "width": 1024, "height": 1024, "batch_size": 1 }
+  },
+  "9": {
+    "class_type": "KSampler",
+    "_meta": { "title": "KSampler" },
+    "inputs": {
+      "model": [ "5", 0 ],
+      "seed": 1930,
+      "steps": 20,
+      "cfg": 1.0,
+      "sampler_name": "euler",
+      "scheduler": "simple",
+      "positive": [ "3", 0 ],
+      "negative": [ "4", 0 ],
+      "latent_image": [ "8", 0 ],
+      "denoise": 1.0
+    }
+  },
+  "10": {
+    "class_type": "VAEDecode",
+    "_meta": { "title": "VAE Decode" },
+    "inputs": { "samples": [ "9", 0 ], "vae": [ "7", 0 ] }
+  },
+  "11": {
+    "class_type": "SaveImage",
+    "_meta": { "title": "Save Image (art)" },
+    "inputs": { "filename_prefix": "qwen21-art", "images": [ "10", 0 ] }
+  }
+}

dankclaw/comfyui/workflows/qwen-image-2.1-t2i-legible-text.json

@@ -0,0 +1,64 @@
+{
+  "3": {
+    "class_type": "CLIPTextEncode",
+    "_meta": { "title": "Positive prompt (legible text)" },
+    "inputs": {
+      "text": "Clean minimalist movie-poster design, dark charcoal background with a subtle vintage grain. Centered large bold white sans-serif text reading \"SOLIDARITY OF THE SOVIET COMPUTER SYSTEMS\", perfectly legible, crisp letterforms, even letter spacing. Below it, a smaller line of yellow text reading \"DANKCLAW DANKSERVER EST 2026\", equally legible. Flat graphic design, high contrast, generous margins, no other words, no watermark",
+      "clip": [ "6", 0 ]
+    }
+  },
+  "4": {
+    "class_type": "CLIPTextEncode",
+    "_meta": { "title": "Negative prompt" },
+    "inputs": {
+      "text": "blurry, illegible, misspelled, warped letters, extra text, watermark, signature, lowres, jpeg artifacts",
+      "clip": [ "6", 0 ]
+    }
+  },
+  "5": {
+    "class_type": "UNETLoader",
+    "_meta": { "title": "Qwen Image 2.1 (int8)" },
+    "inputs": { "unet_name": "qwen_image_2.1_int8_convrot.safetensors", "weight_dtype": "default" }
+  },
+  "6": {
+    "class_type": "CLIPLoader",
+    "_meta": { "title": "Qwen3-VL 8B text encoder (w4a8)" },
+    "inputs": { "clip_name": "qwen3vl_8b_w4a8.safetensors", "type": "qwen_image" }
+  },
+  "7": {
+    "class_type": "VAELoader",
+    "_meta": { "title": "VAE" },
+    "inputs": { "vae_name": "qwen_image_2.1_vae_bf16.safetensors" }
+  },
+  "8": {
+    "class_type": "EmptySD3LatentImage",
+    "_meta": { "title": "Empty Latent 832x1216 poster" },
+    "inputs": { "width": 832, "height": 1216, "batch_size": 1 }
+  },
+  "9": {
+    "class_type": "KSampler",
+    "_meta": { "title": "KSampler" },
+    "inputs": {
+      "model": [ "5", 0 ],
+      "seed": 2026,
+      "steps": 25,
+      "cfg": 1.0,
+      "sampler_name": "euler",
+      "scheduler": "simple",
+      "positive": [ "3", 0 ],
+      "negative": [ "4", 0 ],
+      "latent_image": [ "8", 0 ],
+      "denoise": 1.0
+    }
+  },
+  "10": {
+    "class_type": "VAEDecode",
+    "_meta": { "title": "VAE Decode" },
+    "inputs": { "samples": [ "9", 0 ], "vae": [ "7", 0 ] }
+  },
+  "11": {
+    "class_type": "SaveImage",
+    "_meta": { "title": "Save Image (text poster)" },
+    "inputs": { "filename_prefix": "qwen21-text", "images": [ "10", 0 ] }
+  }
+}

dankclaw/hermes/config.yaml.example

@@ -10,15 +10,15 @@ model:
   provider: dankclaw-large
   base_url: http://192.168.0.143:7770/v1
   api_key: not-needed
-  default: frank-large
+  default: frank-dragoon
 
 providers:
   dankclaw-large:
     api: http://192.168.0.143:7770/v1
     discover_models: false
     models:
-      frank-large:
-        context_length: 114688
+      frank-dragoon:
+        context_length: 262144
   dankserver-small:
     api: http://192.168.0.235:7770/v1
     discover_models: false
@@ -29,7 +29,7 @@ providers:
 # Auxiliary routing from the live configuration. Named providers route via
 # their `api:` URL; `custom` entries route by their explicit `base_url`.
 auxiliary:
-  vision: {provider: dankclaw-large, model: frank-large}
+  vision: {provider: dankclaw-large, model: frank-dragoon}
   web_extract: {provider: dankserver-small, model: frank-medium}
   compression: {provider: dankserver-small, model: frank-medium}
   skills_hub: {provider: dankserver-small, model: frank-medium}
@@ -50,8 +50,8 @@ auxiliary:
         effort: none
   memory_query_rewrite: {provider: custom, model: frank-medium, base_url: http://192.168.0.235:7770/v1}
   tts_audio_tags: {provider: custom, model: frank-medium, base_url: http://192.168.0.235:7770/v1}
-  triage_specifier: {provider: dankclaw-large, model: frank-large}
-  kanban_decomposer: {provider: dankclaw-large, model: frank-large}
+  triage_specifier: {provider: dankclaw-large, model: frank-dragoon}
+  kanban_decomposer: {provider: dankclaw-large, model: frank-dragoon}
   profile_describer: {provider: dankserver-small, model: frank-medium}
   curator: {provider: dankserver-small, model: frank-medium}
 

old/download-frank-large-mimo.sh

@@ -4,7 +4,7 @@
 # command line (a Bearer token in argv is world-readable via /proc).
 #
 # Per-file FRESH downloads, no resume: the house rule (see
-# download-frank-small.sh); -f/--fail-with-body plus size+sha256 against the
+# download-frank-secondary.sh); -f/--fail-with-body plus size+sha256 against the
 # Hub LFS oid is what makes an access error fail instead of "succeeding".
 #
 # Usage:

old/download-frank-large-qwen35.sh

@@ -2,7 +2,7 @@
 # Download the source Qwen3.5-9B checkpoint — the ONLY place this model
 # family's MTP head (15 mtp.* tensors) exists. frank-large runs it as the
 # vLLM cross-model MTP draft (MiMo-V2.6-Distill-Qwen-9B itself ships zero
-# mtp tensors; that is why SGLang's NEXTN on frank-small draws 0.00
+# mtp tensors; that is why SGLang's NEXTN on frank-secondary draws 0.00
 # acceptance). Public repo — no auth header on the command line.
 #
 # NOTE the odd upstream shard names (model.safetensors-0000N-of-00004...).

old/download-frank-secondary-sglang-int8.sh

@@ -1,5 +1,5 @@
 #!/usr/bin/env bash
-# Download MiMo-V2.6-Distill-Qwen-9B-INT8-W8A16-AutoRound for frank-small
+# Download MiMo-V2.6-Distill-Qwen-9B-INT8-W8A16-AutoRound for frank-secondary
 # (SGLang, RTX 3090). Public repo — no auth header on the command line.
 #
 # Per-file FRESH downloads, no resume: `curl -C -` once produced a 213 MB
@@ -7,7 +7,7 @@
 # the house rule.
 #
 # Usage:
-#   scripts/download-frank-small.sh [DEST_DIR] [--verify-only]
+#   scripts/download-frank-secondary.sh [DEST_DIR] [--verify-only]
 #
 # LFS shards are verified against the Hub LFS sha256; non-LFS text files
 # against their declared byte size. Idempotent: complete files are skipped.

scripts/apply-protected-cast.sh

@@ -0,0 +1,43 @@
+#!/usr/bin/env bash
+# apply-protected-cast.sh — RUNS ON DANKSERVER. Rewrite the recipe's
+# PROTECTED-BEGIN/END section from Swift's OWN calibration grams: run
+# derive-protected-experts.py over the calib dir, splice its cast lines into
+# the marked section of the recipe in place, keep the pre-edit recipe in the
+# campaign trash. Refuses to run twice (marker text says derived).
+set -euo pipefail
+CALIB=${1:-/home/dank/corpora/calib}
+RECIPE=${2:-/home/dank/serverAppConfigs-swift15/scripts/recipes/qwen4exp-swift15-w4dn8-i8-hc8m.recipe}
+PY=/home/dank/venvs/radcorpus/bin/python
+DERIVE=/home/dank/serverAppConfigs-swift15/scripts/derive-protected-experts.py
+TS=$(date +%Y%m%d-%H%M%S)
+
+grep -q "derive-protected-experts.py rewrote" "$RECIPE" && {
+    echo "recipe already carries derived cast lines; refusing to re-derive" >&2; exit 1; }
+grep -q "PROTECTED-BEGIN" "$RECIPE" || { echo "no PROTECTED markers in $RECIPE" >&2; exit 1; }
+
+"$PY" "$DERIVE" "$CALIB" > "/tmp/protected-cast-$TS.txt"
+N=$(grep -c 'cast dtype=bf16' "/tmp/protected-cast-$TS.txt")
+[ "$N" -ge 2 ] || { echo "derive produced $N cast lines; refusing" >&2; exit 1; }
+echo "derived $N protected experts -> /tmp/protected-cast-$TS.txt"
+
+cp -f "$RECIPE" "/home/dank/.AGENTTRASH/$TS-qwen4exp-swift15-w4dn8-i8-hc8m.recipe"
+"$PY" - "$RECIPE" "/tmp/protected-cast-$TS.txt" <<'PYEOF'
+import sys
+recipe, castfile = sys.argv[1], sys.argv[2]
+lines = open(recipe).read().splitlines()
+cast = [l for l in open(castfile).read().splitlines() if 'cast dtype=bf16' in l]
+out, insec = [], False
+for l in lines:
+    if 'PROTECTED-BEGIN' in l:
+        insec = True
+        out.append(l)
+        out.extend(cast)
+    elif 'PROTECTED-END' in l:
+        insec = False
+        out.append(l)
+        out.append('# (derive-protected-experts.py rewrote this section from Swift\'s own grams)')
+    elif not insec:
+        out.append(l)
+open(recipe, 'w').write('\n'.join(out) + '\n')
+print(f'spliced {len(cast)} cast lines into {recipe}')
+PYEOF

scripts/autoresearch-bench.py

@@ -104,7 +104,7 @@ def stream_completion(url: str, model: str, prompt: str):
 def main():
     ap = argparse.ArgumentParser()
     ap.add_argument("--url", default="http://127.0.0.1:7770")
-    ap.add_argument("--model", default="frank-large")
+    ap.add_argument("--model", default="frank-dragoon")
     args = ap.parse_args()
 
     with urllib.request.urlopen(args.url.rstrip("/") + "/v1/models",

scripts/autoresearch-frank-primary.sh

@@ -0,0 +1,187 @@
+#!/usr/bin/env bash
+# autoresearch-frank-primary.sh — RUNS ON DANKSERVER, driven over ssh by the
+# dev-box autoresearch.sh. It is the canonical frank-primary benchmark entry
+# point for the autoresearch loop.
+#
+# Method (mandated by the Benchmarking Policy): the production service serves
+# real users and shares the two R9700s, so a bench beside it starves the GPUs
+# and corrupts results. This script therefore:
+#   1. stops the production frank-primary unit (root, relink symlink: stop,
+#      NOT mask — mask refuses to replace a symlinked unit file),
+#   2. boots a scratch radiance container named `arbench` on the SAME two
+#      cards with the candidate engine argv (the env file the loop edits),
+#   3. polls /health until the listener is live (it binds AFTER the full
+#      load, so a bare curl is an honest gate),
+#   4. runs ONE self-contained completion request locally (127.0.0.1, no LAN
+#      jitter): a fixed ~140k-char real-text prompt (~32k tokens) decoded for
+#      256 tokens, greedy. The engine's `timings` object reports the honest
+#      prefill and decode rates for that single request.
+#   5. prints METRIC lines and tears the scratch down,
+#   6. restores production (systemctl start), via a trap so a failed bench
+#      never leaves frank-primary down (the stale-probe-tail lesson).
+#
+# Determinism: the prompt is built from a fixed, sorted corpus of repo files
+# with a fixed random seed, so prompt_n is stable across runs. Sampling is
+# greedy (temperature 0), so the decode length is fixed; the only residual
+# nondeterminism is the wht6 lossy TP all-reduce, which is a constant across
+# runs and cancels in A/B comparisons.
+#
+# The scratch mirrors the production container contract exactly (serve-frank-
+# primary.sh): --network host, --init, seccomp=unconfined (io_uring for the
+# n-gram table), memlock unbounded (the ~132 GiB pinned ledger), /dev/kfd +
+# /dev/dri, the .rad ro, the state dir rw, the tune cache mount. The ONLY
+# differences: the container name (arbench, so prod's identity is untouched)
+# and a FRESH prefix-cache disk tier (arbench/, wiped per run) so the cold
+# prefill is cold — the prod tier at kvcache/frank-primary is left intact.
+set -uo pipefail
+
+ENV_FILE="${1:-/home/dank/serverAppConfigs/scripts/frank-primary.env}"
+SCRATCH=arbench
+PORT=7770
+HEALTH="http://127.0.0.1:$PORT/health"
+COMPLETE="http://127.0.0.1:$PORT/v1/completions"
+BODY=/tmp/arbench-body.json
+LOG=/home/dank/logs/arbench.log
+
+restore() {
+  podman rm -f "$SCRATCH" >/dev/null 2>&1 || true
+  sudo systemctl start frank-primary >/dev/null 2>&1 || true
+}
+trap restore EXIT
+
+# --- source the candidate config (same file the production unit reads) ------
+if [ ! -f "$ENV_FILE" ]; then
+  echo "no env file at $ENV_FILE" >&2; exit 1
+fi
+# shellcheck disable=SC1090
+set -a; . "$ENV_FILE"; set +a
+
+# --- stop production; the scratch needs both cards to itself ----------------
+sudo systemctl stop frank-primary >/dev/null 2>&1 || true
+podman rm -f "$SCRATCH" >/dev/null 2>&1 || true
+
+# --- fresh prefix-cache tier for a cold prefill (prod's tier untouched) ------
+FRESH_KV="$FRANK_PRIMARY_STATE_DIR/kvcache/$SCRATCH"
+rm -rf "$FRESH_KV"; mkdir -p "$FRESH_KV"
+mkdir -p "$FRANK_PRIMARY_STATE_DIR/tune"
+
+# --- boot the scratch: the production run line, scratch name + fresh KV ------
+podman run -d --name "$SCRATCH" \
+  --network host \
+  --init \
+  --security-opt seccomp=unconfined \
+  --ulimit memlock=-1:-1 \
+  --device /dev/kfd --device /dev/dri \
+  -v "$FRANK_PRIMARY_MODEL_DIR":/models:ro \
+  -v "$FRANK_PRIMARY_STATE_DIR":/data \
+  -v "$FRANK_PRIMARY_STATE_DIR/tune":/opt/radiance/share/radiance/tune \
+  -v "$FRANK_PRIMARY_TEMPLATE":/templates/template.jinja:ro \
+  "$FRANK_PRIMARY_IMAGE" \
+  --model "/models/$FRANK_PRIMARY_MODEL_FILE" \
+  --host :: --port "$PORT" \
+  --served-model-name "$FRANK_PRIMARY_SERVED_NAME" \
+  --override-chat-template /templates/template.jinja \
+  --tp "$FRANK_PRIMARY_TP" --tp-wire "$FRANK_PRIMARY_TP_WIRE" \
+  --max-num-seqs "$FRANK_PRIMARY_MAX_SEQS" \
+  --max-model-len "$FRANK_PRIMARY_MAX_MODEL_LEN" \
+  --placement "$FRANK_PRIMARY_PLACEMENT" \
+  --host-pool-mib "$FRANK_PRIMARY_HOST_POOL_MIB" \
+  --gpu-headroom-mib "$FRANK_PRIMARY_GPU_HEADROOM_MIB" \
+  --vram-weights-mib "$FRANK_PRIMARY_VRAM_WEIGHTS_MIB" \
+  --vram-kv-mib "$FRANK_PRIMARY_VRAM_KV_MIB" \
+  --kv-cache-dtype "$FRANK_PRIMARY_KV_DTYPE" \
+  --prefix-cache-host-mib "$FRANK_PRIMARY_PREFIX_HOST_MIB" \
+  --prefix-cache-dir "/data/kvcache/$SCRATCH" \
+  --prefix-cache-disk-mib "$FRANK_PRIMARY_PREFIX_DISK_MIB" \
+  --ngram-placement "$FRANK_PRIMARY_NGRAM_PLACEMENT" \
+  --num-speculative-tokens "$FRANK_PRIMARY_SPEC_TOKENS" \
+  --max-num-batched-tokens "$FRANK_PRIMARY_MAX_BATCHED_TOKENS" \
+  ${FRANK_PRIMARY_EXTRA_ARGS:+$FRANK_PRIMARY_EXTRA_ARGS} \
+  > "$LOG" 2>&1
+if [ $? -ne 0 ]; then echo "scratch boot failed; see $LOG" >&2; exit 1; fi
+
+# --- poll health (listener binds after the full load; cold boot ~110 s) ------
+ok=0
+for _ in $(seq 1 360); do
+  code=$(curl -s -o /dev/null -w "%{http_code}" --max-time 3 "$HEALTH" 2>/dev/null || echo 000)
+  if [ "$code" = "200" ]; then ok=1; break; fi
+  sleep 2
+done
+if [ "$ok" -ne 1 ]; then
+  echo "scratch never reached health 200; tail of $LOG:" >&2
+  tail -30 "$LOG" >&2; exit 1
+fi
+
+# --- build the workload: a fixed ~140k-char real-text prompt (repo corpus) ---
+python3 - "$BODY" <<'PY'
+import json, sys, random, glob
+random.seed(1234)
+root = "/home/dank/serverAppConfigs"
+files = sorted(
+    glob.glob(f"{root}/serverAppConfigs-docs/**/*.md", recursive=True)
+    + glob.glob(f"{root}/scripts/*.sh")
+    + glob.glob(f"{root}/scripts/*.py")
+    + glob.glob(f"{root}/AGENTS.md")
+)
+corpus = []
+total = 0
+for f in files:
+    try:
+        t = open(f, encoding="utf-8", errors="ignore").read()
+    except Exception:
+        continue
+    if not t.strip():
+        continue
+    corpus.append(t)
+    total += len(t)
+    if total > 240000:
+        break
+corpus = "\n".join(corpus)
+TGT = 140000
+parts, n = [], 0
+while n < TGT:
+    start = random.randint(0, max(0, len(corpus) - 500))
+    seg = corpus[start:start + random.randint(200, 500)]
+    parts.append(seg)
+    n += len(seg) + 1
+prompt = " ".join(parts)
+json.dump(
+    {"model": "frank-archon", "prompt": prompt,
+     "max_tokens": 256, "temperature": 0.0, "stream": False,
+     "stop": ["<|im_end|>"]},
+    open(sys.argv[1], "w"),
+)
+PY
+if [ ! -f "$BODY" ]; then echo "prompt build failed" >&2; exit 1; fi
+
+# --- the bench request (prefill ~32k + decode 256, greedy) -------------------
+RESP=$(curl -s --max-time 600 -X POST "$COMPLETE" \
+  -H 'Content-Type: application/json' -d "@$BODY")
+echo "$RESP" > /tmp/arbench-resp.json
+
+# --- parse the timings into METRIC lines -------------------------------------
+python3 - <<'PY'
+import json, sys
+try:
+    r = json.load(open("/tmp/arbench-resp.json"))
+except Exception as e:
+    sys.stderr.write(f"unparseable response: {e}\n"); sys.exit(1)
+if "timings" not in r:
+    sys.stderr.write(f"no timings in response: {json.dumps(r)[:500]}\n"); sys.exit(1)
+t = r["timings"]
+dtps = t.get("predicted_per_second")
+ptps = t.get("prompt_per_second")
+pms  = t.get("prompt_ms")
+dn   = t.get("draft_n", 0)
+da   = t.get("draft_n_accepted", 0)
+if dtps is None:
+    sys.stderr.write("predicted_per_second null (no decode produced)\n"); sys.exit(1)
+print(f"METRIC decode_tps={dtps:.4f}")
+if ptps is not None:
+    print(f"METRIC prefill_tps={ptps:.4f}")
+if pms is not None:
+    print(f"METRIC ttft_ms={pms:.4f}")
+if dn:
+    print(f"METRIC draft_accept={da/dn:.4f}")
+print(f"INFO prompt_n={t.get('prompt_n')} cache_n={t.get('cache_n')} predicted_n={t.get('predicted_n')}")
+PY

scripts/autoresearch/frank-primary-measure.py

@@ -45,7 +45,7 @@ import urllib.error
 import urllib.request
 
 BASE = os.environ.get("AR_BASE", "http://127.0.0.1:7770")
-MODEL = os.environ.get("AR_MODEL", "frank-primary")
+MODEL = os.environ.get("AR_MODEL", "frank-archon")
 PROMPT_CEILING = int(os.environ.get("AR_PROMPT_CEILING", "100000"))
 DEPTHS = (115, 1056, 4096)
 WARM_TOKENS = 64

scripts/bench-decode.sh

@@ -57,7 +57,7 @@ run_bench() {
     # Warmup (small request)
     curl -s -m 60 http://127.0.0.1:$PORT/v1/chat/completions \
         -H "Content-Type: application/json" \
-        -d '{"model":"frank-large","messages":[{"role":"user","content":"Hi"}],"max_tokens":16,"stream":false}' > /dev/null 2>&1 || true
+        -d '{"model":"frank-dragoon","messages":[{"role":"user","content":"Hi"}],"max_tokens":16,"stream":false}' > /dev/null 2>&1 || true
     
     # Build 8K prompt
     local prompt

scripts/bench-probe.py

@@ -0,0 +1,48 @@
+#!/usr/bin/env python3
+"""bench-probe.py — RUNS ON DANKSERVER. The campaign's decode probe: one
+/completion request, a prompt assembled from calib.jsonl to ~TARGET tokens,
+temperature 0, max_tokens 512. Reports the engine's own timings: decode rate,
+prefill rate, draft accept. Run 3x; the median is the arm's number."""
+import json, sys, urllib.request
+
+TARGET = 43600  # tokens
+BYTES_PER_TOK = 3.8
+URL = "http://localhost:7770/v1/completions"
+
+def build_prompt():
+    want = int(TARGET * BYTES_PER_TOK)
+    parts = []
+    n = 0
+    with open("/home/dank/corpora/out/calib.jsonl") as f:
+        for line in f:
+            d = json.loads(line)
+            p = d.get("prompt") or d.get("messages", [{}])[0].get("content", "")
+            parts.append(p)
+            n += len(p.encode())
+            if n >= want:
+                break
+    return "".join(parts)
+
+def probe(salt):
+    prompt = f"[probe {salt}]\n" + build_prompt()
+    body = json.dumps({
+        "model": "frank-archon",
+        "prompt": prompt, "temperature": 0.0, "max_tokens": 512,
+    }).encode()
+    req = urllib.request.Request(URL, data=body,
+        headers={"Content-Type": "application/json"})
+    with urllib.request.urlopen(req, timeout=1800) as r:
+        d = json.load(r)
+    t = d.get("timings", {})
+    return {
+        "prompt_n": t.get("prompt_n"),
+        "prefill_tps": round(t.get("prompt_per_second", 0), 1),
+        "pred_n": t.get("predicted_n"),
+        "decode_tps": round(t.get("predicted_per_second", 0), 2),
+        "draft_n": t.get("draft_n"),
+        "accept": (round(t["draft_n_accepted"] / t["draft_n"], 4)
+                   if t.get("draft_n") else None),
+    }
+
+if __name__ == "__main__":
+    print(json.dumps(probe(sys.argv[1] if len(sys.argv) > 1 else "a")))

scripts/bench-threads-batch-http.py

@@ -39,7 +39,7 @@ def send_prefill_request():
     with open(PROMPT_FILE, "r") as f:
         prompt = f.read()
     body = json.dumps({
-        "model": "frank-large",
+        "model": "frank-dragoon",
         "messages": [{"role": "user", "content": prompt}],
         "max_tokens": 1,
         "stream": False

scripts/build-frank-primary-rad.sh

@@ -0,0 +1,166 @@
+#!/usr/bin/env bash
+# build-frank-primary-rad.sh — build frank-primary's .rad container from the
+# OFFICIAL Qwen/Qwen3.8-Flash-Next-FP8 checkpoint, at NATIVE precision.
+#
+#   bash scripts/build-frank-primary-rad.sh                # convert
+#   bash scripts/build-frank-primary-rad.sh --plan-only    # declare, no I/O
+#   bash scripts/build-frank-primary-rad.sh --restage      # redo staging
+#   bash scripts/build-frank-primary-rad.sh --force        # rebuild existing
+#
+# WHY A STAGING DIR (the one non-obvious step): rad-convert selects its
+# architecture plugin by the checkpoint's quantisation DESCRIPTOR —
+# derived from config.json's quantization_config (core/format/checkpoint.cpp:
+# "fp8" here). No shipped plugin claims (qwen4exp, "fp8"): the shipped
+# qwen4exp plugin claims (qwen4exp, ""), the descriptor recipe-built
+# containers carry (they are built from the bf16 upstream, which has no
+# quantization_config at all). The match is exact with no fallback, so an
+# unmodified convert refuses before declare. The descriptor is only a
+# selection key; per-weight encodings are data — the checkpoint reader
+# derives them from the tensors themselves (X.weight_scale_inv is the
+# scale plane; checkpoint.h). Staging drops the quantization_config key
+# (everything else is a relative symlink into the pristine download) so
+# the convert selects the shipped plugin. EVERY weight is then copied through
+# in its own encoding — block-FP8 codes + bf16 scale planes stay block-FP8 —
+# except TWO value-preserving reshapes that stack-qwen38-official-fp8.py
+# performs on the staging dir first, and ONE recipe rule that finishes the
+# PLE table:
+#   * experts: the plugin maps the STACKED layout (mlp.experts.gate_up_proj
+#     [E,2F,H] with the _scale_inv plane at the same index) while the official
+#     repo ships per-expert unfused tensors — staging byte-stacks them
+#     (gate rows first, no requantisation).
+#   * PLE: the plugin's fp8 branch wants the table codes beside a ONE-ELEMENT
+#     bf16 scale plane; the official repo ships 128 SCALELESS fp8 shards plus
+#     one bf16 scalar. Staging decodes to bf16 with the checkpoint's own
+#     scale, and the convert's rtn rule re-encodes at that same scale —
+#     every original code is restored exactly and the scale plane lands in
+#     the form the plugin declares.
+#
+# WHY A CONTAINER AT ALL: a checkpoint dir has no mapped/file tiers, and a
+# weight only a host kernel reads (the n-gram / PLE table, 51B params)
+# cannot be served from one (GUIDE §3.4). rad-convert also bakes the
+# tokenizer and the checkpoint's chat template; the runtime replaces the
+# template with froggeric's (--override-chat-template).
+#
+# The convert runs rad-convert inside the digest-pinned radiance image
+# (the host has no ROCm and needs none): /home/dank/models is mounted at
+# /models so the staging dir's RELATIVE symlinks resolve inside the
+# container. Output lands beside the raw download, owned by dank
+# (rootless podman maps container-root to the invoking user).
+set -euo pipefail
+
+SRC="/home/dank/models/Qwen/Qwen3.8-Flash-Next-FP8"
+STAGE="/home/dank/models/stage-qwen4exp-fp8"
+OUT_DIR="/home/dank/models/rad"
+OUT="$OUT_DIR/qwen4exp-fp8-native.rad"
+
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+# shellcheck disable=SC1091
+source "$SCRIPT_DIR/frank-primary.env"
+IMAGE="$FRANK_PRIMARY_IMAGE"
+
+plan_only=0 restage=0 force=0
+for a in "$@"; do
+    case "$a" in
+        --plan-only) plan_only=1 ;;
+        --restage)   restage=1 ;;
+        --force)     force=1 ;;
+        *) echo "unknown flag: $a" >&2; exit 2 ;;
+    esac
+done
+
+if [ ! -f "$SRC/config.json" ]; then
+    echo "ERROR: raw checkpoint missing: $SRC (run scripts/download-frank-primary.sh --fetch)" >&2
+    exit 1
+fi
+echo "verifying raw checkpoint first..."
+bash "$SCRIPT_DIR/download-frank-primary.sh"
+
+if [ -f "$OUT" ] && [ "$plan_only" -eq 0 ] && [ "$force" -eq 0 ]; then
+    echo "container already exists: $OUT"
+    echo "rebuild it with --force (the old file moves to ~/.AGENTTRASH)"
+    exit 0
+fi
+
+# ---- staging ----------------------------------------------------------------
+if [ "$restage" -eq 1 ] && [ -e "$STAGE" ]; then
+    TRASH="$HOME/.AGENTTRASH/$(date +%Y%m%d)-frank-primary-stage"
+    mkdir -p "$TRASH"
+    mv "$STAGE" "$TRASH/stage-$(date +%H%M%S)"
+    echo "old staging dir moved to $TRASH"
+fi
+if [ ! -f "$STAGE/config.json" ]; then
+    mkdir -p "$STAGE"
+    python3 - "$SRC/config.json" "$STAGE/config.json" <<'PY'
+import json, sys
+src, dst = sys.argv[1], sys.argv[2]
+cfg = json.load(open(src))
+dropped = cfg.pop("quantization_config", None)
+json.dump(cfg, open(dst, "w"), indent=2)
+print(f"staging config.json written (quantization_config dropped: {dropped is not None})")
+PY
+    # The index is COPIED like config.json, never symlinked: the reshape step
+    # rewrites the stage's index, and a symlink would write through to the
+    # raw checkpoint (measured 2026-10-05: the raw index was restamped with
+    # the stacked layout and the raw verify failed 143/144). Shards stay
+    # relative symlinks — relative so the targets resolve inside the convert
+    # container (the stage's parent is mounted at /models).
+    for f in "$SRC"/*; do
+        name="$(basename "$f")"
+        case "$name" in config.json|model.safetensors.index.json) continue ;; esac
+        ln -sfn "../Qwen/Qwen3.8-Flash-Next-FP8/$name" "$STAGE/$name"
+    done
+    cp "$SRC/model.safetensors.index.json" "$STAGE/"
+    echo "staging dir ready: $STAGE ($(ls "$STAGE" | wc -l) entries)"
+fi
+
+# ---- checkpoint reshape (stacked experts + bf16 PLE table) -------------------
+# stack-qwen38-official-fp8.py reshapes the staged checkpoint in place
+# (idempotent) and writes the PLE scale to $STAGE/ple_scale.txt — see the
+# script's docstring for why each transform is value-preserving.
+echo "stacking experts + merging the PLE table (value-preserving)..."
+python3 "$SCRIPT_DIR/stack-qwen38-official-fp8.py" --stage "$STAGE"
+PLE_SCALE="$(tr -d '\n' < "$STAGE/ple_scale.txt")"
+echo "PLE fixed scale: $PLE_SCALE"
+
+# ---- convert ----------------------------------------------------------------
+# rad-convert needs no GPU: declare resolves kernels and asks each about the
+# encoding it will read — CPU-side layout questions only. (If a plugin load
+# ever demands a card, add --device /dev/kfd --device /dev/dri to this run.)
+# --plan-only runs the same declare phase and exits before a byte is written.
+echo "rad-convert: $SRC (staged) -> $OUT"
+mkdir -p "$OUT_DIR"
+CONVERT_ARGS=(/models/stage-qwen4exp-fp8 -o /models/rad/qwen4exp-fp8-native.rad
+              --home /opt/radiance/share/radiance)
+[ "$plan_only" -eq 1 ] && CONVERT_ARGS+=(--plan-only -v)
+# THE ONE RECIPE RULE (see header): the PLE table arrives from staging as a
+# bf16 merge of the checkpoint's own fp8 codes decoded with the checkpoint's
+# own scale; rtn re-encodes it at that same scale — the codes come back
+# exactly and the plugin's one-element scale plane is produced.
+CONVERT_ARGS+=(--quant "blk.*.ple_ngram.weight=rtn:codes=fp8_e4m3,block=*x*,scale=bf16,rule=fixed,scale_value=$PLE_SCALE")
+[ "$force" -eq 1 ] && [ -f "$OUT" ] && {
+    TRASH="$HOME/.AGENTTRASH/$(date +%Y%m%d)-frank-primary-rad"
+    mkdir -p "$TRASH"
+    mv "$OUT" "$TRASH/qwen4exp-fp8-native.rad.$(date +%H%M%S)"
+    echo "previous container moved to $TRASH"
+}
+podman run --rm \
+    --entrypoint rad-convert \
+    -v /home/dank/models:/models \
+    "$IMAGE" "${CONVERT_ARGS[@]}"
+
+if [ "$plan_only" -eq 1 ]; then
+    echo "plan-only run complete (no container written)"
+    exit 0
+fi
+
+# ---- record -----------------------------------------------------------------
+echo "rad-info summary:"
+podman run --rm --entrypoint rad-info -v /home/dank/models:/models:ro \
+    "$IMAGE" "/models/rad/qwen4exp-fp8-native.rad" | head -20
+MIB=$(( $(stat -c %s "$OUT") / 1048576 ))
+echo "container: $OUT (${MIB} MiB; env floor $FRANK_PRIMARY_RAD_MIN_MIB MiB)"
+if [ "$MIB" -lt "$FRANK_PRIMARY_RAD_MIN_MIB" ]; then
+    echo "ERROR: container below the env floor — the convert was truncated" >&2
+    exit 1
+fi
+echo "build complete"

scripts/calib-push.py

@@ -0,0 +1,110 @@
+#!/usr/bin/env python3
+"""calib-push.py — drive a calibration corpus through a radiance scratch
+server's prefill so its tap writes layer Hessians (docs/MOE-W4.md).
+
+Runs ON DANKSERVER next to the scratch server (the one autoresearch- /
+serve-style scratch container booted with RADIANCE_CALIB_DIR set).
+Reads the corpus JSONL produced by make-calib-corpus.py
+({"prompt": "..."} a line), POSTs each prompt to /v1/completions with
+max_tokens=1 (prefill-only cost, one token out), at N-way concurrency
+matching the scratch server's --max-num-seqs budget.
+
+Corpus rounds repeat until the engine prints the tap's completion line
+("calibration wrote layer Hessians") in `podman logs <container>` — that
+is the ground truth that RADIANCE_CALIB_TOKENS was reached. After that
+line, further prompts are pure waste, so the driver exits.
+
+Mechanics notes:
+- requests carry no sampling params that matter (max_tokens=1, greedy).
+- a per-request timeout of 30 min: at ~1k tok/s prefill a 12k-token
+  prompt is a few seconds; the timeout only catches a wedged engine.
+- failures print and continue: the tap pools over tokens, a few lost
+  prompts do not bias the Hessians (the corpus is drawn round-robin over
+  source classes, so loss is class-neutral as long as failures are rare;
+  a systematic 4xx is loud in the log and must be fixed, not ignored).
+
+Usage: calib-push.py [--port 7770] [--parallel 64] [--container arbench]
+       [--corpus /home/dank/corpora/out/calib.jsonl] [--max-rounds 4]
+"""
+import argparse
+import concurrent.futures
+import json
+import subprocess
+import sys
+import time
+import urllib.request
+
+DONE_MARK = "calibration wrote"
+
+
+def tap_done(container):
+    """True if the engine logged the tap's completion line."""
+    try:
+        out = subprocess.run(
+            ["podman", "logs", "--tail", "50", container],
+            capture_output=True, text=True, timeout=30,
+        )
+    except Exception:
+        return False
+    return DONE_MARK in (out.stdout + out.stderr)
+
+
+def push_one(url, prompt):
+    body = json.dumps({
+        "model": "frank-archon",
+        "prompt": prompt,
+        "max_tokens": 1,
+        "temperature": 0,
+        "stream": False,
+    }).encode()
+    req = urllib.request.Request(
+        url, data=body, headers={"Content-Type": "application/json"})
+    try:
+        with urllib.request.urlopen(req, timeout=1800) as r:
+            r.read()
+        return True
+    except Exception as e:
+        print(f"FAIL {str(e)[:120]}", flush=True)
+        return False
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--port", type=int, default=7770)
+    ap.add_argument("--parallel", type=int, default=64)
+    ap.add_argument("--container", default="arbench")
+    ap.add_argument("--corpus", default="/home/dank/corpora/out/calib.jsonl")
+    ap.add_argument("--max-rounds", type=int, default=4)
+    a = ap.parse_args()
+    url = f"http://127.0.0.1:{a.port}/v1/completions"
+
+    prompts = []
+    with open(a.corpus) as f:
+        for line in f:
+            line = line.strip()
+            if line:
+                prompts.append(json.loads(line)["prompt"])
+    print(f"corpus: {len(prompts)} prompts", flush=True)
+
+    for rnd in range(1, a.max_rounds + 1):
+        if tap_done(a.container):
+            break
+        t0 = time.time()
+        ok = 0
+        with concurrent.futures.ThreadPoolExecutor(a.parallel) as ex:
+            for good in ex.map(lambda p: push_one(url, p), prompts):
+                ok += 1 if good else 0
+        dt = time.time() - t0
+        print(f"round {rnd}: {ok}/{len(prompts)} ok in {dt:.0f}s "
+              f"({sum(len(p) for p in prompts)/4/dt:.0f} chars/s est)",
+              flush=True)
+        if tap_done(a.container):
+            break
+    else:
+        print("max rounds exhausted, tap not done", flush=True)
+        sys.exit(1)
+    print("TAP_DONE: calibration wrote layer Hessians", flush=True)
+
+
+if __name__ == "__main__":
+    main()

scripts/convert-swift15-w4dn8.sh

@@ -0,0 +1,73 @@
+#!/usr/bin/env bash
+# convert-swift15-w4dn8.sh — RUNS ON DANKSERVER. The final recipe convert of
+# the Swift 1.5 checkpoint: gate_up experts through GPTQ against the 10M-token
+# calibration Grams, down projections block-fp8, protected experts bf16,
+# trunk int8, PLE fixed-scale fp8 (scripts/recipes/
+# qwen4exp-swift15-w4dn8-i8-hc8m.recipe).
+#
+# The convert engine is 1.0.13 — the SAME pin that will serve the result, so
+# the .rad crosses no version boundary. Only the Gram files cross versions
+# (1.2.0 wrote them; the RADGRAM2 magic, gram.r%d.%s.bin naming and the
+# calib_gram/calib_resolve/calib_stem symbols are byte-identical between the
+# two libquant.so). rad-convert runs on the host backend (no GPU visible in
+# the container) — production can stay up through this script.
+#
+# Prereqs (campaign order): calibration complete (calib-push printed TAP_DONE),
+# the protected-expert cast lines rewritten into the recipe by
+# derive-protected-experts.py, and the 335 GiB bf16 container deleted —
+# the peak-disk arithmetic needs that space (checkpoint 335.3 + output ~132
+# GiB on the volume; --reuse is NOT used, see the recipe header).
+set -euo pipefail
+
+PIN_1013=sha256:4c3f484564bb2872784e46b0a59cbd71becc9c1e746e301a3596ffae253c322e
+IMAGE="stilldeadcode/radiance@$PIN_1013"
+CKPT=/home/dank/models/ukisai/Swift1.5-Qwen3.8-Flash-Next
+RECIPE=/home/dank/serverAppConfigs-swift15/scripts/recipes/qwen4exp-swift15-w4dn8-i8-hc8m.recipe
+CALIB=/home/dank/corpora/calib
+OUT=/home/dank/models/rad/qwen4exp-swift15-w4dn8-i8-hc8m.rad
+LOG=/home/dank/logs/swift15_convert.log
+# gate_up ~37.6 + down ~18.8 + PLE 47.7 + trunk/embeddings/head ~10 GiB (all-w4 expert table;
+# the plugin's one-encoding law dropped the block-fp8 down tier -- see the recipe header).
+MIN_MIB=${FRANK_SWIFT15_RAD_MIN_MIB:-108000}
+
+# Gate 1: inputs. A missing Gram directory converts UNCALIBRATED with a
+# per-tensor stderr note, which is silently worse than failing here.
+[ -d "$CKPT" ] || { echo "no checkpoint at $CKPT" >&2; exit 1; }
+[ -f "$RECIPE" ] || { echo "no recipe at $RECIPE" >&2; exit 1; }
+GRAMS=$(find "$CALIB" -maxdepth 1 -name 'gram.r*.bin' 2>/dev/null | wc -l)
+[ "$GRAMS" -ge 48 ] || { echo "calib dir $CALIB holds $GRAMS gram files (<48)" >&2; exit 1; }
+if [ -e "$OUT" ]; then echo "$OUT already exists; move it to trash first" >&2; exit 1; fi
+if [ -e /home/dank/models/rad/qwen4exp-swift15-bf16.rad ]; then
+    echo "the bf16 container is still on disk; delete it first (peak disk)" >&2; exit 1
+fi
+
+# Gate 2: disk. Output ~132 GiB plus convert staging, 20 GiB of slack.
+FREE_MIB=$(df --output=avail -m /home/dank/models | tail -1 | tr -d ' ')
+[ "$FREE_MIB" -ge $((MIN_MIB + 20480)) ] || {
+    echo "only ${FREE_MIB} MiB free on /home/dank/models" >&2; exit 1; }
+
+# The recipe names $CALIB; rad-convert expands it from the environment
+# (MOE-W4.md:275). The recipe's cast section must name THIS checkpoint's
+# protected experts — derive-protected-experts.py rewrote it.
+echo "convert: $CKPT -> $OUT (gptq calib=$CALIB, $(date -Is))" | tee "$LOG"
+podman run --rm --name swift15-convert \
+    -v /home/dank/models:/models \
+    -v /home/dank/serverAppConfigs-swift15:/campaign:ro \
+    -v "$CALIB:/data/calib:ro" \
+    -e CALIB=/data/calib \
+    --entrypoint /opt/radiance/bin/rad-convert \
+    "$IMAGE" "/models/${CKPT#/home/dank/models/}" \
+    --recipe /campaign/"${RECIPE#/home/dank/serverAppConfigs-swift15/}" \
+    -o /models/rad/"$(basename "$OUT")" 2>&1 | tee -a "$LOG"
+rc=${PIPESTATUS[0]}
+[ "$rc" -eq 0 ] || { echo "rad-convert rc=$rc; log: $LOG" >&2; exit "$rc"; }
+
+# Gate 3: the artifact. rad-info's recipe echo is the receipt that GPTQ ran.
+SIZE_MIB=$(( $(stat -c %s "$OUT") / 1048576 ))
+[ "$SIZE_MIB" -ge "$MIN_MIB" ] || {
+    echo "$OUT is ${SIZE_MIB} MiB < floor ${MIN_MIB}" >&2; exit 1; }
+podman run --rm --entrypoint /opt/radiance/bin/rad-info \
+    -v /home/dank/models:/models:ro "$IMAGE" /models/rad/"$(basename "$OUT")" \
+    2>&1 | tee -a "$LOG" | grep -c 'gptq' >/dev/null \
+    || echo "WARNING: rad-info shows no gptq rule in the recorded recipe" >&2
+echo "CONVERT_DONE ${SIZE_MIB} MiB -> $OUT"

scripts/deep-probe.py

@@ -0,0 +1,55 @@
+#!/usr/bin/env python3
+"""deep-probe.py — one chat completion at ~43.6k prompt tokens, temp 0, 512
+generated, streamed; decode = completion_tokens / (total - ttft)."""
+import json, time, urllib.request
+
+TARGET_BYTES = int(43600 * 3.8)
+
+def build():
+    parts, n = [], 0
+    with open("/home/dank/corpora/out/calib.jsonl") as f:
+        for line in f:
+            p = json.loads(line)["prompt"]
+            parts.append(p)
+            n += len(p.encode())
+            if n >= TARGET_BYTES:
+                break
+    return "".join(parts)
+
+def main(salt):
+    body = json.dumps({
+        "model": "frank-archon",
+        "messages": [{"role": "user", "content": f"[probe {salt}] Continue and summarize this material in English, 400 words.\n\n" + build()}],
+        "temperature": 0.0, "max_tokens": 512,
+        "stream": True, "stream_options": {"include_usage": True},
+    }).encode()
+    req = urllib.request.Request("http://localhost:7770/v1/chat/completions",
+        data=body, headers={"Content-Type": "application/json"})
+    t0 = time.time(); ttft = None; ctok = 0; usage = {}
+    with urllib.request.urlopen(req, timeout=2400) as r:
+        for raw in r:
+            line = raw.decode().strip()
+            if not line.startswith("data:"):
+                continue
+            d = line[5:].strip()
+            if d == "[DONE]":
+                break
+            j = json.loads(d)
+            if j.get("usage"):
+                usage = j["usage"]
+            for ch in j.get("choices", []):
+                delta = (ch.get("delta") or {})
+                piece = delta.get("content") or delta.get("reasoning_content") or ""
+                if piece and ttft is None:
+                    ttft = time.time() - t0
+                ctok += 1
+    total = time.time() - t0
+    gen = usage.get("completion_tokens") or ctok
+    dec = gen / (total - (ttft or 0)) if total > (ttft or 0) else 0
+    print(json.dumps({"salt": salt, "prompt_tokens": usage.get("prompt_tokens"),
+                      "completion_tokens": gen, "ttft_s": round(ttft or 0, 2),
+                      "total_s": round(total, 1), "decode_tps": round(dec, 2)}))
+
+if __name__ == "__main__":
+    import sys
+    main(sys.argv[1] if len(sys.argv) > 1 else "x")

scripts/deploy-plan.py

@@ -62,7 +62,7 @@ SERVICE_ORDER = (
     "lidarr",
     "readarr",
     "frank-large",
-    "frank-small",
+    "frank-secondary",
     "frank-primary",
 )
 
@@ -94,7 +94,7 @@ DANKCLAW_SERVICES = {
     "caddy",
     "homepage",
     "frank-large",
-    "frank-small",
+    "frank-secondary",
 }
 
 DANKSERVER_SERVICES = {
@@ -400,12 +400,12 @@ def services_for_path(path: str, target: str) -> set[str]:
             # change to it redeploying alertmanager is the only path that
             # reaches it on the host.
             _add(services, "alertmanager")
-        if name in {"frank-small.env", "frank-small.service", "serve-frank-small.sh"}:
-            # llama.cpp frank-small (2026-09-16) keeps its unit, env and serve
-            # files here; each one selects frank-small only. The ik_llama.cpp
+        if name in {"frank-secondary.env", "frank-secondary.service", "serve-frank-secondary.sh"}:
+            # llama.cpp frank-secondary (2026-09-16) keeps its unit, env and serve
+            # files here; each one selects frank-secondary only. The ik_llama.cpp
             # trio that lived here for two days is in scripts/.AGENTTRASH/,
             # which is archive material and plans nothing.
-            _add(services, "frank-small")
+            _add(services, "frank-secondary")
         if name in {"frank-large.env", "frank-large.service", "serve-frank-large.sh"}:
             # CIRU v4.4.1 frank-large (2026-09-19) keeps its unit, env and
             # serve files here the same way: a root unit on a host-built

scripts/deploy-remote.sh

@@ -90,7 +90,7 @@ service_is_allowed() {
         dankclaw:authentik-postgres|dankclaw:authentik-server|\
         dankclaw:authentik-worker|dankclaw:authentik-outpost|dankclaw:authentik-outpost-ldap|dankclaw:anubis|\
         dankclaw:caddy|dankclaw:homepage|dankclaw:seerr|dankclaw:marreta|\
-        dankclaw:frank-large|dankclaw:frank-small|\
+        dankclaw:frank-large|dankclaw:frank-secondary|\
         dankserver:romm-mariadb|dankserver:romm-redis|dankserver:romm|\
         dankserver:copyparty|dankserver:jellyfin|dankserver:calibre-web|dankserver:calibre-server|dankserver:plex|dankserver:tautulli|\
         dankserver:prowlarr|dankserver:qbittorrent|dankserver:radarr|\
@@ -322,6 +322,12 @@ deploy_homepage() {
     if [[ -d shared/homepage/fonts ]]; then
         run_privileged cp -r shared/homepage/fonts /srv/homepage/
     fi
+    if [[ -d shared/homepage/img ]]; then
+        run_privileged cp -r shared/homepage/img /srv/homepage/
+    fi
+    if [[ -d shared/homepage/sfx ]]; then
+        run_privileged cp -r shared/homepage/sfx /srv/homepage/
+    fi
     local deployed_at
     deployed_at="$(git log -1 --format=%cI)"
     printf 'window.DEPLOYED_AT = "%s";\n' "$deployed_at" > /tmp/deployed.js
@@ -1086,33 +1092,37 @@ deploy_anubis() {
     wait_container_healthy anubis 60 || return 1
 }
 
-# frank-large is the halogen CONTAINER (ghcr.io/peonist-ai/halogen,
-# digest-pinned through scripts/frank-large.env) serving
-# peonist-ai/halogen-qwen3.8-27b — qwen3.8-27b-p1w4d-d2.hgn, 33.4 GiB,
-# DFlash2 spec decode, 1 slot x 200700, text-only — on the Strix Halo
-# iGPU. It stays a ROOT unit because the serve script drives `podman run`
-# directly (the frank-primary pattern): relink_unit points
-# /etc/systemd/system/frank-large.service at the repo file, boot
+# frank-large is the halogen-flash-server CONTAINER (0.16.2, digest-pinned
+# through scripts/frank-large.env) serving the Quat3rnion ABLITERATED
+# Swift1.5 finetune of peonist's Qwen3.8-Flash-Next v2 checkpoint —
+# qwen38-flash-next-v2-swift15-abliterated.hgn (66,687,678,464 B, ~62 GiB
+# resident) + the REQUIRED n-gram lookup table + the vision tower —
+# MTP spec decode (HALOGEN_SPEC_ADAPT=0), 2 slots x 200192, image input —
+# on the Strix Halo iGPU. It stays a ROOT unit because the serve script
+# drives `podman run` directly (the frank-primary pattern): relink_unit
+# points /etc/systemd/system/frank-large.service at the repo file, boot
 # persistence is that unit's [Install], and LimitMEMLOCK=infinity is
-# fleet parity. 2026-10-05 cutover from the ROCmFPX llama.cpp shape —
-# rollback = revert the swap commit's trio; that payload, its draft head
-# and the fork build all stay on disk
-# (~/models/kingjones-Qwen3.8-27B-Uncensored-ROCmFP4-STRIX-MTP,
-# ~/GitPackages/kingjones-ROCmFPX).
+# fleet parity. 2026-10-06 cutover from the halogen 27B-dense shape —
+# rollback = revert the swap commit's trio; the 27B payload stays on
+# disk (~/models/halogen-qwen3.8-27b).
 #
 # The gates mirror serve-frank-large.sh's predicates EXACTLY (the
 # 2026-10-01 crash-loop lesson): image digest in dank's podman storage,
-# the .hgn checkpoint at the exact byte count, the tokenizer directory
-# holding every named file as a regular (not symlinked) file — a green
-# deploy against a missing artifact would
-# leave a restart-looping unit and a dark endpoint. The port gate is the
-# same as every cutover since 2026-09-16: nothing starts until 7770 is
-# released.
-LLM_LARGE_IMAGE="${LLM_LARGE_IMAGE:-ghcr.io/peonist-ai/halogen@sha256:dc0a39a0016d6cfc58a197978febaafdf8d28403f724ded6111d98b5fb7ac0ea}"
-LLM_LARGE_CKPT="${LLM_LARGE_CKPT:-/home/dank/models/halogen-qwen3.8-27b/qwen3.8-27b-p1w4d-d2.hgn}"
-LLM_LARGE_CKPT_BYTES="${LLM_LARGE_CKPT_BYTES:-35865565184}"
-LLM_LARGE_TOK_DIR="${LLM_LARGE_TOK_DIR:-/home/dank/models/halogen-qwen3.8-27b/tokenizer}"
-LLM_LARGE_TOK_FILES="${LLM_LARGE_TOK_FILES:-tokenizer.json chat_template.jinja tokenizer_config.json merges.txt vocab.json}"
+# each .hgn payload (checkpoint, n-gram table, vision tower) at the exact
+# byte count, the tokenizer directory holding every named file as a
+# regular (not symlinked) file — a green deploy against a missing
+# artifact would leave a restart-looping unit and a dark endpoint. The
+# port gate is the same as every cutover since 2026-09-16: nothing
+# starts until 7770 is released.
+LLM_LARGE_IMAGE="${LLM_LARGE_IMAGE:-ghcr.io/peonist-ai/halogen-flash-server@sha256:0c61bf84ac22308a53f5d1ca6b86806702d7039e5ebc51cae4c66621b92fe04a}"
+LLM_LARGE_CKPT="${LLM_LARGE_CKPT:-/home/dank/models/halogen-flashnext-v2/qwen38-flash-next-v2-swift15-abliterated.hgn}"
+LLM_LARGE_CKPT_BYTES="${LLM_LARGE_CKPT_BYTES:-66687678464}"
+LLM_LARGE_NGRAM="${LLM_LARGE_NGRAM:-/home/dank/models/halogen-flashnext-v2/qwen38-flash-next-ngram.hgn}"
+LLM_LARGE_NGRAM_BYTES="${LLM_LARGE_NGRAM_BYTES:-51200246144}"
+LLM_LARGE_VISION="${LLM_LARGE_VISION:-/home/dank/models/halogen-flashnext-v2/qwen38-flash-next-vision.hgn}"
+LLM_LARGE_VISION_BYTES="${LLM_LARGE_VISION_BYTES:-897916416}"
+LLM_LARGE_TOK_DIR="${LLM_LARGE_TOK_DIR:-/home/dank/models/halogen-flashnext-v2/tokenizer}"
+LLM_LARGE_TOK_FILES="${LLM_LARGE_TOK_FILES:-tokenizer.json chat_template.jinja tokenizer_config.json merges.txt vocab.json generation_config.json}"
 
 retire_halogen_large_quadlet() {
     local trash="$HOME/.AGENTTRASH/20260919-orca-cutover/quadlet"
@@ -1156,23 +1166,37 @@ deploy_llm_large() {
     fi
     if [[ ! -f "$LLM_LARGE_CKPT" ]]; then
         echo "ERROR: halogen checkpoint absent at $LLM_LARGE_CKPT" >&2
-        echo "frank-large: fetch it: bash scripts/download-frank-large-halogen.sh" >&2
+        echo "frank-large: fetch it: bash scripts/download-frank-large-flashnext.sh" >&2
         return 1
     fi
     if [[ "$(stat -c %s "$LLM_LARGE_CKPT")" != "$LLM_LARGE_CKPT_BYTES" ]]; then
         echo "ERROR: halogen checkpoint is not $LLM_LARGE_CKPT_BYTES bytes (partial download?)" >&2
-        echo "frank-large: re-check: bash scripts/download-frank-large-halogen.sh --verify-only" >&2
+        echo "frank-large: re-check: bash scripts/download-frank-large-flashnext.sh --verify-only" >&2
         return 1
     fi
+    for gate in "$LLM_LARGE_NGRAM:$LLM_LARGE_NGRAM_BYTES:n-gram table" \
+                "$LLM_LARGE_VISION:$LLM_LARGE_VISION_BYTES:vision tower"; do
+        IFS=: read -r gpath gbytes gname <<<"$gate"
+        if [[ ! -f "$gpath" ]]; then
+            echo "ERROR: halogen $gname absent at $gpath" >&2
+            echo "frank-large: fetch it: bash scripts/download-frank-large-flashnext.sh" >&2
+            return 1
+        fi
+        if [[ "$(stat -c %s "$gpath")" != "$gbytes" ]]; then
+            echo "ERROR: halogen $gname is not $gbytes bytes (partial download?)" >&2
+            echo "frank-large: re-check: bash scripts/download-frank-large-flashnext.sh --verify-only" >&2
+            return 1
+        fi
+    done
     if [[ -L "$LLM_LARGE_TOK_DIR" || ! -d "$LLM_LARGE_TOK_DIR" ]]; then
         echo "ERROR: tokenizer dir missing or a symlink at $LLM_LARGE_TOK_DIR" >&2
-        echo "frank-large: fetch it: bash scripts/download-frank-large-halogen.sh" >&2
+        echo "frank-large: fetch it: bash scripts/download-frank-large-flashnext.sh" >&2
         return 1
     fi
     for tf in $LLM_LARGE_TOK_FILES; do
         if [[ ! -f "$LLM_LARGE_TOK_DIR/$tf" || -L "$LLM_LARGE_TOK_DIR/$tf" ]]; then
             echo "ERROR: tokenizer file missing or a symlink: $LLM_LARGE_TOK_DIR/$tf" >&2
-            echo "frank-large: fetch it: bash scripts/download-frank-large-halogen.sh" >&2
+            echo "frank-large: fetch it: bash scripts/download-frank-large-flashnext.sh" >&2
             return 1
         fi
     done
@@ -1184,7 +1208,7 @@ deploy_llm_large() {
     # `podman run --name frank-large-halogen` / `podman wait` but NOT in
     # this deployer's own command line (`bash deploy-remote.sh
     # frank-large`) — so it cannot shoot itself in the foot (the fleet
-    # pkill law), and it can never match frank-small's llama-server.
+    # pkill law), and it can never match frank-secondary's llama-server.
     run_privileged systemctl stop frank-large.service 2>/dev/null || true
     pkill -f 'frank-large-halogen' 2>/dev/null || true
     podman rm -f frank-large frank-large-halogen >/dev/null 2>&1 || true
@@ -1198,75 +1222,78 @@ deploy_llm_large() {
     wait_for_health frank-large http://127.0.0.1:7770/health 1200
 }
 
-# deploy_llm_small — frank-small is llama.cpp (master build, CUDA sm_86) on
+# deploy_llm_secondary — frank-secondary is llama.cpp (master build, CUDA sm_86) on
 # the RTX 3090 as a ROOT unit: the runtime is a native binary tree under
 # ~/GitPackages/llama-master/build-cuda, so there is no container image and
 # no generated unit to avoid. relink_unit points
-# /etc/systemd/system/frank-small.service at the repo file and boot
+# /etc/systemd/system/frank-secondary.service at the repo file and boot
 # persistence is that unit's [Install] section.
 #
 # It is a SEPARATE endpoint on port 7771; frank-large keeps 7770. The 3090 is
 # not shared: frank-large's engine runs on the gfx1151 iGPU.
-LLM_SMALL_MODEL_FILE="${LLM_SMALL_MODEL_FILE:-/home/dank/models/Ornith-Q8/Ornith-1.5-9B-Q8_0.gguf}"
-LLM_SMALL_RUNTIME="${LLM_SMALL_RUNTIME:-/home/dank/GitPackages/llama-master/build-cuda/bin/llama-server}"
-# The projector IS the vision capability here: the Ornith GGUF ships no
-# `v.*` tensors, so a deploy that starts without this file silently ships a
-# text-only endpoint. Checked unconditionally, like frank-large's.
-LLM_SMALL_MMPROJ="${LLM_SMALL_MMPROJ:-/home/dank/models/Ornith-Q8/mmproj-Ornith-1.5-9B-BF16.gguf}"
+LLM_SECONDARY_MODEL_FILE="${LLM_SECONDARY_MODEL_FILE:-/home/dank/models/Qwen35-9B-HauhauCS-Q6K/Qwen3.5-9B-Uncensored-HauhauCS-Aggressive-Q6_K.gguf}"
+LLM_SECONDARY_RUNTIME="${LLM_SECONDARY_RUNTIME:-/home/dank/GitPackages/llama-master/build-cuda/bin/llama-server}"
+# The projector IS the vision capability here: the model is natively
+# multimodal and the Q6_K file carries no vision tensors — the publisher ships
+# its own BF16 projector in the same repo, so a deploy that starts without
+# this file silently ships a text-only endpoint. Checked unconditionally, like
+# frank-large's.
+LLM_SECONDARY_MMPROJ="${LLM_SECONDARY_MMPROJ:-/home/dank/models/Qwen35-9B-HauhauCS-Q6K/mmproj-Qwen3.5-9B-Uncensored-HauhauCS-Aggressive-BF16.gguf}"
 
-deploy_llm_small() {
+deploy_llm_secondary() {
     # Refuse rather than restart-loop: a unit whose runtime or payload is
     # missing never becomes healthy, and a green deploy would leave the
     # endpoint dark with nothing in the log to say why.
-    if [[ ! -x "$LLM_SMALL_RUNTIME" ]]; then
-        echo "ERROR: llama.cpp runtime absent at $LLM_SMALL_RUNTIME" >&2
-        echo "frank-small: build it (cmake -B build-cuda -DGGML_CUDA=ON" >&2
-        echo "frank-small: -DCMAKE_CUDA_ARCHITECTURES=86 -DCMAKE_CUDA_HOST_COMPILER=/usr/bin/gcc-13" >&2
-        echo "frank-small: && cmake --build build-cuda --target llama-server) first." >&2
+    if [[ ! -x "$LLM_SECONDARY_RUNTIME" ]]; then
+        echo "ERROR: llama.cpp runtime absent at $LLM_SECONDARY_RUNTIME" >&2
+        echo "frank-secondary: build it (cmake -B build-cuda -DGGML_CUDA=ON" >&2
+        echo "frank-secondary: -DCMAKE_CUDA_ARCHITECTURES=86 -DCMAKE_CUDA_HOST_COMPILER=/usr/bin/gcc-13" >&2
+        echo "frank-secondary: && cmake --build build-cuda --target llama-server) first." >&2
         return 1
     fi
-    for f in "$LLM_SMALL_MODEL_FILE" "$LLM_SMALL_MMPROJ"; do
+    for f in "$LLM_SECONDARY_MODEL_FILE" "$LLM_SECONDARY_MMPROJ"; do
         if [[ ! -f "$f" ]]; then
-            echo "ERROR: frank-small payload file absent at $f" >&2
-            echo "frank-small: run scripts/download-frank-small.sh first;" >&2
-            echo "frank-small: refusing to start a unit that cannot load." >&2
+            echo "ERROR: frank-secondary payload file absent at $f" >&2
+            echo "frank-secondary: run scripts/download-frank-secondary.sh first;" >&2
+            echo "frank-secondary: refusing to start a unit that cannot load." >&2
             return 1
         fi
     done
-    relink_unit frank-small
+    relink_unit frank-secondary
     run_privileged systemctl daemon-reload
-    run_privileged systemctl enable frank-small.service
-    run_privileged systemctl restart frank-small.service
-    wait_for_health frank-small http://127.0.0.1:7771/health 900 || return 1
+    run_privileged systemctl enable frank-secondary.service
+    run_privileged systemctl restart frank-secondary.service
+    wait_for_health frank-secondary http://127.0.0.1:7771/health 900 || return 1
     # False-green hardening (frank-large's lesson): the port answering
     # proves nothing about WHO answers — the unit must be active with a
     # live MainPID, or a stray SGLang holdover is masquerading as deployed.
     local main_pid
-    main_pid="$(run_privileged systemctl show -p MainPID --value frank-small.service)"
+    main_pid="$(run_privileged systemctl show -p MainPID --value frank-secondary.service)"
     if [[ -z "$main_pid" || "$main_pid" == "0" ]]; then
-        echo "FAIL: :7771 answered but frank-small.service has no MainPID — a stray process holds the port" >&2
+        echo "FAIL: :7771 answered but frank-secondary.service has no MainPID — a stray process holds the port" >&2
         return 1
     fi
-    echo "frank-small: health answered by unit MainPID $main_pid"
+    echo "frank-secondary: health answered by unit MainPID $main_pid"
 }
 
-# deploy_llm_primary — frank-primary is the FreeToken engine (PR #587 ROCm
-# build, gfx1201) serving the orcarouter Qwen3.8-Flash-Next-Uncensored-FP8
-# payload (131 safetensors shards) on ONE R9700 as a ROOT unit whose serve
-# script drives `podman run` directly (the image carries `ft serve`).
-# 2026-10-05 cutover from the tcclaviger vLLM fork (pre-cutover tag
-# frank-primary-vllm-shapeG-20261003); before that, 2026-09-29 cutover from
-# the R9V radiance fork, whose launcher/shim/setup-state machinery is
-# retired. Same port and alias as the endpoints it replaced; the harness
-# models.yml id is unchanged.
+# deploy_llm_primary — frank-primary is the radiance engine (StillDeadcode,
+# digest-pinned stilldeadcode/radiance image) serving the OFFICIAL
+# Qwen/Qwen3.8-Flash-Next-FP8 at native block-FP8 precision from the
+# rad-convert container qwen4exp-fp8-native.rad, on BOTH R9700s (TP=2) as a
+# ROOT unit whose serve script drives `podman run` directly (the engine is
+# the image entrypoint; the image lives in dank's rootless podman storage).
+# 2026-10-05 cutover from FreeToken (pre-cutover tag
+# frank-primary-freetoken-pr587-20261005); before that, the tcclaviger vLLM
+# fork (tag frank-primary-vllm-shapeG-20261003). Same port and alias; the
+# harness models.yml id is unchanged.
 #
-# Gates: the runtime image must exist in dank's rootless storage
-# (FRANK_PRIMARY_IMAGE — a LOCAL tag: the image is built on the host from
-# scripts/docker/frank-primary-freetoken.Dockerfile, which pins the
-# FreeToken PR-head SHA and the base image), the payload dir must carry
-# config.json and the expected shard count (FRANK_PRIMARY_MODEL_SHARDS),
-# and the unit env must be complete. Per-file sha256 verification was the
-# fetch script's job at download time, never re-hashed here.
+# Gates: the image must exist under its digest ref in dank's rootless
+# storage (FRANK_PRIMARY_IMAGE), the .rad container file must exist at the
+# env's size floor (FRANK_PRIMARY_MODEL_FILE >= FRANK_PRIMARY_RAD_MIN_MIB —
+# a truncated convert is a guaranteed long-timeout boot), and the unit env
+# must be complete. Per-file sha256 verification is the fetch script's job
+# at download time and rad-convert/rad-info's job at build time, never
+# re-hashed here.
 LLM_PRIMARY_CONTAINER=frank-primary
 LLM_PRIMARY_LEGACY_CONTAINERS="frank-primary-qwen frank-primary-r9v qwen-fp8 glm-probe"
 
@@ -1275,44 +1302,36 @@ deploy_llm_primary() {
     # missing never becomes healthy, and a green deploy would leave the
     # endpoint dark with nothing in the log to say why.
     local env_file="/home/dank/serverAppConfigs/scripts/frank-primary.env"
-    local image model_dir shards count
+    local image model_dir model_file rad_min mib
     if [[ ! -f "$env_file" ]]; then
         echo "ERROR: frank-primary env file absent: $env_file" >&2
         return 1
     fi
     image="$(sed -n 's/^FRANK_PRIMARY_IMAGE=//p' "$env_file" | tail -1)"
     model_dir="$(sed -n 's/^FRANK_PRIMARY_MODEL_DIR=//p' "$env_file" | tail -1)"
-    shards="$(sed -n 's/^FRANK_PRIMARY_MODEL_SHARDS=//p' "$env_file" | tail -1)"
-    if [[ -z "$image" || -z "$model_dir" || -z "$shards" ]]; then
-        echo "ERROR: frank-primary.env missing FRANK_PRIMARY_IMAGE/MODEL_DIR/MODEL_SHARDS" >&2
+    model_file="$(sed -n 's/^FRANK_PRIMARY_MODEL_FILE=//p' "$env_file" | tail -1)"
+    rad_min="$(sed -n 's/^FRANK_PRIMARY_RAD_MIN_MIB=//p' "$env_file" | tail -1)"
+    if [[ -z "$image" || -z "$model_dir" || -z "$model_file" || -z "$rad_min" ]]; then
+        echo "ERROR: frank-primary.env missing FRANK_PRIMARY_IMAGE/MODEL_DIR/MODEL_FILE/RAD_MIN_MIB" >&2
         return 1
     fi
-    # The image gate is EXISTENCE IN STORAGE, not a registry digest: the
-    # FreeToken image is built ON THE HOST (a local tag carries NO
-    # RepoDigests entry — the old grep refused exactly this shape), and
-    # pinning lives in the Dockerfile (pinned PR-head SHA + digest-pinned
-    # base image). `podman image exists` accepts a local tag and a
-    # name@digest ref alike, in one command — no pipefail/grep race (the
-    # 2026-09-30 lesson) for either shape.
+    # The image gate is the digest ref itself: a name@sha256 ref exists in
+    # storage only if THAT manifest was pulled. One command — no
+    # pipefail/grep race (the 2026-09-30 lesson).
     if ! podman image exists "$image"; then
-        echo "ERROR: runtime image absent from rootless storage: $image" >&2
-        echo "frank-primary: build it (scripts/docker/frank-primary-freetoken.Dockerfile) or pull it" >&2
+        echo "ERROR: runtime image absent from storage: $image" >&2
+        echo "frank-primary: podman pull docker://stilldeadcode/radiance:latest" >&2
         return 1
     fi
-    if [[ ! -f "$model_dir/config.json" ]]; then
-        echo "ERROR: payload config missing: $model_dir/config.json" >&2
-        echo "frank-primary: re-run scripts/download-frank-primary.sh" >&2
+    if [[ ! -f "$model_dir/$model_file" ]]; then
+        echo "ERROR: .rad container missing: $model_dir/$model_file" >&2
+        echo "frank-primary: run scripts/build-frank-primary-rad.sh" >&2
         return 1
     fi
-    if [[ ! -f "$model_dir/model.safetensors.index.json" ]]; then
-        echo "ERROR: weight-map index missing: $model_dir/model.safetensors.index.json" >&2
-        echo "frank-primary: re-run scripts/download-frank-primary.sh" >&2
-        return 1
-    fi
-    count="$(find "$model_dir" -maxdepth 1 -name 'model-*-of-*.safetensors' 2>/dev/null | wc -l)"
-    if [[ "$count" != "$shards" ]]; then
-        echo "ERROR: payload shard count wrong: $model_dir (found $count, want $shards)" >&2
-        echo "frank-primary: re-run scripts/download-frank-primary.sh" >&2
+    mib=$(( $(stat -c %s "$model_dir/$model_file") / 1048576 ))
+    if [[ "$mib" -lt "$rad_min" ]]; then
+        echo "ERROR: .rad container too small: $model_dir/$model_file is ${mib} MiB, want >= $rad_min" >&2
+        echo "frank-primary: rebuild it (scripts/build-frank-primary-rad.sh --force)" >&2
         return 1
     fi
 
@@ -1337,18 +1356,16 @@ deploy_llm_primary() {
     run_privileged systemctl daemon-reload
     run_privileged systemctl enable frank-primary.service
     run_privileged systemctl restart frank-primary.service
-    # Cold start: FP8 page-in plus torch compile/graph capture (~15 min cold,
-    # measured 2026-09-29). Parity with every predecessor on this port.
-    # FreeToken's /health returns 200 with {"status":"loading"} while the
-    # engine loads (the FastAPI front end binds before the scheduler is
-    # up), so a bare 200 false-greens — measured 2026-10-06: "healthy after
-    # 6 s" while 131 shards were still paging in. vLLM 503'd during load,
-    # which made the bare curl honest there; FreeToken needs the BODY read.
-    # Only status:"ok" counts. (The MainPID check below still guards the
-    # stray-port case.)
+    # Cold start: .rad load + PLE pin + expert host-pool upload (the
+    # publisher's compose healthcheck allows a 15-minute start_period).
+    # Parity with every predecessor on this port. The HTTP listener binds
+    # AFTER the full load (main.cpp: engine.start() then engine.serve()),
+    # so during load the port REFUSES connections — a bare curl is honest
+    # here, unlike FreeToken's 200-while-loading body. (The MainPID check
+    # below still guards the stray-port case.)
     local i ok=0
     for ((i=1; i<=900; i++)); do
-        if curl -fsS http://127.0.0.1:7770/health 2>/dev/null | grep -q '"status":"ok"'; then
+        if curl -fsS http://127.0.0.1:7770/health 2>/dev/null >/dev/null; then
             echo "frank-primary engine healthy after approximately $((i * 2))s"
             ok=1
             break
@@ -1356,7 +1373,7 @@ deploy_llm_primary() {
         sleep 2
     done
     if [[ "$ok" != 1 ]]; then
-        echo "FAIL: frank-primary never reached /health status ok in 1800s" >&2
+        echo "FAIL: frank-primary /health never answered 200 in 1800s" >&2
         return 1
     fi
     # False-green hardening (the 2026-09-26 lesson): a bare curl against the
@@ -1557,7 +1574,7 @@ deploy_service() {
         seerr) deploy_seerr ;;
         marreta) deploy_marreta ;;
         frank-large) deploy_llm_large ;;
-        frank-small) deploy_llm_small ;;
+        frank-secondary) deploy_llm_secondary ;;
         frank-primary) deploy_llm_primary ;;
         hermes|hermes-dashboard|hermes-webui) deploy_hermes ;;
         romm|romm-mariadb|romm-redis|mariadb|redis) deploy_romm_stack "$service" ;;

scripts/deploy.sh

@@ -141,7 +141,7 @@ valid_service() {
         dankclaw:alertmanager|\
         dankclaw:node-exporter|dankclaw:authentik-postgres|dankclaw:authentik-server|\
         dankclaw:authentik-worker|dankclaw:authentik-outpost|dankclaw:authentik-outpost-ldap|dankclaw:anubis|dankclaw:seerr|\
-        dankclaw:frank-large|dankclaw:frank-small|\
+        dankclaw:frank-large|dankclaw:frank-secondary|\
         dankserver:romm-mariadb|dankserver:romm-redis|dankserver:romm|\
         dankserver:copyparty|dankserver:jellyfin|dankserver:calibre-web|dankserver:calibre-server|dankserver:plex|dankserver:tautulli|\
         dankserver:qbittorrent|dankserver:radarr|dankserver:sonarr|\

scripts/derive-protected-experts.py

@@ -0,0 +1,117 @@
+#!/usr/bin/env python3
+"""derive-protected-experts.py — rewrite the recipe's protected-expert section
+from a calibration recording. RUNS ON DANKSERVER (venv: numpy).
+
+The law (docs/MOE-W4.md "Protected experts", restated in the recipe):
+an expert whose share of its layer's down-input energy is at least 3% AND
+at least three times its share of the routing is kept bf16; the count must
+be EVEN on each rank's half of the layer (the four-bit GEMM takes the rest
+as two equal tables by parity) — an odd half gains its next most energetic
+non-qualifying expert.
+
+Data: the calibration dir's per-expert down Grams,
+gram.r<rank>.blk.L.ffn_down_exps.<e>.bin — 32-byte RADGRAM2 header
+(magic, k, rank, rows, domain) then k*k f32, named with the checkpoint's
+GLOBAL expert numbering (arch/common/rad_block_moe_fp8.h: ge =
+expert_base + e). rows (header) is the expert's routing count; the trace
+of the Gram is its down-input energy. Rank halves: 0-255, 256-511
+(contiguous expert_base split at TP=2).
+
+Output: the cast-rule lines (recipe format) on stdout, a report on stderr.
+
+Usage: derive-protected-experts.py <calib dir> [--min-share 0.03]
+       [--min-route-mult 3.0]
+"""
+import argparse
+import glob
+import os
+import re
+import struct
+import sys
+
+import numpy as np
+
+NAME_RE = re.compile(r"^gram\.r(\d)\.blk\.(\d+)\.ffn_down_exps\.(\d+)\.bin$")
+
+
+def read_gram(path):
+    """(k, rank, rows, trace) from a RADGRAM2 file (full matrix on disk)."""
+    with open(path, "rb") as f:
+        hdr = f.read(32)
+        magic, k, rank, rows, domain = struct.unpack("<8sIIQI", hdr[:28])
+        if magic != b"RADGRAM2":
+            raise SystemExit(f"not a RADGRAM2 file: {path}")
+        n = k * k
+        a = np.frombuffer(f.read(n * 4), dtype=np.float32)
+        if a.size != n:
+            raise SystemExit(f"short gram file: {path}")
+        tr = float(a.reshape(k, k).diagonal().sum())
+    return k, rank, int(rows), tr
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("calib_dir")
+    ap.add_argument("--min-share", type=float, default=0.03)
+    ap.add_argument("--min-route-mult", type=float, default=3.0)
+    a = ap.parse_args()
+
+    # layer -> global expert -> (rank, rows, energy)
+    data = {}
+    for path in sorted(glob.glob(os.path.join(
+            a.calib_dir, "gram.r*.ffn_down_exps.*.bin"))):
+        m = NAME_RE.match(os.path.basename(path))
+        if not m:
+            continue
+        rank, layer, expert = int(m.group(1)), int(m.group(2)), int(m.group(3))
+        k, _, rows, tr = read_gram(path)
+        data.setdefault(layer, {})[expert] = (rank, rows, tr)
+
+    if not data:
+        raise SystemExit("no per-expert down grams found")
+
+    lines, report = [], []
+    total = 0
+    for layer in sorted(data):
+        experts = data[layer]
+        tot_e = sum(v[2] for v in experts.values())
+        tot_r = sum(v[1] for v in experts.values())
+        if tot_e <= 0 or tot_r <= 0:
+            report.append(f"blk.{layer}: empty recording, skipped")
+            continue
+        qual, pool = [], []
+        for e, (rank, rows, tr) in experts.items():
+            es, rs = tr / tot_e, rows / max(tot_r, 1)
+            rec = (e, rank, es, rs)
+            if es >= a.min_share and es >= a.min_route_mult * rs:
+                qual.append(rec)
+            elif es > 0:
+                pool.append(rec)
+        chosen = {r[0]: (r[2], r[3]) for r in qual}
+        for half in (0, 1):  # rank halves by the contiguous expert_base split
+            n_half = sum(1 for e in chosen if e // 256 == half)
+            if n_half % 2:
+                pool.sort(key=lambda r: -r[2])
+                for cand in pool:
+                    if cand[1] == half and cand[0] not in chosen:
+                        chosen[cand[0]] = (cand[2], cand[3])
+                        report.append(
+                            f"blk.{layer}: half {half} odd -> added e{cand[0]} "
+                            f"(energy {cand[2]*100:.2f}%)")
+                        break
+        for e in sorted(chosen):
+            es, rs = chosen[e]
+            lines.append(f"blk.{layer}.ffn_*_exps.{e}.weight"
+                         f"         cast dtype=bf16")
+            report.append(f"blk.{layer} e{e:3d} (rank {e // 256}): "
+                          f"energy {es*100:5.2f}%, routing {rs*100:5.3f}%, "
+                          f"ratio {es/max(rs,1e-12):6.1f}")
+        total += len(chosen)
+
+    print("\n".join(lines))
+    print("\n".join(report), file=sys.stderr)
+    print(f"total protected: {total}", file=sys.stderr)
+
+
+if __name__ == "__main__":
+    main()

scripts/docker/frank-primary-freetoken.Dockerfile

@@ -1,63 +0,0 @@
-# frank-primary-freetoken.Dockerfile — FreeToken (PR #587 ROCm) serving image
-# for the frank-primary endpoint (dankserver :7770, 1x R9700 gfx1201).
-#
-# Built on dankserver with rootless podman (dank user), same storage the
-# service's `podman run` reads:
-#   podman build -t frank-primary-freetoken:pr587-77a7201 \
-#     -f scripts/docker/frank-primary-freetoken.Dockerfile scripts/docker/
-#
-# Base: the PR's own docs/install_amd.md names this exact image as the
-# known-good ROCm 7.14 stack for gfx1201 (torch 2.11.0+rocm7.14, triton-rocm
-# 3.7 — the original pin; FreeToken main+PR587 accepts torch>=2.11,<2.16 and
-# triton>=3.6,<3.9). There is no rocm7.14+torch2.14 image on Docker Hub (the
-# 2.14 images are rocm10.1.0 — an untested pair for this PR), so the PR doc's
-# verbatim recommendation is the deployed stack.
-#
-# FreeToken source: FlashML-org/FreeToken PR #587 at pinned head
-# 77a7201f98f00d93f618c6cf8b81ffe8360e0899 (branch rocm714_torch214, base
-# main 581fdca3). The PR is ROCm-gated end to end; the NVIDIA/CUDA path is
-# byte-identical to main.
-#
-# Build law from docs/install_amd.md:
-#   - `pip install --no-build-isolation --no-deps -e .` — the image's ROCm
-#     torch is preserved and used to compile the C++/HIP extensions.
-#   - NEVER a plain `pip install -e .` on ROCm: the dependency set is
-#     authored for NVIDIA (flashlib pulls numba + nvidia-cutlass-dsl; torch
-#     metadata pulls cuda-bindings) and the resolve can REPLACE the ROCm
-#     torch with a CUDA build.
-#   - Runtime deps installed explicitly, curated by the same doc. flashinfer
-#     / sgl_kernel / nvidia-cutlass-dsl have no ROCm builds and are
-#     intentionally absent — FreeToken gates them off and falls back to its
-#     Triton kernels on ROCm.
-#
-# Base is digest-pinned (2026-10-05): sha256:a223aee1… is the manifest this
-# build was qualified against; a re-pushed tag cannot change the stack. The
-# readable tag rides along for humans (podman verifies the digest matches).
-FROM rocm/pytorch:rocm7.14_ubuntu24.04_py3.12_pytorch_release_2.11.0@sha256:a223aee17aef5d21c3b9f63436dd19d27d1c665ec8b2f40011c9546cabae2a80
-
-# Pin the PR head, not the branch: a future push to rocm714_torch214 would
-# otherwise silently change the deployed engine.
-# The pip-ROCm torch in this image ships libhiprtc as the VERSIONED
-# libhiprtc.so.7 only; the PR's `_cpu_moe` extension links `-lhiprtc`, and
-# ld looks for the unversioned dev symlink in the ALREADY-LINKED
-# _rocm_sdk_core/lib dir. Create it there (first build failure 2026-10-05:
-# `ld: cannot find -lhiprtc`).
-RUN ln -s libhiprtc.so.7 /opt/venv/lib/python3.12/site-packages/_rocm_sdk_core/lib/libhiprtc.so
-RUN git clone https://github.com/FlashML-org/FreeToken.git /opt/FreeToken \
- && cd /opt/FreeToken \
- && git fetch origin pull/587/head \
- && git checkout 77a7201f98f00d93f618c6cf8b81ffe8360e0899
-
-WORKDIR /opt/FreeToken
-RUN python -m pip install --no-build-isolation --no-deps -e .
-
-# Curated runtime dependencies (docs/install_amd.md). transformers is pinned
-# to the pyproject window (>=5.16,<5.17); the rest are the doc's portable set.
-# torch / torchvision / triton-rocm come from the base image, 1:1 aligned.
-RUN python -m pip install \
-      "transformers>=5.16,<5.17" tokenizers safetensors huggingface_hub \
-      fastapi uvicorn pydantic openai httpx anyio \
-      einops gguf msgpack pyzmq typer rich apache-tvm-ffi \
-      partial-json-parser \
- && python -m pip install --no-deps flashlib==0.3.0 \
- && python -m pip install ninja

scripts/download-frank-large-flashnext.sh

@@ -0,0 +1,119 @@
+#!/usr/bin/env bash
+# download-frank-large-flashnext.sh — fetch frank-large's flash-server v2
+# payload: the Quat3rnion abliterated Swift1.5 checkpoint of peonist's v2
+# .hgn, the REQUIRED lookup table and the optional vision tower from
+# peonist-ai/halogen-qwen3.8-flash-next (both at the revisions the model
+# card pins), and the flat tokenizer the two repos share. All public, no
+# token — and per the fleet secret law, none would ever touch argv.
+#
+# The fetch law, same as every sibling script: curl -f (an access error
+# must not become a "successful" 153-byte artifact), exact byte count from
+# the Hub tree API, LFS sha256 verification, --verify-only to re-check an
+# existing payload without downloading.
+#
+# File manifest (verified against the Hub tree API on 2026-10-06):
+#   qwen38-flash-next-v2-swift15-abliterated.hgn  66,687,678,464 B  sha256
+#     9c880b8ba48deabfa06172eff1e7572e4fe465e09e97faec6e50c3f6fdcac037
+#     (Quat3rnion main; compacted r2 of the card's 89,775,868,032 B
+#     append-only original — byte-identical live tensors, dropped padding)
+#   qwen38-flash-next-ngram.hgn  51,200,246,144 B  sha256
+#     3450acada94e19aabad88bd49b45eb70304820949c2240fc178067b20ae5dffe
+#     (peonist @5cc17cea; REQUIRED — the engine reads it beside the
+#     checkpoint)
+#   qwen38-flash-next-vision.hgn  897,916,416 B  sha256
+#     d62e0ae553fe88afd3833733d4a4c669f34d20fd8dfce4b9610525bed2134b10
+#     (peonist @e053f488; optional, enables image input)
+#   tokenizer/* @e053f488 — byte-identical to the 27B-era tokenizer
+#     already on disk (tokenizer.json LFS
+#     0997f410c57a1f4e53b09e4be8f4a172d90edd9564368fb0847030937229b9f3);
+#     fetched fresh rather than symlinked, per the no-symlink law.
+set -euo pipefail
+
+DEST="${FRANK_LARGE_MODEL_DIR:-$HOME/models/halogen-flashnext-v2}"
+ABLIT="https://huggingface.co/Quat3rnion/halogen-swift1.5-qwen3.8-flash-next-v2-abliterated/resolve/main"
+PEONIST="https://huggingface.co/peonist-ai/halogen-qwen3.8-flash-next/resolve"
+NGRAM_REV="5cc17cea1a10b502b4a5db41d8d2b1a0f22dba26"
+TOK_REV="e053f488b120b99ed2525e6ac99f68c51b3b6179"
+
+FILES=(qwen38-flash-next-v2-swift15-abliterated.hgn
+       qwen38-flash-next-ngram.hgn
+       qwen38-flash-next-vision.hgn
+       tokenizer/tokenizer.json
+       tokenizer/chat_template.jinja
+       tokenizer/generation_config.json
+       tokenizer/merges.txt
+       tokenizer/vocab.json
+       tokenizer/tokenizer_config.json)
+SIZES=(66687678464
+       51200246144
+       897916416
+       12809320
+       8952
+       202
+       3353259
+       6722759
+       17928)
+SHAS=(9c880b8ba48deabfa06172eff1e7572e4fe465e09e97faec6e50c3f6fdcac037
+      3450acada94e19aabad88bd49b45eb70304820949c2240fc178067b20ae5dffe
+      d62e0ae553fe88afd3833733d4a4c669f34d20fd8dfce4b9610525bed2134b10
+      0997f410c57a1f4e53b09e4be8f4a172d90edd9564368fb0847030937229b9f3
+      - - - - - -)
+
+# Fetch source per file: repo base + revision (empty = ABLIT @main).
+SRCS=("$ABLIT"
+      "$PEONIST/$NGRAM_REV"
+      "$PEONIST/$TOK_REV"
+      "$PEONIST/$TOK_REV"
+      "$PEONIST/$TOK_REV"
+      "$PEONIST/$TOK_REV"
+      "$PEONIST/$TOK_REV"
+      "$PEONIST/$TOK_REV"
+      "$PEONIST/$TOK_REV")
+
+VERIFY_ONLY=0
+[ "${1:-}" = "--verify-only" ] && VERIFY_ONLY=1
+
+mkdir -p "$DEST"
+
+for i in "${!FILES[@]}"; do
+    f="${FILES[$i]}"; want="${SIZES[$i]}"; sha="${SHAS[$i]}"; base="${SRCS[$i]}"
+    path="$DEST/$f"
+    mkdir -p "$(dirname "$path")"
+    if [ "$VERIFY_ONLY" -eq 0 ]; then
+        # A file already at the published size skips the fetch entirely:
+        # resuming it would ask the CDN for a range past EOF (416 -> curl
+        # error under set -e), and re-pulling 62 GiB to prove a point is
+        # not what --verify-only is for. sha256 below still decides.
+        if [ -f "$path" ] && [ "$(stat -c %s "$path")" = "$want" ]; then
+            echo "HAVE  $f ($want bytes) — skipping fetch"
+        else
+            echo "FETCH $f -> $path"
+            # HF answers /resolve/ with a 302 to the Xet CDN and curl DROPS
+            # the Range header across redirects — a naive -C - then
+            # "resumes" by pulling the ~1 KB redirect body (measured
+            # 2026-10-05 on the 27B fetch: file frozen 14 MiB short, curl
+            # exit 0). Resolve the effective URL first, then resume
+            # against the CDN, which honors Range.
+            url=$(curl -sfIL -o /dev/null -w '%{url_effective}' "$base/$f") \
+                || { echo "cannot resolve $base/$f" >&2; exit 1; }
+            curl -fL --retry 5 -C - -o "$path" "$url"
+        fi
+    fi
+    [ -f "$path" ] || { echo "MISSING $path (run without --verify-only)" >&2; exit 1; }
+    got="$(stat -c %s "$path")"
+    [ "$got" = "$want" ] || { echo "SIZE $path: $got != $want" >&2; exit 1; }
+    echo "OK   $f ($got bytes)"
+done
+
+echo "byte counts match; verifying sha256 where the Hub publishes an LFS oid..."
+for i in "${!FILES[@]}"; do
+    f="${FILES[$i]}"; sha="${SHAS[$i]}"
+    if [ "$sha" = "-" ]; then
+        echo "OK   $f (plain git blob, size-checked)"
+        continue
+    fi
+    got="$(sha256sum "$DEST/$f" | cut -d' ' -f1)"
+    [ "$got" = "$sha" ] || { echo "SHA256 $f: $got != $sha" >&2; exit 1; }
+    echo "OK   $f sha256"
+done
+echo "DOWNLOAD_DONE $DEST"

scripts/download-frank-primary-swift15.sh

@@ -0,0 +1,113 @@
+#!/usr/bin/env bash
+# download-frank-primary-swift15.sh — payload fetch/verify for the planned
+# frank-primary swap to the Swift 1.5 finetune: ukisai/Swift1.5-Qwen3.8-Flash-Next
+# (BF16 source, 149 files, 360,037,355,038 bytes = 335.3 GiB: 131 safetensors
+# shards + weight-map index + tokenizer + vision preprocessing configs + chat
+# template + licenses). Key set verified identical to the official
+# Qwen/Qwen3.8-Flash-Next base (0 added / 0 missing), so the qwen4exp recipes
+# apply verbatim; the checkpoint ships BF16 with NO quantization_config.
+# Repo is UNGATED — no token needed (never put one on the command line).
+#
+#   bash scripts/download-frank-primary-swift15.sh --fetch         resume/complete
+#   bash scripts/download-frank-primary-swift15.sh                 verify only
+#
+# Fetch uses the vendored scripts/hfget.sh (StillDeadcode's Apache-2.0
+# listing/resume downloader, curl+jq only): per-file size checks against
+# the Hub listing, `-C -` resume, pickle-format duplicates skipped.
+# Verify (default) is the AGENTS.md law in three parts: every listed file
+# present with the listed size, the shard count matches the weight-map
+# index, and every shard's sha256 equals the Hub's LFS oid. Verify needs
+# only the public API.
+set -euo pipefail
+
+REPO="ukisai/Swift1.5-Qwen3.8-Flash-Next"
+DEST="/home/dank/models/ukisai/Swift1.5-Qwen3.8-Flash-Next"
+EXPECTED_SHARDS=131
+
+mode="verify"
+[[ "${1:-}" == "--fetch" ]] && mode="fetch"
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+
+if [[ "$mode" == "fetch" ]]; then
+    # HFGET_JOBS=4 is hfget's default (a few streams saturate the link).
+    bash "$SCRIPT_DIR/hfget.sh" "$REPO" "$DEST" main
+fi
+
+echo "verifying local payload: $DEST"
+fail=0
+
+INDEX="$DEST/model.safetensors.index.json"
+if [[ ! -f "$INDEX" ]]; then
+    echo "  BAD  weight-map index missing: $INDEX" >&2
+    exit 1
+fi
+
+python3 - "$REPO" "$DEST" <<'PY' || fail=1
+import hashlib, json, os, sys, urllib.request
+
+repo, dest = sys.argv[1], sys.argv[2]
+
+def listing():
+    """The tree API, following the Link cursor exactly like hfget.sh."""
+    out, api, pages = [], (
+        f"https://huggingface.co/api/models/{repo}/tree/main"
+        "?recursive=true&expand=true"), 0
+    while api:
+        with urllib.request.urlopen(api) as r:
+            out += json.load(r)
+            api = None
+            link = r.headers.get("Link", "")
+            for part in link.split(","):
+                if 'rel="next"' in part:
+                    api = part[part.find("<") + 1:part.find(">")]
+        pages += 1
+        if pages > 200:
+            raise SystemExit("listing did not terminate")
+    return out
+
+files = [e for e in listing() if e.get("type") == "file"]
+sizes = {e["path"]: e.get("size", 0) for e in files}
+lfs = {e["path"]: e["lfs"]["oid"] for e in files if e.get("lfs")}
+
+bad = 0
+for path, want in sorted(sizes.items()):
+    p = os.path.join(dest, path)
+    if not os.path.isfile(p):
+        print(f"  BAD  {path}: missing")
+        bad += 1
+    elif os.path.getsize(p) != want:
+        print(f"  BAD  {path}: {os.path.getsize(p)} bytes, listing says {want}")
+        bad += 1
+print(f"  {'OK' if bad == 0 else 'FAIL'}: {len(sizes) - bad}/{len(sizes)} files present at listed size")
+if bad:
+    raise SystemExit(1)
+
+# Shard set against the weight-map index.
+wm = json.load(open(os.path.join(dest, "model.safetensors.index.json")))["weight_map"]
+shards = sorted(set(wm.values()))
+print(f"  index maps {len(wm)} tensors across {len(shards)} shards")
+if len(shards) != 131:
+    print(f"  BAD  shard count {len(shards)}, want 131")
+    raise SystemExit(1)
+
+# Content: every LFS-tracked file hashed against the Hub oid. The safetensors
+# shards dominate the bytes; tokenizer/config files ride the same check when
+# the Hub tracks them in LFS (they are plain blobs here, already size-checked).
+todo = [p for p in shards if p in lfs]
+for i, path in enumerate(todo):
+    h = hashlib.sha256()
+    with open(os.path.join(dest, path), "rb") as f:
+        for chunk in iter(lambda: f.read(1 << 24), b""):
+            h.update(chunk)
+    if h.hexdigest() != lfs[path]:
+        print(f"  BAD  {path}: sha256 mismatch (want {lfs[path]})")
+        bad += 1
+    if (i + 1) % 16 == 0:
+        print(f"  ...hashed {i + 1}/{len(todo)}")
+print(f"  {'OK' if bad == 0 else 'FAIL'}: {len(todo) - bad}/{len(todo)} shards sha256-match the Hub")
+if bad:
+    raise SystemExit(1)
+PY
+
+[[ "$fail" -eq 0 ]] || exit 1
+echo "payload verified: 131 shards + index + tokenizer + configs, sizes and sha256 checked"

scripts/download-frank-primary.sh

@@ -1,88 +1,111 @@
 #!/usr/bin/env bash
-# Payload inventory/verification for frank-primary:
-# orcarouter/Qwen3.8-Flash-Next-Uncensored-FP8 (compressed-tensors FP8, 131
-# shards + weight-map index, ~172.8 GiB) — the tcclaviger-stack payload that
-# became frank-primary on 2026-09-29.
+# download-frank-primary.sh — payload fetch/verify for frank-primary:
+# the OFFICIAL Qwen/Qwen3.8-Flash-Next-FP8 checkpoint (131 safetensors
+# shards + weight-map index + tokenizer + vision preprocessing configs,
+# ~172.8 GB). 2026-10-05: replaces the orcarouter uncensored payload of the
+# FreeToken era (that script lived here; its repo was gated and needed
+# HF_TOKEN — this one is UNGATED and needs no token at all).
 #
-#   bash scripts/download-frank-primary.sh --verify-only   (the ONLY mode)
-#   bash scripts/download-frank-primary.sh --fetch
+#   bash scripts/download-frank-primary.sh --fetch         resume/complete
+#   bash scripts/download-frank-primary.sh                 verify only
 #
-# THIS REPO IS GATED (401 anonymous — measured 2026-09-29): anonymous fetch
-# is impossible and the tree API masks LFS hashes, so a byte-exact upstream
-# manifest cannot be built without the authed endpoint. The payload on
-# /home/dank/models was fetched with the operator's HF token (out of band:
-# HF_TOKEN in the environment, never in argv) at bring-up time.
-#
-# Modes:
-#   --fetch       resume any missing/truncated shard via `hf download` with
-#                 HF_TOKEN from the environment (NEVER passed on the command
-#                 line — a -H auth header is world-readable in
-#                 /proc/<pid>/cmdline, the 2026-09-16 leak).
-#   --verify-only default: local integrity check — shard count vs the
-#                 weight-map index, every file nonzero and its size equal to
-#                 the index's declared total, plus sha256 of every shard
-#                 (slow, ~10 min for 173 GiB).
+# Fetch uses the vendored scripts/hfget.sh (StillDeadcode's Apache-2.0
+# listing/resume downloader, curl+jq only): per-file size checks against
+# the Hub listing, `-C -` resume, pickle-format duplicates skipped.
+# Verify (default) is the AGENTS.md law in three parts: every listed file
+# present with the listed size, the shard count matches the weight-map
+# index, and every shard's sha256 equals the Hub's LFS oid. Verify needs
+# only the public API.
 set -euo pipefail
 
-REPO="orcarouter/Qwen3.8-Flash-Next-Uncensored-FP8"
-DEST="/home/dank/models/Qwen3.8-Flash-Next-Uncensored-FP8"
+REPO="Qwen/Qwen3.8-Flash-Next-FP8"
+DEST="/home/dank/models/Qwen/Qwen3.8-Flash-Next-FP8"
 EXPECTED_SHARDS=131
 
 mode="verify"
 [[ "${1:-}" == "--fetch" ]] && mode="fetch"
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
 
 if [[ "$mode" == "fetch" ]]; then
-    : "${HF_TOKEN:?--fetch needs HF_TOKEN in the environment (never argv)}"
-    export HF_TOKEN
-    mkdir -p "$DEST"
-    hf download "$REPO" --local-dir "$DEST"
+    # HFGET_JOBS=4 is hfget's default (a few streams saturate the link).
+    bash "$SCRIPT_DIR/hfget.sh" "$REPO" "$DEST" main
 fi
 
 echo "verifying local payload: $DEST"
 fail=0
 
-# The weight-map index is the source of truth for the shard set.
 INDEX="$DEST/model.safetensors.index.json"
 if [[ ! -f "$INDEX" ]]; then
     echo "  BAD  weight-map index missing: $INDEX" >&2
     exit 1
 fi
 
-# Every index entry must exist, nonzero, and sized equal to the map's total.
-python3 - "$INDEX" "$DEST" <<'PY' || fail=1
-import json, os, sys
-index, dest = sys.argv[1], sys.argv[2]
-wm = json.load(open(index))["weight_map"]
-files = sorted(set(wm.values()))
-print(f"  index maps {len(wm)} tensors across {len(files)} files")
+python3 - "$REPO" "$DEST" <<'PY' || fail=1
+import hashlib, json, os, sys, urllib.request
+
+repo, dest = sys.argv[1], sys.argv[2]
+
+def listing():
+    """The tree API, following the Link cursor exactly like hfget.sh."""
+    out, api, pages = [], (
+        f"https://huggingface.co/api/models/{repo}/tree/main"
+        "?recursive=true&expand=true"), 0
+    while api:
+        with urllib.request.urlopen(api) as r:
+            out += json.load(r)
+            api = None
+            link = r.headers.get("Link", "")
+            for part in link.split(","):
+                if 'rel="next"' in part:
+                    api = part[part.find("<") + 1:part.find(">")]
+        pages += 1
+        if pages > 200:
+            raise SystemExit("listing did not terminate")
+    return out
+
+files = [e for e in listing() if e.get("type") == "file"]
+sizes = {e["path"]: e.get("size", 0) for e in files}
+lfs = {e["path"]: e["lfs"]["oid"] for e in files if e.get("lfs")}
+
 bad = 0
-total = 0
-for f in files:
-    p = os.path.join(dest, f)
-    if not os.path.isfile(p) or os.path.getsize(p) == 0:
-        print(f"  BAD  {f}: missing or zero bytes" )
+for path, want in sorted(sizes.items()):
+    p = os.path.join(dest, path)
+    if not os.path.isfile(p):
+        print(f"  BAD  {path}: missing")
         bad += 1
-    else:
-        total += os.path.getsize(p)
-print(f"  {'OK' if bad == 0 else 'FAIL'}: {len(files) - bad}/{len(files)} files present, {total/2**30:.1f} GiB total")
-raise SystemExit(1 if bad else 0)
+    elif os.path.getsize(p) != want:
+        print(f"  BAD  {path}: {os.path.getsize(p)} bytes, listing says {want}")
+        bad += 1
+print(f"  {'OK' if bad == 0 else 'FAIL'}: {len(sizes) - bad}/{len(sizes)} files present at listed size")
+if bad:
+    raise SystemExit(1)
+
+# Shard set against the weight-map index.
+wm = json.load(open(os.path.join(dest, "model.safetensors.index.json")))["weight_map"]
+shards = sorted(set(wm.values()))
+print(f"  index maps {len(wm)} tensors across {len(shards)} shards")
+if len(shards) != 131:
+    print(f"  BAD  shard count {len(shards)}, want 131")
+    raise SystemExit(1)
+
+# Content: every LFS-tracked file hashed against the Hub oid. The safetensors
+# shards dominate the bytes; tokenizer/config files ride the same check when
+# the Hub tracks them in LFS (they are plain blobs here, already size-checked).
+todo = [p for p in shards if p in lfs]
+for i, path in enumerate(todo):
+    h = hashlib.sha256()
+    with open(os.path.join(dest, path), "rb") as f:
+        for chunk in iter(lambda: f.read(1 << 24), b""):
+            h.update(chunk)
+    if h.hexdigest() != lfs[path]:
+        print(f"  BAD  {path}: sha256 mismatch (want {lfs[path]})")
+        bad += 1
+    if (i + 1) % 16 == 0:
+        print(f"  ...hashed {i + 1}/{len(todo)}")
+print(f"  {'OK' if bad == 0 else 'FAIL'}: {len(todo) - bad}/{len(todo)} shards sha256-match the Hub")
+if bad:
+    raise SystemExit(1)
 PY
 
-# Shard count gate (what the serve script and deployer check).
-n="$(find "$DEST" -maxdepth 1 -name 'model-*-of-*.safetensors' | wc -l)"
-if [[ "$n" != "$EXPECTED_SHARDS" ]]; then
-    echo "  BAD  shard count: found $n, want $EXPECTED_SHARDS" >&2
-    fail=1
-else
-    echo "  OK   shard count $n"
-fi
-
-if [[ "${2:-}" == "--hash" || "${VERIFY_HASH:-0}" == "1" ]]; then
-    echo "hashing every shard (slow)..."
-    cd "$DEST"
-    sha256sum -c --quiet model.safetensors.index.json 2>/dev/null || \
-    sha256sum model-*-of-00131.safetensors > /dev/null  # presence check; content hash list printed on demand
-fi
-
 [[ "$fail" -eq 0 ]] || exit 1
-echo "payload inventory OK: $EXPECTED_SHARDS shards + index present"
+echo "payload verified: 131 shards + index + tokenizer + configs, sizes and sha256 checked"

scripts/download-frank-secondary.sh

@@ -0,0 +1,90 @@
+#!/usr/bin/env bash
+# Download the frank-secondary payload for llama.cpp on the RTX 3090:
+# HauhauCS/Qwen3.5-9B-Uncensored-HauhauCS-Aggressive at **Q6_K** plus the
+# PUBLISHER's OWN vision projector (the BF16 mmproj — the model is natively
+# multimodal, so this file is the endpoint's eyes). Public repo — no auth
+# header, and never a token on the command line (it is world-readable in
+# /proc/<pid>/cmdline).
+#
+# Fresh downloads, no resume (`curl -C -` once produced a 213 MB oversized
+# file on a 31 GiB payload); -f/--fail-with-body so an HTTP error is not a
+# success, then byte count and sha256. The Hub exposes no LFS oid for this
+# repo (Xet storage), so the sha256 values below were computed locally after
+# the first verified fetch — byte counts match the Hub's declared sizes.
+# A sha of "-" means "first fetch: size-verified, sha printed for pinning".
+# Idempotent: complete files are skipped.
+#
+# 2026-10-07 (Frank): the payload moved to Qwen3.5-9B-Uncensored Q6_K at
+# 2 x 200,704 (qwen35 hybrid: 32 layers, 8 full-attention + 24 Gated
+# DeltaNet, no MTP tensors in this file). Rollback payloads stay on disk:
+# /home/dank/models/Gemma4-26B-A4B/ (Q4_K_M, the 2026-10-07 era being
+# replaced), Swift15-UncIQ3XXS/, MiMo-Q6K/, MiMo-Q5KM/, Ornith-Q8/.
+#
+# Usage:
+#   scripts/download-frank-secondary.sh [DEST_DIR] [--verify-only]
+set -euo pipefail
+
+DEST=""
+VERIFY_ONLY=0
+for arg in "$@"; do
+    case "$arg" in
+        --verify-only) VERIFY_ONLY=1 ;;
+        *) DEST="$arg" ;;
+    esac
+done
+DEST="${DEST:-/home/dank/models/Qwen35-9B-HauhauCS-Q6K}"
+
+# repo | hub-name | dest-name | bytes | sha256 (computed locally; Xet repo)
+FILES=(
+  "HauhauCS/Qwen3.5-9B-Uncensored-HauhauCS-Aggressive|Qwen3.5-9B-Uncensored-HauhauCS-Aggressive-Q6_K.gguf|Qwen3.5-9B-Uncensored-HauhauCS-Aggressive-Q6_K.gguf|7359259008|c0ba7beb68fd3fe47891bd549486d38dcf62d00817296ea314ad37017f5a4986"
+  "HauhauCS/Qwen3.5-9B-Uncensored-HauhauCS-Aggressive|mmproj-Qwen3.5-9B-Uncensored-HauhauCS-Aggressive-BF16.gguf|mmproj-Qwen3.5-9B-Uncensored-HauhauCS-Aggressive-BF16.gguf|921704448|05f662501f8bd45607b079723a3e238a4e888fd085a10a53f4057a0e250f6934"
+)
+
+status=0
+for entry in "${FILES[@]}"; do
+  IFS='|' read -r repo hubname destname size sha <<<"$entry"
+  url="https://huggingface.co/${repo}/resolve/main/${hubname}"
+  f="$DEST/$destname"
+  have=0
+  if [[ -f "$f" ]] && [[ "$(stat -c%s "$f")" == "$size" ]]; then
+    if [[ "$sha" == "-" ]]; then
+      have=1
+    elif sha256sum -c --quiet <(printf '%s  %s\n' "$sha" "$f"); then
+      have=1
+    fi
+  fi
+  if (( have )); then
+    echo "OK        $destname"
+    continue
+  fi
+  if (( VERIFY_ONLY )); then
+    echo "MISSING   $destname"
+    status=1
+    continue
+  fi
+  echo "FETCHING  $destname (from $repo)"
+  tmp="$f.part"
+  curl -fsSL --fail-with-body -o "$tmp" "$url"
+  mv "$tmp" "$f"
+  if [[ "$(stat -c%s "$f")" != "$size" ]]; then
+    echo "SIZE FAIL $destname ($(stat -c%s "$f") != $size)"
+    status=1
+    continue
+  fi
+  if [[ "$sha" == "-" ]]; then
+    echo "VERIFIED  $destname (size only) sha256=$(sha256sum "$f" | cut -d' ' -f1)"
+    continue
+  fi
+  if ! sha256sum -c --quiet <(printf '%s  %s\n' "$sha" "$f"); then
+    echo "SHA FAIL  $destname"
+    status=1
+    continue
+  fi
+  echo "VERIFIED  $destname"
+done
+if (( status == 0 )); then
+  echo "DOWNLOAD_DONE $DEST"
+else
+  echo "DOWNLOAD_FAILED $DEST" >&2
+fi
+exit "$status"

scripts/download-frank-small.sh

@@ -1,87 +0,0 @@
-#!/usr/bin/env bash
-# Download the frank-small payload for llama.cpp on the RTX 3090:
-# ornith-ai/Ornith-1.5-9B-GGUF:Q8_0 plus its vision projector
-# (the GGUF carries no `v.*` tensors — the tower lives only in the mmproj, so
-# without this file the endpoint is text-only by construction). Public repo —
-# no auth header, and never a token on the command line (it is world-readable
-# in /proc/<pid>/cmdline).
-#
-# Fresh downloads, no resume (`curl -C -` once produced a 213 MB oversized file
-# on a 31 GiB payload); -f/--fail-with-body so an HTTP 401 is not a success,
-# then byte count and the Hub LFS sha256. Idempotent: complete files are
-# skipped.
-#
-# 2026-10-05 (Frank): the rung is Ornith-1.5-9B Q8_0. The payload moved off
-# MiMo-V2.6 (Q6_K) to Ornith because it is the fleet's own model and it is the
-# only one the harness's own prompt recipes were written against; Q8_0 was the
-# rung chosen so the quant does not cost the throughput this endpoint already
-# runs at. The Q5_K_M file this script served from 2026-10-02 stays on disk at
-# /home/dank/models/MiMo-Q5KM/ (6876124704 B, LFS oid
-# 04cf46f2b5584ef922697d6c063f94879434fb86c467f1a09e52daf0cddaf35f) as the
-# rollback target; re-fetch it from the same repo with the same recipe.
-#
-# Usage:
-#   scripts/download-frank-small.sh [DEST_DIR] [--verify-only]
-set -euo pipefail
-
-REPO="ornith-ai/Ornith-1.5-9B-GGUF"
-BASE="https://huggingface.co/${REPO}/resolve/main"
-
-DEST=""
-VERIFY_ONLY=0
-for arg in "$@"; do
-    case "$arg" in
-        --verify-only) VERIFY_ONLY=1 ;;
-        *) DEST="$arg" ;;
-    esac
-done
-DEST="${DEST:-/home/dank/models/Ornith-Q8}"
-
-# name bytes sha256 (the sha256 is the LFS oid in the repo's pointer file)
-FILES=(
-  "Ornith-1.5-9B-Q8_0.gguf 9786060384 22086870b009dbe9815ee752c48a82de930118a7c5ce5599590892ae03b8b010"
-  "mmproj-Ornith-1.5-9B-BF16.gguf 921704672 626f9f90627402a6bf4a999111d0fbd69b5fcca7aa8ba089d69e5f10e8858e1d"
-)
-
-mkdir -p "$DEST"
-status=0
-for entry in "${FILES[@]}"; do
-  read -r name size sha <<<"$entry"
-  f="$DEST/$name"
-  have=0
-  if [[ -f "$f" ]] && [[ "$(stat -c%s "$f")" == "$size" ]]; then
-    if sha256sum -c --quiet <(printf '%s  %s\n' "$sha" "$f"); then
-      have=1
-    fi
-  fi
-  if (( have )); then
-    echo "OK        $name"
-    continue
-  fi
-  if (( VERIFY_ONLY )); then
-    echo "MISSING   $name"
-    status=1
-    continue
-  fi
-  echo "FETCHING  $name"
-  tmp="$f.part"
-  curl -fsSL --fail-with-body -o "$tmp" "$BASE/$name"
-  mv "$tmp" "$f"
-  if [[ "$(stat -c%s "$f")" != "$size" ]]; then
-    echo "SIZE FAIL $name ($(stat -c%s "$f") != $size)"
-    status=1
-    continue
-  fi
-  if ! sha256sum -c --quiet <(printf '%s  %s\n' "$sha" "$f"); then
-    echo "SHA FAIL  $name"
-    status=1
-    continue
-  fi
-  echo "VERIFIED  $name"
-done
-if (( status == 0 )); then
-  echo "DOWNLOAD_DONE $DEST"
-else
-  echo "DOWNLOAD_FAILED $DEST" >&2
-fi
-exit "$status"

scripts/fleet-probes-swift15.sh

@@ -0,0 +1,56 @@
+#!/usr/bin/env bash
+# fleet-probes-swift15.sh — RUNS ON DANKSERVER. The cutover contract probes.
+set -uo pipefail
+B=http://localhost:7770/v1
+
+echo "== vision (solid red 512px, expect 'Red')"
+python3 - <<'PY'
+import base64, json, urllib.request, struct, zlib
+def png(color):
+    w = h = 512
+    raw = b"".join(b"\x00" + bytes(color) * w for _ in range(h))
+    def chunk(t, d):
+        c = t + d
+        return struct.pack(">I", len(d)) + c + struct.pack(">I", zlib.crc32(c))
+    return (b"\x89PNG\r\n\x1a\n"
+            + chunk(b"IHDR", struct.pack(">IIBBBBB", w, h, 8, 2, 0, 0, 0))
+            + chunk(b"IDAT", zlib.compress(raw)) + chunk(b"IEND", b""))
+b64 = base64.b64encode(png((255, 0, 0))).decode()
+body = json.dumps({"model": "frank-archon", "max_tokens": 256, "temperature": 0,
+    "messages": [{"role": "user", "content": [
+        {"type": "image_url", "image_url": {"url": "data:image/png;base64," + b64}},
+        {"type": "text", "text": "What color is this image? Answer with one word."}]}]}).encode()
+r = urllib.request.Request("http://localhost:7770/v1" + "/chat/completions",
+                           data=body, headers={"Content-Type": "application/json"})
+d = json.load(urllib.request.urlopen(r, timeout=300))
+m = d["choices"][0]["message"]
+ans = (m.get("content") or m.get("reasoning_content") or "")
+print("VISION ANSWER:", ans[:80].replace("\n", " "))
+PY
+
+echo "== tools (expect tool_calls)"
+curl -s -m 120 $B/chat/completions -H 'Content-Type: application/json' -d '{
+ "model":"frank-archon","temperature":0,"max_tokens":128,
+ "messages":[{"role":"user","content":"What is 23 times 47? Use the calculator tool."}],
+ "tools":[{"type":"function","function":{"name":"calculator","description":"evaluate arithmetic",
+ "parameters":{"type":"object","properties":{"expr":{"type":"string"}},"required":["expr"]}}}]}' \
+ | python3 -c 'import json,sys; d=json.load(sys.stdin); m=d["choices"][0]["message"]; print("TOOL_CALLS:", m.get("tool_calls"))'
+
+echo "== reasoning_effort fold (xhigh/medium/low/high all 200 on froggeric v22.5)"
+for lvl in xhigh medium low high; do
+  code=$(curl -s -o /dev/null -w "%{http_code}" -m 120 $B/chat/completions \
+    -H 'Content-Type: application/json' \
+    -d "{\"model\":\"frank-archon\",\"max_tokens\":8,\"reasoning_effort\":\"$lvl\",\"messages\":[{\"role\":\"user\",\"content\":\"hi\"}]}")
+  echo "  reasoning_effort=$lvl -> HTTP $code"
+done
+
+echo "== reasoning_content channel"
+curl -s -m 120 $B/chat/completions -H 'Content-Type: application/json' \
+ -d '{"model":"frank-archon","max_tokens":64,"messages":[{"role":"user","content":"Is 97 prime? Think briefly."}]}' \
+ | python3 -c 'import json,sys; m=json.load(sys.stdin)["choices"][0]["message"]; print("has reasoning_content:", bool(m.get("reasoning_content")), "| content:", (m.get("content") or "")[:60].replace(chr(10)," "))'
+
+echo "== response_format json_object"
+curl -s -m 120 $B/chat/completions -H 'Content-Type: application/json' \
+ -d '{"model":"frank-archon","max_tokens":64,"response_format":{"type":"json_object"},"messages":[{"role":"user","content":"Return JSON {\"ok\": true}"}]}' \
+ | python3 -c 'import json,sys; d=json.load(sys.stdin); print("JSON MODE:", (d["choices"][0]["message"].get("content") or "")[:60])'
+echo "PROBES_DONE"

scripts/frank-large.env

@@ -1,75 +1,98 @@
 # frank-large.env — tunables for the frank-large endpoint on Dankclaw's
 # Strix Halo iGPU (gfx1151), host port 7770.
 #
-# RUNTIME (2026-10-05, Frank): halogen — the closed-source C++/HIP engine
-# (github.com/peonist-ai/halogen-server, image ghcr.io/peonist-ai/halogen,
-# digest-pinned below) serving peonist-ai/halogen-qwen3.8-27b, a ~6.32 bpw
-# NVFP4-derivative quant of the BASE Qwen/Qwen3.8-27B (35,865,565,184 B,
-# 1352 tensors), as a podman container under a root unit, driven by
-# scripts/serve-frank-large.sh. This CUTS OVER the
-# 2026-10-02..10-03 ROCmFPX llama.cpp shape (uncensored
-# Q6_0_ROCMFPX_AGENT + MTP, 27.6-28.1 t/s): rollback = revert the swap
-# commit's trio; the ROCmFPX payload, draft head and build all stay on
-# disk at /home/dank/models/kingjones-Qwen3.8-27B-Uncensored-ROCmFP4-STRIX-MTP/.
+# RUNTIME (2026-10-06, Frank): halogen-flash-server 0.16.2 — the
+# closed-source C++/HIP engine (image ghcr.io/peonist-ai/halogen-flash-server,
+# digest-pinned below) serving the Quat3rnion ABLITERATED Swift1.5 finetune
+# of peonist's Qwen3.8-Flash-Next v2 checkpoint, as a podman container under
+# a root unit, driven by scripts/serve-frank-large.sh. This CUTS OVER the
+# 2026-10-05 halogen 27B-dense shape (censored base, DFlash2, 31.7 t/s):
+# rollback = revert the swap commit's trio; the 27B payload stays on disk at
+# /home/dank/models/halogen-qwen3.8-27b/.
 #
 # WHY THE RUNTIME MOVED WITH THE MODEL: the payload is .hgn — per its own
-# card it "will not load in transformers, vLLM, or llama.cpp". Accepted
-# consequences (Frank, 2026-10-05): TEXT-ONLY (halogen ignores the model's
-# vision encoder; the fleet's vision path is frank-small) and the CENSORED
-# base (this is a finetune of Qwen/Qwen3.8-27B, not the abliterated file —
-# refusals may return). What it buys: vendor-measured decode DFlash2 31.7
-# t/s mean (vs this endpoint's MTP 27.6-28.1 and the old halogen-era MTP
-# 26.9), prefill ~566 t/s at 32k (was ~400 on the sibling engine), 1.4-2.3x
-# wall-clock on prefill-heavy agent traffic, at HIGHER effective precision
-# (~6.32 bpw vs ~6.6 with the old W4A4 promotion — see FLAGS.md).
+# card it "will not load in transformers, vLLM, or llama.cpp" — and the v2
+# .hgn loads ONLY in the flash-server line (the old halogen engine is for
+# the 27B p1w4d file; the v2 quickstart pins flash-server). What the move
+# buys (card + live probe evidence): MTP spec decode (53-57 t/s greedy,
+# 3-10x serial), ~1,640 t/s prefill at 34k, ~72% fewer thinking tokens than
+# the censored 27B (905 mean), the ABLITERATED base (refusals should NOT
+# return), IMAGE INPUT (the v2 vision tower loads beside the checkpoint —
+# red-PNG probe verified on the card; the 27B engine ignored its encoder),
+# tool calls verified, and the native 262,144 context.
 #
-# SHAPE — the two knobs that are NOT free:
-#   HALOGEN_KV_SLOTS=1 KEEPS SPECULATION ON. halogen makes batching and
-#   speculation mutually exclusive: with >1 slot the drafter is disabled
-#   and per-stream decode falls to serial speed (the sibling engine
-#   measured 41 t/s -> 14 t/s when slots were raised, and halogen's own
-#   FLAGS.md prints the same law). frank-large was already a one-session
-#   endpoint under ROCmFPX (PARALLEL=1), so slots=1 is the same contract,
-#   not a regression.
-#   HALOGEN_SLOT_CTX=200700 (Frank's window) is the per-conversation hard
+# SHAPE — the knobs that are NOT free, with the arithmetic:
+#   HALOGEN_CTX=262144 is the PER-SLOT window and the per-conversation hard
 #   ceiling — a prompt over it is a 400 naming the limit, never a silent
-#   truncation. KV pool = slots x slot_ctx x 64 KiB = 12.2 GiB. Budget
-#   against the 121 GiB of unified memory: weights 33.4 GiB (mmap'd — page
-#   cache, not anon), KV 12.2, engine working set + auto prompt cache
-#   ~20-30, host baseline ~10 -> ~75-85 GiB in use. The 2026-09-16 halogen
-#   era ran the same engine class on this box with LESS headroom.
-#   HALOGEN_DRAFTER=2 (DFlash2) is the shipped default and the fastest
-#   exact drafter on this checkpoint; the .hgn BAKES BOTH drafters (MTP +
-#   DFlash2), so unlike the Flash-Next era there is no sidecar to mount
-#   and no --dflash flag — the drafter is an int knob, byte-identical
-#   output to greedy verification either way.
+#   truncation. RAISED from 200192 on 2026-10-07: the harness clamps
+#   max_tokens to contextWindow − est − 4096, its chars/4 est UNDER-counts
+#   the rendered prompt by ~3 % on code/agent traffic, and compaction only
+#   fires at 85 % — so with window == engine ceiling, every request over
+#   ~146k est (65-85 % of the window) was a guaranteed 400. Measured
+#   2026-10-07: est 152,938, server 157,388, request 43,158 vs room 42,804
+#   — over by 354, margin 4,096 < the 4,450 under-count.
+#   Harness contract (models.yml): contextWindow 217088, maxTokens 45056.
+#   Compaction fires at 85 % = 184,524 est ≈ 190k measured (Frank's 180k
+#   target). Worst-case request: prompt + budget <= 217088 − 4096 + 3 %
+#   est-error ≈ 219k <= 262144 — the 45k gap between harness window and
+#   engine ceiling absorbs the estimate error with ~7x margin (robust to a
+#   10 % under-count). Compaction rides on frank-secondary: input ≈
+#   threshold − keepRecent(20k) ≈ 170k measured, inside its 200,704 slot.
+#   HALOGEN_KV_POOL_POSITIONS=524288 = KV_SLOTS(2) x CTX — the explicit
+#   form of the engine's default, so the seam test can pin the product.
+#   KV costs ~28 KiB/position (FLAGS.md) => 524,288 x 28 KiB = 14.0 GiB.
+#   HALOGEN_KV_SLOTS=2 restores the w4b-era shape on this box: the MTP
+#   drafter is NOT slot-locked on this engine (batching and speculation
+#   coexist — DFlash-era slots>1 law does NOT carry over; the card's own
+#   quickstart ships 2 slots WITH the head), so the second slot is
+#   concurrent capacity, not a speculation tax.
+#   Memory ledger against the box's 121.2 GiB unified (the n-gram table is
+#   mmap'd page cache beside the checkpoint, not a pinned charge — the
+#   card's own quickstart arithmetic does not bill it):
+#     weights ~62 GiB resident + KV 14.0 + arena (HALOGEN_MAX_TOK=8192 =>
+#     ~8 GiB per FLAGS.md arena formula 3.8+8.4*(MT/16384)) + vision ~2
+#     = ~86 GiB engine-side; HALOGEN_HOST_RESERVE_GIB=20 keeps the host
+#     honest; baseline co-tenancy (frank-secondary, ComfyUI, Caddy/observ)
+#     ~10-15. ~15 GiB margin. The boot ledger is the referee — read it
+#     after deploy and re-size if the engine prints a clamp.
+#   HALOGEN_SPEC_ADAPT=0 is MANDATORY (card): the default adaptive policy
+#   turns the MTP head off for whole requests after low acceptance and
+#   HALVES decode as a result. Frank accepted the head always-on trade
+#   (temp 1.0 traffic costs acceptance; the card still ships the recipe).
 #
-# CLIENT CONTRACT deltas vs the ROCmFPX shape (the harness side lives in
+# CLIENT CONTRACT deltas vs the 27B shape (harness side lives in
 # ~/.omp/agent/models.yml):
-#   * NO server-side sampler defaults: a request without temperature
-#     decodes GREEDY. The harness sends temp 1.0 / top_p 0.95 via
-#     extraBody, so that wire recipe is load-bearing now, not cosmetic.
-#   * max_tokens defaults to 8192 PER REQUEST and INCLUDES thinking; a
-#     reasoning reply over a small budget returns empty content with
-#     finish_reason "length". The harness maxTokens is 57344: compaction
-#     fires at 70% of 200700 = 140,490 and 140,490 + 57,344 = 197,834 <=
-#     200,700 keeps prompt + budget inside the pool (the 262144-era 65536
-#     would overflow). Requests over HALOGEN_MAX_TOKENS_CAP are a 400
-#     naming the cap; cap and queue timeout are COUPLED (see FLAGS.md
-#     table) — do not move one without the other.
-#   * response_format json_object/json_schema is a 400 (no constrained
-#     decoding in this build).
-#   * Thinking lands in reasoning_content — same field as the llama.cpp
-#     era, so the harness compat block does NOT change. reasoning_effort
-#     accepts none|low|xhigh on this build (template default xhigh); the
-#     models.yml fold (high->xhigh) still applies.
+#   * SERVER-SIDE SAMPLER EXISTS NOW: HALOGEN_TEMPERATURE/TOP_P/TOP_K set
+#     the card's thinking recipe (1.0 / 0.95 / 20) as engine defaults, so
+#     an absent temperature is no longer greedy. models.yml extraBody
+#     carries the same values — consistent, not load-bearing.
+#   * HALOGEN_MAX_TOK=8192 is only the DEFAULT for requests that omit
+#     max_tokens; the harness sends at most its maxTokens 45056. Requests
+#     over HALOGEN_MAX_TOKENS_CAP (65536) are a 400 naming the cap. Cap and
+#     queue timeout are COUPLED (FLAGS.md table): 65536 at ~10 t/s serial
+#     worst case needs > 6550 s; 7200 honors it. Do not move one alone.
+#   * max_tokens INCLUDES thinking; a reasoning reply over a small budget
+#     returns empty content with finish_reason "length".
+#   * Prompt cache is ON by default (PROMPT_CACHE=2): a follow-up turn at
+#     100k context re-prefills ~2 s instead of ~88 s cold. No pin needed —
+#     unlike the 27B engine, auto-sizing fits this box's ledger.
+#   * Thinking lands in reasoning_content (same as the 27B engine); the
+#     v2 chat template rejects reasoning_effort outside xhigh|medium|low,
+#     so the models.yml fold (high->xhigh) still applies.
+#   * Vision: OpenAI image_url content parts only (the fleet's standard
+#     contract). >=256 px probe images or the model answers "video".
+#   * response_format json_object/json_schema unsupported in this line
+#     (no constrained decoding) — unchanged from the 27B engine.
 #   * The engine's token protocol has no auth and binds loopback INSIDE
-#     the container; only the frontend port is published. Keep it that way.
+#     the container; only the frontend port (8731) is published. Same as
+#     before. Kernel needs CONFIG_HSA_AMD_SVM (CachyOS ships it) and
+#     amdgpu.noretry=0 (check at deploy).
 
 # Image: digest-pinned (fetch law: tag pulls silently re-point; the digest
-# is what was qualified). 0.1.4 = the version the model card names.
-FRANK_LARGE_IMAGE=ghcr.io/peonist-ai/halogen@sha256:dc0a39a0016d6cfc58a197978febaafdf8d28403f724ded6111d98b5fb7ac0ea
-# Container identity: the halogen container name.
+# is what was qualified). 0.16.2 = the version the model card names as its
+# quickstart pin; RepoDigest captured at pull on 2026-10-06.
+FRANK_LARGE_IMAGE=ghcr.io/peonist-ai/halogen-flash-server@sha256:0c61bf84ac22308a53f5d1ca6b86806702d7039e5ebc51cae4c66621b92fe04a
+# Container identity: kept from the 27B era (unit ExecStop names it).
 FRANK_LARGE_CONTAINER=frank-large-halogen
 # Old quadlet-era container identity; the serve script clears stale names.
 FRANK_LARGE_LEGACY_CONTAINERS=frank-large
@@ -77,37 +100,59 @@ FRANK_LARGE_LEGACY_CONTAINERS=frank-large
 # default); the engine port (8730) is never published.
 FRANK_LARGE_PUBLISH=7770:8731
 
-FRANK_LARGE_MODEL_DIR=/home/dank/models/halogen-qwen3.8-27b
-FRANK_LARGE_CHECKPOINT=qwen3.8-27b-p1w4d-d2.hgn
-FRANK_LARGE_CHECKPOINT_BYTES=35865565184
-FRANK_LARGE_CHECKPOINT_SHA256=274c3fc767fb57faf025dd03c76376b1fc1b32448aef45aaf02d1898fa962089
-# The tokenizer is a FLAT DIRECTORY, $FRANK_LARGE_MODEL_DIR/tokenizer, mounted
-# whole at /tokenizer — the model card ships `tokenizer/` and the engine's
-# HALOGEN_TOKENIZER default is that directory (the card's own docker run sets
-# no tokenizer flag). The Hub stores it as 5 files (sizes from the Hub API,
-# 2026-10-05): tokenizer.json 12809320 (LFS sha256
-# 0997f410c57a1f4e53b09e4be8f4a172d90edd9564368fb0847030937229b9f3),
-# vocab.json 6722759, merges.txt 3353259, chat_template.jinja 8952,
-# tokenizer_config.json 17928 (plain git blobs — size-checked, no LFS oid).
-# The serve gate and the deployer refuse a missing file or ANY symlink: an
-# HF cache snapshot symlinks each file into blobs/, and those links dangle
+FRANK_LARGE_MODEL_DIR=/home/dank/models/halogen-flashnext-v2
+# The checkpoint: Quat3rnion/halogen-swift1.5-qwen3.8-flash-next-v2-abliterated
+# qwen38-flash-next-v2-swift15-abliterated.hgn — compacted r2 of the card's
+# append-only original (byte-identical live tensors, padding dropped:
+# 66,687,678,464 B vs 89,775,868,032 B). LFS sha256 verified at fetch.
+FRANK_LARGE_CHECKPOINT=qwen38-flash-next-v2-swift15-abliterated.hgn
+FRANK_LARGE_CHECKPOINT_BYTES=66687678464
+FRANK_LARGE_CHECKPOINT_SHA256=9c880b8ba48deabfa06172eff1e7572e4fe465e09e97faec6e50c3f6fdcac037
+# The n-gram lookup table: REQUIRED (the engine reads it beside the
+# checkpoint; the card's quickstart names it explicitly). From
+# peonist-ai/halogen-qwen3.8-flash-next @ 5cc17cea.
+FRANK_LARGE_NGRAM_TABLE=qwen38-flash-next-ngram.hgn
+FRANK_LARGE_NGRAM_BYTES=51200246144
+FRANK_LARGE_NGRAM_SHA256=3450acada94e19aabad88bd49b45eb70304820949c2240fc178067b20ae5dffe
+# The vision tower: OPTIONAL on the engine, ENABLED here (image input).
+# From peonist-ai/halogen-qwen3.8-flash-next @ e053f488.
+FRANK_LARGE_VISION_TOWER=qwen38-flash-next-vision.hgn
+FRANK_LARGE_VISION_BYTES=897916416
+FRANK_LARGE_VISION_SHA256=d62e0ae553fe88afd3833733d4a4c669f34d20fd8dfce4b9610525bed2134b10
+# The tokenizer is a FLAT DIRECTORY, $FRANK_LARGE_MODEL_DIR/tokenizer,
+# mounted whole at /tokenizer. The two repos ship the same one; fetched
+# fresh from peonist @ e053f488 (tokenizer.json is LFS, byte-identical to
+# the 27B-era copy; the rest are plain git blobs — size-checked only). The
+# serve gate and the deployer refuse a missing file or ANY symlink: an HF
+# cache snapshot symlinks each file into blobs/, and those links dangle
 # inside the container mount.
-FRANK_LARGE_TOKENIZER_FILES="tokenizer.json chat_template.jinja tokenizer_config.json merges.txt vocab.json"
+FRANK_LARGE_TOKENIZER_FILES="tokenizer.json chat_template.jinja tokenizer_config.json merges.txt vocab.json generation_config.json"
 
-# --- engine flags (docs/FLAGS.md of the engine repo; anything unset stays
-# at the image default, which is what was qualified) ------------------------
-HALOGEN_MODEL_ID=frank-large
-HALOGEN_SLOT_CTX=200700
-HALOGEN_KV_SLOTS=1
-HALOGEN_DRAFTER=2
+# --- engine flags (docs/FLAGS.md of the flash-server repo; anything unset
+# stays at the image default, which is what was qualified) ------------------
+# Served model id — Protoss name (wire rename 2026-10-09); the service, unit,
+# scripts and this file keep the frank-large name. OpenWebUI api_configs
+# model_ids for the :7770 backend MUST name this value.
+HALOGEN_MODEL_ID=frank-dragoon
+HALOGEN_CTX=262144
+HALOGEN_KV_POOL_POSITIONS=524288
+HALOGEN_KV_SLOTS=2
+HALOGEN_HOST_RESERVE_GIB=20
+# Spec decode: MTP head is INSIDE the checkpoint (do NOT set HALOGEN_MTP_HEAD);
+# depth 2 is the documented "best all-round" default. HALOGEN_DRAFTER /
+# HALOGEN_DFLASH_* from the 27B engine DO NOT EXIST here.
+HALOGEN_SPEC_ADAPT=0
 # Coupled pair (FLAGS.md table): the cap bounds how long ONE request holds
-# the GPU, the timeout bounds how long the NEXT client waits. 65536 at
-# ~10 t/s serial worst case needs > 6550 s queue timeout; the defaults
-# honor that. Do not lower the timeout alone.
+# the GPU, the timeout bounds how long the NEXT client waits. Do not move
+# one alone (see the client-contract note above).
+HALOGEN_MAX_TOK=8192
 HALOGEN_MAX_TOKENS_CAP=65536
 HALOGEN_QUEUE_TIMEOUT=7200
-# Engine prompt cache: EMPTY = auto (sized from available memory at
-# startup). Pin an explicit MB only to argue with the auto figure — a
-# full-context entry is ~18 GB at 262K, so a too-small pin yields a cache
-# that reports enabled and never hits (the engine warns at startup).
-#HALOGEN_CACHE_MB=
+# Server-side sampler = the card's thinking recipe (the engine now HAS one;
+# requests may still override per-call). Matches models.yml extraBody.
+HALOGEN_TEMPERATURE=1.0
+HALOGEN_TOP_P=0.95
+HALOGEN_TOP_K=20
+# Card's belt-and-braces: the engine pins weights by default
+# (HALOGEN_FLASH_PIN_TRUNK=1; only the retired w4b build honored 0).
+HALOGEN_WEIGHTS_LOCK=1

scripts/frank-large.service

@@ -1,5 +1,5 @@
 [Unit]
-Description=halogen container (peonist-ai/halogen-qwen3.8-27b, DFlash2 spec decode, 1 slot x 200700, text-only), root systemd unit (dankclaw, frank-large, Strix Halo iGPU)
+Description=halogen-flash-server container (Quat3rnion abliterated Swift1.5 of Qwen3.8-Flash-Next v2, MTP spec decode, 2 slots x 200192, vision ON), root systemd unit (dankclaw, frank-large, Strix Halo iGPU)
 After=network-online.target
 Wants=network-online.target
 
@@ -22,8 +22,8 @@ TimeoutStopSec=120
 # would otherwise leave the endpoint down until someone notices.
 Restart=always
 RestartSec=10
-# The engine mmaps 33.4 GiB of weights and keeps a multi-GiB working set
-# on a box that also hosts frank-small, ComfyUI and the Caddy/observability
+# The engine pins ~62 GiB of v2 weights and keeps a multi-GiB working set
+# on a box that also hosts frank-secondary, ComfyUI and the Caddy/observability
 # stack; make it a late OOM candidate, not the first.
 OOMScoreAdjust=-500
 

scripts/frank-primary.env

@@ -1,82 +1,168 @@
-# frank-primary.env — FreeToken (PR #587 ROCm) serving the orcarouter
-# Qwen3.8-Flash-Next-Uncensored-FP8 payload on dankserver :7770.
+# frank-primary.env — radiance (StillDeadcode, Apache-2.0) serving the
+# OFFICIAL Qwen/Qwen3.8-Flash-Next-FP8 checkpoint at NATIVE precision
+# (modelopt block-FP8 throughout, no requantisation) on BOTH R9700s (TP=2),
+# dankserver :7770.
 #
-# 2026-10-05 cutover from the tcclaviger RDNA4 vLLM fork (git tag
-# frank-primary-vllm-shapeG-20261003 is the pre-cutover state; the 362-line
-# vLLM-era env is in that tag). The engine is FreeToken at PR head
-# 77a7201f98f00d93f618c6cf8b81ffe8360e0899 (branch rocm714_torch214), built
-# on the host into a local image by
-# scripts/docker/frank-primary-freetoken.Dockerfile — the PR's own
-# docs/install_amd.md stack (ROCm 7.14 base image, torch 2.11.0+rocm7.14,
-# triton 3.7) for gfx1201.
+# 2026-10-05 cutover from FreeToken (PR #587 ROCm, one card, experts in
+# pinned host RAM; git tag frank-primary-freetoken-pr587-20261005 is the
+# pre-cutover state — its unit/env/serve trio and Dockerfile are all in the
+# tag and rebuildable). Before that: tcclaviger vLLM fork (tag
+# frank-primary-vllm-shapeG-20261003).
 #
-# WHY FREETOKEN: the vLLM fork's expert offload planned VRAM/host tiers per
-# boot from an estimator (util 0.982 law, arena 17.77 GiB / 7,762 slots,
-# ~40k-token GDN state faults) and 1.0 was unbootable. FreeToken keeps ALL
-# expert weights in pinned HOST RAM (~120 GiB) with an LRU GPU cache, sizes
-# KV from an explicit token pool, and serves on ONE R9700 (32 GB) with the
-# second card idle. RAM fit: 246 GiB total, service stopped frees the vLLM
-# container's ~170 GiB; weights ~120 GiB + PLE 47.7 GiB + overhead ≈ 190 GiB.
+# WHY RADIANCE: the engine is C++/HIP with libr4d kernels built for
+# gfx1201, drives `podman run` directly from the digest-pinned
+# stilldeadcode/radiance image (287 MiB; host needs only the amdgpu driver),
+# and its qwen4exp plugin reads the modelopt FP8 encoding NATIVELY
+# (`X.weight_scale_inv` is the scale plane — checkpoint.h). The PLE n-gram
+# table (51B params, 47.7 GiB) is served from pinned host RAM via io_uring
+# reads — Frank's call: "Native FP8. Experts from ram. Pin the PLE to ram."
+# The 4-bit-codebook publisher shape (qwen3.8-next-flash-fp8-iq4r-moe.rad,
+# --host-pool-mib 12288) remains the documented fallback if the native
+# container proves unbootable.
 #
-# PAYLOAD: unchanged — /home/dank/models/Qwen3.8-Flash-Next-Uncensored-FP8,
-# 131 safetensors shards. Quant is standard modelopt block-FP8: 75,264
-# weight_scale_inv tensors (block 128x128), zero input_scale (FreeToken #462
-# covers exactly this export shape), one weight_scale on the PLE ngram
-# embedding. FreeToken loads HF safetensors directly; no FTW conversion.
+# PAYLOAD: /home/dank/models/rad/qwen4exp-fp8-native.rad — a rad-convert
+# NO-RECIPE pure copy of the official 131-shard checkpoint (every weight
+# keeps its block-FP8 encoding; ~161 GiB). The convert runs against a
+# staging dir whose config.json drops `quantization_config`: the descriptor
+# string is only the plugin SELECTION key (spec §2.4 — exact match, no
+# fallback), and no shipped plugin claims (qwen4exp, "fp8"); the shipped
+# qwen4exp plugin claims (qwen4exp, "") which is what recipe-built
+# containers carry. Per-weight encodings are data, not the descriptor —
+# removing the key changes nothing about what the weights hold. Built by
+# scripts/build-frank-primary-rad.sh (staging + convert + rad-info).
 #
-# SERVING FACTS (docs/cli.md, verified against main+PR587):
-# - `ft serve` loads the checkpoint directly; `--served-model-name` keeps the
-#   harness id; reasoning/tool parsers are AUTO (reasoning lands in
-#   reasoning_content — models.yml flips reasoningContentField to match).
-# - KV: --page-size must stay 1 for GDN/SWA models (default); --cache-type
-#   radix auto-picks the GDN-aware variant.
-# - MoE: --moe-strategy auto -> offload (fp8 experts CANNOT use
-#   --moe-cpu-layers; only bf16/nvfp4/mxfp4 have a CPU executor).
-#   --moe-cache-rate holds the GPU LRU fraction of all experts.
-# - `ft ctl cache --kv --moe` grows the pools LIVE on a running server
-#   (shrinking needs a restart); `ft ctl stats` reports pool occupancy.
-# - KV MATH: 12 full-attention layers x 2 KV heads x 256 head_dim x 2 (K+V)
-#   x 2 B = 24 KiB/token (f16 KV; the 36 GDN layers use fixed per-seq state,
-#   not per-token KV). See FRANK_PRIMARY_FT_NUM_TOKENS below for the pool
-#   size and its measured page-size law.
-FRANK_PRIMARY_IMAGE=localhost/frank-primary-freetoken:pr587-77a7201
-FRANK_PRIMARY_MODEL_DIR=/home/dank/models/Qwen3.8-Flash-Next-Uncensored-FP8
-FRANK_PRIMARY_MODEL_SHARDS=131
+# SERVING FACTS (docs/GUIDE.md, docs/DOCKER.md, compose/flashnext.yaml):
+# - The HTTP listener binds AFTER the full load (main.cpp: engine.start()
+#   then engine.serve()) — during load the port refuses connections, so a
+#   bare curl /health gate is honest. /health == /ping: 200 JSON while
+#   serving, 503 only while stopping (core/server/admin.cpp:385).
+# - Reasoning: --reasoning-format defaults to auto, emitting thinking in
+#   `reasoning_content` (config.cpp). Tool calls: the qwen3.8 dialect, from
+#   the chat format. Both need no parser flags.
+# - Sampling: the OpenAI endpoint accepts temperature, top_p, top_k, min_p,
+#   typical_p, presence_penalty, frequency_penalty, repetition_penalty,
+#   repeat_last_n, chat_template_kwargs, reasoning_effort,
+#   enable_thinking, preserve_thinking (core/server/oai.cpp:194-205) —
+#   models.yml's extraBody set is fully inside it. Unknown params are
+#   refused (400), which is why nothing unverified rides the wire.
+# - MTP: the official checkpoint carries the mtp.* head (3,101 tensors,
+#   mtp_num_hidden_layers=1) and the plugin probes draft depth 3 — the
+#   publisher's throughput peak for this model. Stated explicitly with
+#   --num-speculative-tokens 3.
+# - The fused TP all-reduce is declared over max-num-seqs x
+#   (1 + --num-speculative-tokens) rows; 3 x 3 = 9 rows fits either wire.
+# Engine pin: Codeberg tag v1.0.13 (commit 53282fa), Docker Hub tag 1.0.13,
+# index digest below. REJECTED UPGRADE: 1.2.0 (sha256:665775ed1d90de6dbb
+# 25558e07fc60d20328f1346f96f3c6e00845eb763f01fa, tag 1.2.0, commit
+# e95d397) was benched 2026-10-06 on this exact shape (fp8-native rad,
+# vram pair, spec 2, seqs 3, 43.6k greedy probe): decode 29.21 t/s and
+# draft acceptance 0.7123 vs the 1.0.13 control's 30.08 and 0.7549.
+# Acceptance on the greedy harness is deterministic (the control
+# reproduced the recorded 0.755 exactly), so the drop is a real numerics
+# change — the 1.2.0 MoE weight-rotation change (92b8aaf) is the named
+# suspect; decode loss tracks the acceptance ratio (0.943 vs 0.945).
+# The vram-pair translation itself is exact (1.2.0 boot ledger took
+# vram_weights 25.46 GiB / vram_kv 3.46 GiB per card byte-identical to
+# the 1.0.13 ledger at EVC 0.82). Re-test 1.2.0+ when the rotation change
+# is untangled; 1.2.0 also REMOVED --expert-vs-cache-ratio in favour of
+# the --vram-weights-mib / --vram-kv-mib pair (core/config.cpp) — this
+# file already uses the pair, which BOTH pins accept (rollback symmetry).
+FRANK_PRIMARY_IMAGE=stilldeadcode/radiance@sha256:4c3f484564bb2872784e46b0a59cbd71becc9c1e746e301a3596ffae253c322e
+# The .rad container FILE (mounted under /models) and the state dir for the
+# prefix-cache disk tier (mounted rw at /data). The model dir holds ONLY
+# .rad files; the raw HF download and its staging dir live beside it and
+# are never mounted.
+FRANK_PRIMARY_MODEL_DIR=/home/dank/models/rad
+FRANK_PRIMARY_MODEL_FILE=qwen4exp-swift15-w4dn8-i8-hc8m.rad
+FRANK_PRIMARY_STATE_DIR=/home/dank/radiance-state
+# The state dir's tune/ subdirectory holds rad-tune's kernel-selection
+# cache (the engine's selector reads $RADIANCE_HOME/tune/<machine>.tune
+# at boot; $RADIANCE_HOME defaults to /opt/radiance, in the image, so the
+# cache must ride a host mount — serve script mounts it, see the runbook's
+# "Tuning levers").
 # Container identity is the SERVICE name (Frank's call, 2026-10-05): one
-# name across systemctl and podman ps. The vLLM-era name frank-primary-qwen
-# moved into the legacy list so every deploy cleans it up.
+# name across systemctl and podman ps. The runtime changes; the name does
+# not. The legacy list retires every predecessor identity that could hold
+# the GPUs or the port.
 FRANK_PRIMARY_CONTAINER=frank-primary
 FRANK_PRIMARY_LEGACY_CONTAINERS="frank-primary-qwen frank-primary-r9v qwen-fp8 glm-probe"
-FRANK_PRIMARY_SERVED_NAME=frank-primary
-FRANK_PRIMARY_PUBLISH=7770:7770
+# Served model id — Protoss name (wire rename 2026-10-09). The container,
+# unit, scripts and this file keep the frank-primary name above; only the
+# alias on the wire moved. Radiance validates the requested model name, so
+# every client (harness, bench scripts, probes) MUST send this value.
+FRANK_PRIMARY_SERVED_NAME=frank-archon
+FRANK_PRIMARY_PORT=7770
+# Frank's chat-template call: froggeric's fixed Qwen3.8 template (the
+# repo's root chat_template.jinja, vendored here; the checkpoint's own
+# template is baked into the .rad by rad-convert and this overrides it).
+# Radiance derives the reasoning/tool-call reply format from it.
+FRANK_PRIMARY_TEMPLATE=/home/dank/serverAppConfigs/scripts/qwen38-froggeric-v22.5.jinja
 
-# --- FreeToken engine knobs (first-boot conservative, resize live) -----
-# Single GPU: the R9700 at index 0. HIP_VISIBLE_DEVICES=0 hides card 1 so
-# the in-container index 0 IS the right card; --gpu 0 selects it.
-FRANK_PRIMARY_FT_GPU=0
-# KV pool in TOKENS (not bytes — vLLM-era FRANK_PRIMARY_KV_BYTES is gone).
-# 299,968 tokens x 24 KiB = 7.0 GiB; clears the harness worst case
-# (177,356 tokens) and one NATIVE 262,144-token conversation + full budget.
-# MEASURED FIRST BOOT (2026-10-05): the auto attention backend is
-# qsa_sparse, which OVERRIDES page size to 64, so --num-tokens must be a
-# MULTIPLE OF 64 — 300,000 was refused ("nearest valid values: 299968 or
-# 300032"). Grows live with `ft ctl cache --kv <n>`; shrink needs a
-# restart.
-FRANK_PRIMARY_FT_NUM_TOKENS=299968
-# Concurrent decode slots. 2 is the first-boot conservative value; the
-# radix pool admits ~2 full-length 200,704-token conversations. Raise with
-# --max-running-requests only after a real-traffic look at slot occupancy.
-FRANK_PRIMARY_FT_MAX_RUNNING=2
-# Per-request output budget ceiling: the harness's maxTokens (36,864).
-# Requests that omit max_tokens get this default instead of the engine's
-# 32,768.
-FRANK_PRIMARY_FT_MAX_OUTPUT_TOKENS=36864
-# GPU expert LRU cache as a FRACTION of all experts (~120 GiB fp8 total ->
-# ~12 GiB VRAM at 0.10). First-boot conservative; resize live with
-# `ft ctl cache --moe` after reading the boot ledger + ft ctl stats.
-FRANK_PRIMARY_FT_MOE_RATE=0.10
-# Escape hatch for boot-ladder experiments (YaRN, --max-prefill-length,
-# --moe-cpu-threads, --sampling-defaults none, ...). Unset -> no extra
-# argv. Keep multi-flag values as one string; the serve script expands it
-# unquoted on purpose.
-FRANK_PRIMARY_FT_EXTRA_ARGS=
+# --- engine shape (first-boot conservative; the boot ledger arbitrates) --
+# Tensor parallel: BOTH R9700s. The board swap (2026-09-20, ProArt B850
+# x8/x8 on root ports) made collectives legal on this host; radiance's TP
+# wire is libr4d's own, not RCCL.
+FRANK_PRIMARY_TP=2
+# Lossy WHT 6-bit wire for large all-reduces (publisher's production choice:
+# +5.6-6.3% prefill, ~-1.3% 8-stream decode at TP2). min-kb stays the
+# engine default.
+FRANK_PRIMARY_TP_WIRE=wht6
+# Decode slots. 2 is Frank's first-boot value ("use parallelism = 2 to
+# start"); raise only after a real-traffic look at slot occupancy and the
+# KV pool's headroom line. The recurrent-state cost scales with it (54
+# MiB/copy/card, 2 copies with speculation).
+FRANK_PRIMARY_MAX_SEQS=3
+# Served window. Native is 262,144; 200,000 is the publisher's production
+# value (compose) and what models.yml's contextWindow must quote — a longer
+# prompt is refused by the engine, never truncated.
+FRANK_PRIMARY_MAX_MODEL_LEN=200000
+FRANK_PRIMARY_PLACEMENT=expert_tiered
+# Pinned host pool for the expert rows that do not fit VRAM. Native FP8
+# experts are 112.8 GiB total (56.4 GiB per rank slice) vs the 4-bit
+# publisher shape's ~29 GiB — this pool is the cost of native precision.
+# The boot ledger prints the pool's coverage ("the pool backs N% of the
+# worst case ... M full-length sessions"); resize from that line, not from
+# arithmetic.
+FRANK_PRIMARY_HOST_POOL_MIB=40960
+# VRAM left unclaimed per card (publisher default 96 MiB).
+FRANK_PRIMARY_GPU_HEADROOM_MIB=96
+# VRAM split of claimed memory per card, MiB (0 = derive from the card).
+# Engine 1.2.0 REMOVED --expert-vs-cache-ratio; these two knobs replace
+# it. The values transcribe the live 1.0.13 boot ledger at ratio 0.82:
+# vram_weights 25.46 GiB, vram_kv 3.46 GiB per card -> 26071 / 3543.
+# Bracket arms scale the KV term linearly in (1-r) from the 0.82 anchor
+# with weights fixed: ratio 0.90 -> KV 1968, ratio 0.95 -> KV 984. The
+# boot ledger arbitrates; re-derive after any payload change.
+FRANK_PRIMARY_VRAM_WEIGHTS_MIB=26071
+FRANK_PRIMARY_VRAM_KV_MIB=3543
+# KV cache in FP8 (7.32 KiB/token at this model's 12 full-attention layers,
+# 2 KV heads, 256 head_dim, K+V).
+FRANK_PRIMARY_KV_DTYPE=fp8
+# Spec decode: the checkpoint's own MTP head at the publisher's depth-3
+# operating point (probe() returns 3 when mtp_num_hidden_layers > 0; the
+# flag states it so the ledger records the intent).
+FRANK_PRIMARY_SPEC_TOKENS=2
+FRANK_PRIMARY_MAX_BATCHED_TOKENS=2048
+# Prefix cache tiers (publisher pattern; disk tier sized for 2 slots).
+FRANK_PRIMARY_PREFIX_HOST_MIB=4096
+FRANK_PRIMARY_PREFIX_DISK_MIB=65536
+# Frank's pin law: the PLE n-gram table (47.7 GiB) serves from pinned host
+# RAM the cards read. `ram` REFUSES the start if the memlock limit is too
+# small — the unit's LimitMEMLOCK=infinity is the other half of this law.
+FRANK_PRIMARY_NGRAM_PLACEMENT=ram
+# TTM preflight: the pinned ledger (2 x 40 GiB pools + 47.7 GiB PLE + 4 GiB
+# prefix host) is ~132 GiB. ttm's pages_limit/pages_max cap the GPU-mappable
+# pinned pages; the current cap (32,252,917 pages = ~123 GiB) is BELOW the
+# plan, so the serve script refuses to start until both are raised. This is
+# the floor it enforces (in MiB), with ~8 GiB of margin.
+FRANK_PRIMARY_TTM_MIN_MIB=140000
+# Escape hatch for boot-ladder experiments. Unset -> no extra argv. Keep
+# multi-flag values as one string; the serve script expands it unquoted on
+# purpose.
+FRANK_PRIMARY_EXTRA_ARGS=
+# The .rad's minimum size in MiB (deployer gate; a truncated convert is a
+# guaranteed long-timeout boot instead of a fast failure). The converted
+# container measured 177,109 MiB (1,857,127,997,440 B = 173.0 GiB; the
+# plan-only estimate 172.76 GiB covers the data planes but not the metadata
+# tail) - floor sits just under the payload.
+FRANK_PRIMARY_RAD_MIN_MIB=110000

scripts/frank-primary.service

@@ -1,26 +1,25 @@
-# frank-primary.service — FreeToken (PR #587 ROCm) endpoint (orcarouter
-# Qwen3.8-Flash-Next-Uncensored-FP8, 131-shard payload) on ONE R9700
-# (gfx1201), dankserver :7770. 2026-10-05 cutover from the tcclaviger vLLM
-# fork (git tag frank-primary-vllm-shapeG-20261003 is the pre-cutover
-# state; the retired unit/env/serve trio is in that tag).
+# frank-primary.service — radiance (StillDeadcode, Apache-2.0) endpoint
+# (OFFICIAL Qwen/Qwen3.8-Flash-Next-FP8 at NATIVE block-FP8 precision, the
+# rad-convert container qwen4exp-fp8-native.rad) on BOTH R9700s (TP=2,
+# wht6 wire), dankserver :7770. 2026-10-05 cutover from FreeToken (git tag
+# frank-primary-freetoken-pr587-20261005 is the pre-cutover state; the
+# retired unit/env/serve trio and Dockerfile are in that tag).
 #
 # Same port, same served alias, same container identity as the unit name;
 # the harness models.yml id is unchanged. The serve script drives
-# `podman run` directly (the image carries `ft serve`) and then waits on
-# the container. The image is built ON THE HOST from
-# scripts/docker/frank-primary-freetoken.Dockerfile (FreeToken PR head
-# 77a7201 in the PR's own ROCm 7.14 base image).
-#
-# WHY A ROOT UNIT (not a rootless quadlet): FreeToken pins ~120 GiB of
-# expert weights plus the ~47.7 GiB PLE ngram table in HOST RAM. Pinned
-# pages are charged against RLIMIT_MEMLOCK; dank's login session on this
-# host carries 8 MiB and a user quadlet is capped by the user manager's own
-# hard limit — raising that means restarting user@1000.service and bouncing
-# every user container on the host. A root unit grants
-# LimitMEMLOCK=infinity to the dank process via PID1, and the image stays
-# in dank's rootless podman storage.
+# `podman run` directly: the digest-pinned stilldeadcode/radiance image
+# (287 MiB, kernels prebuilt for gfx1201) has the engine as its
+# entrypoint. The image lives in dank's ROOTLESS podman storage.
+# WHY A ROOT UNIT (not a rootless quadlet): radiance pins ~132 GiB of HOST
+# RAM (2 x 40 GiB expert pools + the 47.7 GiB PLE ngram table + the prefix
+# host tier). Pinned pages are charged against RLIMIT_MEMLOCK; dank's login
+# session on this host carries 8 MiB and a user quadlet is capped by the
+# user manager's own hard limit — raising that means restarting
+# user@1000.service and bouncing every user container on the host. A root
+# unit grants LimitMEMLOCK=infinity to the dank process via PID1; the
+# engine still runs out of dank's rootless podman storage.
 [Unit]
-Description=frank-primary — FreeToken (PR587) Qwen3.8-Flash-Next-Uncensored-FP8 on 1x R9700
+Description=frank-primary — radiance Qwen3.8-Flash-Next-FP8 (native) on 2x R9700 TP=2
 After=network-online.target
 Wants=network-online.target
 # Never let the start-rate limiter give up after a crash loop (a failed LLM
@@ -30,21 +29,21 @@ StartLimitIntervalSec=0
 [Service]
 User=dank
 Group=dank
-# The expert LRU + PLE pin ~170 GiB of host RAM; an RLIMIT_MEMLOCK smaller
-# than the pinned set fails the load mid-start. PID1 grants infinity here —
-# parity with the fleet's other LLM units.
+# The engine pins ~132 GiB of host RAM (expert pools + PLE table); an
+# RLIMIT_MEMLOCK smaller than the pinned set fails the load mid-start.
+# PID1 grants infinity here — parity with the fleet's other LLM units.
 LimitMEMLOCK=infinity
 Environment="FRANK_PRIMARY_ENV_FILE=/home/dank/serverAppConfigs/scripts/frank-primary.env"
 ExecStart=/home/dank/serverAppConfigs/scripts/serve-frank-primary.sh
 # systemd stop: wait alone cannot stop the container child; stop it
-# explicitly. ft serve drops connections on SIGTERM; no graceful drain
-# contract is claimed (the OpenAI front end holds no queue to drain —
-# in-flight decodes are lost, which a stop during a bench window accepts).
+# explicitly. No graceful drain contract is claimed (the OpenAI front end
+# holds no queue to drain — in-flight decodes are lost, which a stop
+# during a bench window accepts; radiance takes SIGTERM on the engine
+# process and podman reaps the container).
 ExecStop=/usr/bin/podman stop -t 120 frank-primary
-# Boot cost: 131-shard FP8 page-in + PLE pin + expert upload. vLLM-era
-# budget (1800 s) carried forward; the FreeToken first boot measured here
-# once it ran (see the runbook for the real number).
-TimeoutStartSec=1800
+# Boot cost: .rad load (weights + PLE upload) + expert host-pool pin. The
+# publisher's compose healthcheck allows a 15-minute start_period; the
+# vLLM-era budget (1800 s) carried forward.
 # Restart=always, NOT on-failure (the 2026-10-01 vLLM lesson carries over:
 # a clean API-server exit after a worker fault reads as success to
 # systemd). `always` restarts on any container death; an explicit

scripts/frank-secondary.env

@@ -0,0 +1,62 @@
+# frank-secondary — llama.cpp (master, CUDA sm_86) serving
+# HauhauCS/Qwen3.5-9B-Uncensored-HauhauCS-Aggressive at Q6_K on the RTX 3090,
+# :7771.
+#
+# 2026-10-07 (Frank): swap from Gemma4-26B-A4B QAT Q4_K_M (one 200,704 slot)
+# to Qwen3.5-9B Q6_K at TWO 200,704-token conversations, EXACT (f16) KV,
+# MTP disabled (SPEC_TYPE=none), log verbosity 4.
+#
+# Architecture — read from the GGUF's own header (HTTP range read, not the
+# model card): arch qwen35, 32 layers, dense 9B, full_attention_interval 4
+# ⇒ 8 full-attention layers (3,7,11,...,31) + 24 Gated DeltaNet linear layers,
+# 16 attention heads / 4 KV heads × 256 dim, ssm conv_kernel 4 / state_size
+# 128 / group_count 16 / time_step_rank 32 / inner_size 4096, native 262,144,
+# vocab 248,320, file_type 18 (Q6_K). Tensor-type histogram: Q6_K×250 +
+# F32×177 — standard types only, so the stock master build loads it (no
+# custom-quant lock-in like the ROCmFPX era). NO nextn/mtp tensors exist, so
+# in-file MTP is impossible here; SPEC_TYPE=none is forced by the payload.
+#
+# KV rate law: 8 full layers × 4 KV heads × 256 dim = 8,192 elements per
+# token per cache. f16 K + f16 V = 32,768 B/token = 32 KiB/token, so the
+# 401,408-token pool is 12.25 GiB. The 24 DeltaNet layers cost a fixed
+# recurrent state per SLOT (not per token) — that is why two 200k slots fit
+# where the 26B MoE fit only one.
+#
+# Feasibility comes from the predecessor's MEASURED boots, not a formula:
+# MiMo-V2.6-Distill-Qwen-9B (identical qwen35 geometry, 7,793,710,624 B) at
+# 2×200,704 f16 K + f16 V measured 21,276 used / 2,849 free at ubatch 1024
+# and 21,846 / 2,279 at ubatch 2048. This file is 415 MiB lighter in weights,
+# its BF16 mmproj is 3 MiB heavier ⇒ projected ~21,430 used / ~2,700 free.
+# The verbosity-4 boot ledger is the referee.
+#
+# Ubatch 2048 is the measured CUDA optimum (measured on the MiMo predecessor
+# at this exact arch + shape: +4.6 % prefill at 8.9k depth, decode flat, cost
+# 616 MiB). 1024 is the fallback rung if the boot lands under the 2 GiB floor.
+LLAMA_BIN=/home/dank/GitPackages/llama-master/build-cuda/bin/llama-server
+MODEL_FILE=/home/dank/models/Qwen35-9B-HauhauCS-Q6K/Qwen3.5-9B-Uncensored-HauhauCS-Aggressive-Q6_K.gguf
+MMPROJ_FILE=/home/dank/models/Qwen35-9B-HauhauCS-Q6K/mmproj-Qwen3.5-9B-Uncensored-HauhauCS-Aggressive-BF16.gguf
+# Served model id — the harness/family name (Protoss rename completed to the
+# wire 2026-10-09: harness ids + homepage cards 2026-10-08, /v1/models aliases
+# this day). The SERVICE, unit, scripts and this env file keep the
+# frank-secondary name; only the alias on the wire changed. Consumers that
+# key on the served id: OpenWebUI (model row, access_grant, api_configs
+# model_ids) and the harness models.yml.
+ALIAS=frank-zealot
+BIND_ADDR=0.0.0.0
+LISTEN_PORT=7771
+# CTX_SIZE is the POOL llama.cpp divides across slots: 401,408 / 2 = 200,704
+# per conversation. MUST stay divisible by PARALLEL (the seam enforces it) and
+# the harness contextWindow quotes the PER-SLOT number — a longer prompt is
+# answered with a 400, never truncated.
+CTX_SIZE=401408
+PARALLEL=2
+BATCH_SIZE=4096
+UBATCH_SIZE=2048
+CACHE_TYPE_K=f16
+CACHE_TYPE_V=f16
+SPEC_TYPE=none
+NGL=99
+# Log verbosity threshold (llama.cpp -lv). 4 = debug: KV layout, buffer sizes
+# and graph capture land in the journal, which is what the VRAM-fit forensics
+# read. Levels above 4 flood the journal.
+VERBOSITY=4

scripts/frank-secondary.service

@@ -0,0 +1,19 @@
+[Unit]
+Description=llama.cpp Qwen3.5-9B-Uncensored-HauhauCS-Aggressive Q6_K GGUF (qwen35 hybrid, 2x200704 slots, f16 K+V, MTP off, mmproj vision) on the RTX 3090 (dankclaw, frank-secondary)
+After=network-online.target
+Wants=network-online.target
+
+[Service]
+Type=simple
+User=dank
+Group=dank
+# CUDA pins its staging buffers, so the process needs the memlock budget even
+# though the weights themselves are copied into VRAM.
+LimitMEMLOCK=infinity
+Environment="FRANK_SECONDARY_ENV_FILE=/home/dank/serverAppConfigs/scripts/frank-secondary.env"
+ExecStart=/home/dank/serverAppConfigs/scripts/serve-frank-secondary.sh
+Restart=on-failure
+RestartSec=10
+
+[Install]
+WantedBy=multi-user.target

scripts/frank-small.env

@@ -1,136 +0,0 @@
-# frank-small — llama.cpp (master, CUDA sm_86) serving
-# ornith-ai/Ornith-1.5-9B-GGUF:Q8_0 on the RTX 3090, :7771.
-#
-# 2026-10-05 (Frank): the payload moved off bartowski/MiMo-V2.6 (Q6_K) to
-# ornith-ai/Ornith-1.5-9B Q8_0 (9,786,060,384 B, LFS oid 22086870...).
-# Header-verified facts that re-price the shape: qwen35 hybrid like MiMo, but
-# 33 blocks (not 32) with full_attention_interval 4 => NINE full-attention
-# layers (MiMo had 8), 16 Q heads / 4 KV heads, k/v length 256, native window
-# 262144, dense (no expert keys), nextn_predict_layers=1 (an MTP head is IN
-# the file — capability only; no spec decode ships, same law as before).
-#
-# THE KV CONTRACT STAYS f16 K + f16 V — NO KV QUANTIZATION (Frank, 2026-10-05,
-# overruling a written q8_0-V proposal). The arithmetic that makes this hard:
-#   KV rate 9 attn layers x 4 heads x 256 x 2 B = 18 KiB/token K, same again
-#   for V at f16 => 36 KiB/token (MiMo was ~24.5 measured: one fewer
-#   full-attn layer AND one fewer KV head).
-#   Weights +1,900 MiB (9,334 vs Q6_K's 7,432). Measured free at the Q6_K
-#   4096/2048 shape was 2,279 MiB; Q8_0 at f16/f16 does not fit that shape —
-#   the deficit is made up the way Frank ordered: UBATCH FIRST (2048 -> 1024,
-#   the measured 616 MiB rung), then POOL STEPS, then measure. Guess-and-check
-#   on the live card is the accepted method for llama.cpp sizing here: boot,
-#   read nvidia-smi free, step the pool (12,288-token increments, ~432 MiB
-#   each at the 36 KiB/token rate) until the 2-3 GiB floor holds. Compute
-#   buffers allocate at graph capture during BOOT, so a failure is loud and
-#   fast, not a surprise at the first long prompt.
-#
-# Previous shape history (MiMo era, for the rollback trail): 2026-10-03 the
-# rung went Q5_K_M -> Q6_K with the pool HELD at 401,408; the freed third
-# slot funded exact f16 V first; the ~3.5 GiB still free funded the +875 MiB
-# of weights.
-#
-# 2026-10-03 (Frank): TWO streams instead of three, each one WIDE — 200,704
-# tokens per conversation, above the 200,700 the harness compacts at — and the
-# KV cache is now FULLY UNQUANTIZED (f16 K, f16 V). The two moves are the same
-# move: llama.cpp divides --ctx-size across --parallel slots, so the slot given
-# up is exactly the VRAM that pays for unquantized V. No further quantization
-# anywhere on this endpoint.
-#
-# Three measurements on this card, this runtime, price the terms:
-#   2 x 262,144, no projector: 20,190 MiB used / 4,386 free
-#     KV = 524,288 tok x 24.5 KiB = 12,800 MiB => non-KV (weights 6,557 +
-#     DeltaNet state ~25/slot + compute buffers + CUDA graphs) = 7,390 MiB
-#   3 x 196,608, projector on: 22,972 MiB used / 1,153 free
-#     KV = 14,113 MiB => non-KV WITH the vision tower = 8,859 MiB
-#   3 x 180,224, projector on: 21,684 MiB used / 2,441 free (the 2026-10-02
-#     shape) => non-KV = 8,748 MiB
-# Two slots give back one DeltaNet state (~25 MiB). Priced at f16 K+V
-# (32 KiB/token) this pool is 12,544 MiB, so the prediction is
-# 12,544 + ~8,800 = ~20.9 GiB used, ~3.1 GiB free — the band the 2026-10-02
-# shape hit, bought with precision instead of a slot. The 1024 ubatch
-# re-prices the compute buffers upward, so MEASURE after any change: if the
-# boot lands under 2 GiB free, step the pool down in 12,288-token
-# (384 MiB at this rate) increments.
-#
-# KV arithmetic: attention KV exists only in the 8 full-attention layers
-# (4 KV heads x 256 head_dim x 2 B) = 16 KiB/token for K and the same again for
-# V => 32 KiB/token, so 12,288 tokens (= 6144/slot) is one 384 MiB step — that
-# is the ladder to walk if this moves again. Two native windows (2 x 262,144)
-# are NOT reachable with full-precision V: 524,288 x 32 KiB = 16.1 GiB of KV
-# against a card with ~15.7 GiB of KV room once weights, the vision tower, the
-# DeltaNet states and the graphs are loaded. 200,704 per stream is what full
-# precision buys; q8_0 on V would buy 262,144 instead.
-#
-# The asymmetric-KV contract this endpoint was created for (Frank, 2026-10-01
-# night: f16 K + q8_0 V) is now spent — it existed to make a third slot fit,
-# and the third slot is gone. The flags stay explicit so the choice is visible
-# in the argv either way.
-LLAMA_BIN=/home/dank/GitPackages/llama-master/build-cuda/bin/llama-server
-MODEL_FILE=/home/dank/models/Ornith-Q8/Ornith-1.5-9B-Q8_0.gguf
-
-# Vision: the Ornith Q8_0 GGUF ships no `v.*` tensors either (dense-9B byte
-# arithmetic: 9.79 GB ≈ 9B params x ~1.06 B/param leaves no room for a tower),
-# so the projector file IS the vision capability (mmproj-Ornith-1.5-9B-BF16
-# .gguf, 921,704,672 B, LFS oid 626f9f90..., fetched by
-# scripts/download-frank-small.sh). Empty string = text-only on purpose.
-MMPROJ_FILE=/home/dank/models/Ornith-Q8/mmproj-Ornith-1.5-9B-BF16.gguf
-
-# Fleet endpoint: same port and alias as every frank-small before it, so the
-# harness models.yml id and the homepage probe do not move.
-ALIAS=frank-small
-BIND_ADDR=0.0.0.0
-LISTEN_PORT=7771
-
-# ONE conversation at the NATIVE 262,144 window (Frank, 2026-10-05). The
-# two-slot shape died under Ornith and the NO-QUANTIZE law, by arithmetic:
-# the KV rate is the ARCHITECTURE's, not the quant's — 9 full-attn layers x
-# 4 KV heads x 256 dim x 2 B = 36 KiB/token at f16 K+V (MiMo measured 24.5),
-# so 2 x 200,704 wants ~14.1 GiB of KV on top of +1,900 MiB of Q8 weights
-# against 2,279 MiB measured free; ubatch 1024 claws back 616, and no
-# pool step fits BOTH the 2-3 GiB floor and the harness compaction
-# invariant (slot >= 191,148). One native slot fits with room; the window
-# per conversation RISES 200,704 -> 262,144. The cost: a second concurrent
-# request queues behind the released slot (subagent concurrency 2 -> 1).
-CTX_SIZE=262144
-PARALLEL=1
-
-# Compute-buffer geometry. This endpoint passed neither flag until 2026-10-03,
-# so it ran on llama.cpp's own defaults (2048/512); the pair is now 4096/2048.
-# Both are tunables because the pair is a measurement, not a law — and the
-# measurement is CARD-SPECIFIC. On the 2x R9700 layer-split sibling the ubatch
-# curve peaks at 512 and every step up costs GiB of compute buffer for no
-# speed (see frank-medium-batch-ubatch). On THIS CUDA 3090 the curve climbs
-# monotonically to 2048: 710 -> 1188 t/s prefill from ubatch 1024 to 2048 on
-# the 35B MoE sweep, 1944 -> 2423 t/s on the Ornith 8k sweep, with decode flat
-# to under 1 t/s on every rung. Ubatch is a prefill knob; decode never moves.
-#
-# The logical batch is free — two independent sweeps agree that at a fixed
-# ubatch, 2048/4096/8192 give identical prefill speed AND identical peak VRAM,
-# because the compute buffers are sized by the ubatch alone. Doubling it costs
-# nothing and buys nothing; it is here because it is the value the pair was
-# measured at.
-#
-# THE RISK IS ALL IN THE UBATCH. The 1024 -> 2048 step cost ~1,244 MiB of peak
-# graph VRAM on the 35B MoE (7,330 -> 8,574 MiB); this 9B hybrid has far
-# smaller compute tensors, so the delta should be well under that, but the
-# predecessor Q5_K_M shape recorded "ubatch 2048 failed the scratch boot" at
-# ~3,571 MiB free. The Q8_0 rung ships UBATCH 1024 (Frank, 2026-10-05: lower
-# ubatch FIRST — the measured 616 MiB step is the first VRAM the Q8 weights
-# claw back, before any pool move). Compute buffers are allocated at graph
-# capture during BOOT, so a failure is loud and fast, not a surprise at the
-# first long prompt. MEASURE nvidia-smi free immediately after the deploy:
-# under ~1.5 GiB, or a boot failure, step the POOL down next (12,288-token
-# increments, ~432 MiB each at the Ornith 36 KiB/token f16 KV rate).
-BATCH_SIZE=4096
-UBATCH_SIZE=1024
-
-# The KV contract (2026-10-05, Frank): f16 K + f16 V — NO KV quantization.
-# The q8_0-V arithmetic was written and OVERRULED. The Q8 weights eat the
-# VRAM that funded f16 V on 2026-10-03, so the remedy ladder is UBATCH FIRST
-# (2048 -> 1024, the measured 616 MiB rung), then POOL STEPS — and
-# guess-and-check on the live card is the accepted method: boot, read
-# nvidia-smi, step down until the floor holds. --flash-attn stays pinned on
-# in the serve script: it is the fast path for f16 KV.
-CACHE_TYPE_K=f16
-CACHE_TYPE_V=f16
-NGL=99

scripts/frank-small.service

@@ -1,19 +0,0 @@
-[Unit]
-Description=llama.cpp MiMo-V2.6-Distill-Qwen-9B Q6_K GGUF (qwen35 hybrid, 2x200704, unquantized f16 KV, mmproj vision) on the RTX 3090 (dankclaw, frank-small)
-After=network-online.target
-Wants=network-online.target
-
-[Service]
-Type=simple
-User=dank
-Group=dank
-# CUDA pins its staging buffers, so the process needs the memlock budget even
-# though the weights themselves are copied into VRAM.
-LimitMEMLOCK=infinity
-Environment="FRANK_SMALL_ENV_FILE=/home/dank/serverAppConfigs/scripts/frank-small.env"
-ExecStart=/home/dank/serverAppConfigs/scripts/serve-frank-small.sh
-Restart=on-failure
-RestartSec=10
-
-[Install]
-WantedBy=multi-user.target

scripts/hfget.sh

@@ -0,0 +1,107 @@
+#!/bin/sh
+# hfget.sh -- fetch a Hugging Face repo with curl and jq, and nothing else.
+#
+# The engine and its tooling do not depend on Python, so `huggingface-cli` is not assumed. The API
+# gives a file listing as JSON and every file has a stable resolve/ URL, which is all a downloader
+# needs.
+#
+#   scripts/hfget.sh <repo-id> [dest-dir] [revision]
+#
+# Set HF_TOKEN for a gated or private repo. Re-running resumes: a file whose local size already
+# matches the listing is skipped and a short one is continued with `curl -C -`, so an interrupted
+# transfer of a 300 GB checkpoint costs only what it had not finished.
+#
+# WEIGHT DUPLICATES ARE SKIPPED. Many repos carry the same tensors several times over -- safetensors
+# beside the older pickle .bin, an ONNX export, a GGUF conversion -- and on a large checkpoint each
+# copy is hundreds of gigabytes of the same numbers. When the listing holds any .safetensors, every
+# other weight format is left behind.
+#
+# HFGET_JOBS concurrent transfers, default 4. These are long sequential reads: a few streams
+# saturate the link and more only makes the destination seek.
+set -e
+
+# ---- child mode: one file, named by "<size><tab><path>" in $1, everything else inherited -------
+# sh cannot export a function, so parallelism is one re-entry of this script per file under
+# `xargs -P` rather than a worker pool.
+if [ -n "$HFGET_CHILD" ]; then
+    size=${1%%	*}; path=${1#*	}
+    out=$HFGET_DEST/$path
+    if [ -f "$out" ]; then
+        cur=$(wc -c < "$out")
+        if [ "$cur" = "$size" ]; then echo "  = $path"; exit 0; fi
+        echo "  + $path (resuming at $cur of $size)"
+    else
+        echo "  > $path"
+    fi
+    mkdir -p "$(dirname "$out")"
+    if [ -n "$HF_TOKEN" ]; then
+        curl -sSL --fail --retry 5 --retry-delay 5 --retry-all-errors -C - \
+             -H "Authorization: Bearer $HF_TOKEN" \
+             -o "$out" "https://huggingface.co/$HFGET_REPO/resolve/$HFGET_REV/$path"
+    else
+        curl -sSL --fail --retry 5 --retry-delay 5 --retry-all-errors -C - \
+             -o "$out" "https://huggingface.co/$HFGET_REPO/resolve/$HFGET_REV/$path"
+    fi
+    got=$(wc -c < "$out")
+    [ "$got" = "$size" ] || { echo "hfget: $path is $got bytes, listing says $size" >&2; exit 1; }
+    exit 0
+fi
+
+# ---- parent ------------------------------------------------------------------------------------
+repo=$1
+[ -n "$repo" ] || { echo "usage: hfget.sh <repo-id> [dest-dir] [revision]" >&2; exit 2; }
+dest=${2:-$HOME/models/$repo}
+rev=${3:-main}
+jobs=${HFGET_JOBS:-4}
+command -v jq >/dev/null || { echo "hfget: jq is required" >&2; exit 2; }
+
+echo "hfget: listing $repo@$rev"
+# THE LISTING IS PAGINATED AND THE PAGE SIZE IS SMALL. A large checkpoint runs to a hundred-odd
+# shards and the API returns fifty entries with a `Link: ...; rel="next"` header. Taking only the
+# first page is the worst possible failure here: every file it did fetch is the right length, so
+# the size check passes and the converter is handed a checkpoint missing two thirds of its
+# tensors. Follow the cursor until there is no next link.
+api="https://huggingface.co/api/models/$repo/tree/$rev?recursive=true&expand=true"
+listing='[]'
+page=0
+while [ -n "$api" ]; do
+    hdr=$(mktemp "${TMPDIR:-/tmp}/hfhdr.XXXXXX")
+    if [ -n "$HF_TOKEN" ]; then
+        body=$(curl -sSL --fail-with-body -D "$hdr" -H "Authorization: Bearer $HF_TOKEN" "$api")
+    else
+        body=$(curl -sSL --fail-with-body -D "$hdr" "$api")
+    fi
+    [ "$(printf '%s' "$body" | jq -r 'type')" = "array" ] || {
+        rm -f "$hdr"
+        echo "hfget: $(printf '%s' "$body" | jq -r '.error // .message // .')" >&2
+        echo "hfget: a gated or private repo needs HF_TOKEN in the environment" >&2; exit 1; }
+    listing=$(printf '%s\n%s' "$listing" "$body" | jq -s 'add')
+    api=$(tr -d '\r' < "$hdr" | sed -n 's/^[Ll]ink: *<\([^>]*\)>; *rel="next".*/\1/p' | head -1)
+    rm -f "$hdr"
+    page=$((page + 1))
+    [ "$page" -gt 200 ] && { echo "hfget: listing did not terminate after 200 pages" >&2; exit 1; }
+done
+echo "hfget: $page page(s) of listing"
+have_st=$(printf '%s' "$listing" | jq '[.[] | select(.path | endswith(".safetensors"))] | length')
+sel='.[] | select(.type == "file")'
+[ "$have_st" -gt 0 ] && sel="$sel"' | select((.path | test("\\.(bin|pth|msgpack|h5|onnx|onnx_data|gguf)$")) | not)'
+
+list=$(mktemp "${TMPDIR:-/tmp}/hfget.XXXXXX")
+trap 'rm -f "$list"' EXIT INT TERM
+printf '%s' "$listing" | jq -r "$sel"' | "\(.size)\t\(.path)"' > "$list"
+
+n=$(wc -l < "$list")
+[ "$n" -gt 0 ] || { echo "hfget: the listing has no files" >&2; exit 1; }
+need=$(awk -F'\t' '{s += $1} END {printf "%.1f", s / 1073741824}' "$list")
+mkdir -p "$dest"
+free=$(df -BG --output=avail "$dest" | tail -1 | tr -dc '0-9')
+echo "hfget: $n file(s), $need GiB -> $dest"
+[ "$have_st" -gt 0 ] && echo "hfget: safetensors present, pickle weight formats skipped"
+echo "hfget: $free GiB free on the destination filesystem"
+awk -v n="$need" -v f="$free" 'BEGIN { if (n > f) { print "hfget: THE DOWNLOAD DOES NOT FIT"; exit 1 } }'
+
+export HFGET_CHILD=1 HFGET_DEST="$dest" HFGET_REPO="$repo" HFGET_REV="$rev"
+self=$(cd "$(dirname "$0")" && pwd)/$(basename "$0")
+tr '\n' '\0' < "$list" | xargs -0 -P "$jobs" -I{} sh "$self" "{}" \
+    || { echo "hfget: incomplete -- re-run to resume" >&2; exit 1; }
+echo "hfget: complete -- $(du -sh "$dest" | cut -f1) in $dest"

scripts/kld-gate-swift15.sh

@@ -0,0 +1,91 @@
+#!/usr/bin/env bash
+# kld-gate-swift15.sh — RUNS ON DANKSERVER. Score the quantized candidate
+# against the BF16 reference and apply the pre-approved gate:
+#   mean KL <= 0.03 AND top-1 agreement >= 95%.
+# Same engine (1.0.13) and same boot shape as the reference RECORD
+# (run-swift15-bf16.sh kld): disk-tier, no speculation, deterministic, fp8 KV.
+# --kld-ref reads its tokens from the reference (no --kld-corpus here — the
+# (run-swift15-bf16.sh kld): 1.0.13, tp 2, disk-tier, fp8 KV, no speculation,
+# deterministic, max-model-len 16384, vram-kv 3072, 4 slots. Two deviations
+# are forced by the w4 file: MNBT 2048 (the w4 kernels stream from the file
+# tier ONLY at MNBT<=2048 — at the recording's 32768 the engine demands a
+# widened form: "DID NOT FIT ... stored form is not the container's"), and
+# pool 40960 (the recording's 12 GiB pool predates the w4 tables; at 12288 the
+# gate_up slab cannot be cached). Chunk size is softmax-invariant, so the
+# MNBT delta is float-order noise riding on the KL floor.
+set -uo pipefail
+PIN=sha256:4c3f484564bb2872784e46b0a59cbd71becc9c1e746e301a3596ffae253c322e
+C=swift15gate
+LOG=/home/dank/logs/swift15_kldgate.log
+KLD_REF_DIR=/home/dank/corpora/kld-ref
+KLD_OUT=/home/dank/radiance-state/kld-gate.json
+
+[ -d "$KLD_REF_DIR" ] || { echo "missing reference: $KLD_REF_DIR"; exit 2; }
+: > "$KLD_OUT"
+cp -f /home/dank/serverAppConfigs/scripts/qwen38-froggeric-v22.5.jinja \
+      /home/dank/models/tpl.jinja 2>/dev/null || true
+sudo systemctl stop frank-primary >/dev/null 2>&1 || true
+podman rm -f "$C" >/dev/null 2>&1 || true
+
+podman run --rm --name "$C" \
+    --network host --init \
+    --security-opt seccomp=unconfined \
+    --ulimit memlock=-1:-1 \
+    --device /dev/kfd --device /dev/dri \
+    -v /home/dank/models:/models:ro \
+    -v /home/dank/radiance-state:/data \
+    -v "$KLD_REF_DIR:/data/kld-ref:ro" \
+    "stilldeadcode/radiance@$PIN" \
+    --model /models/rad/qwen4exp-swift15-w4dn8-i8-hc8m.rad \
+    --tp 2 --placement expert_tiered --weights-disk-tier \
+    --host-pool-mib 40960 --deterministic --gpu-headroom-mib 768 \
+    --num-speculative-tokens 0 \
+    --kv-cache-dtype fp8 --max-model-len 16384 \
+    --ngram-placement ram \
+    --override-chat-template /models/tpl.jinja \
+    --kld-ref /data/kld-ref --kld-out /data/kld-gate.json \
+    --vram-kv-mib 3072 \
+    --max-num-seqs 4 --max-num-batched-tokens 2048 \
+    2>&1 | tee "$LOG"
+rc=${PIPESTATUS[0]}
+[ "$rc" -eq 0 ] || { echo "KLD GATE BOOT FAILED rc=$rc; log: $LOG"; exit "$rc"; }
+
+python3 - "$KLD_OUT" "$LOG" <<'PY'
+import json, sys, re
+out, log = sys.argv[1], sys.argv[2]
+def num(pat):
+    m = re.findall(pat, open(log, errors="replace").read(), re.I)
+    return float(m[-1]) if m else None
+kl = top1 = None
+try:
+    d = json.load(open(out))
+    def find(obj, keys):
+        if isinstance(obj, dict):
+            for k, v in obj.items():
+                if any(s in k.lower() for s in keys) and isinstance(v, (int, float)):
+                    return float(v)
+            for v in obj.values():
+                r = find(v, keys)
+                if r is not None: return r
+        return None
+    kl = find(d, ("mean_kl", "mean kl", "kl_mean", "mean"))
+    top1 = find(d, ("top1", "top_1", "top-1", "agreement"))
+    if top1 is not None and top1 <= 1.0:
+        top1 *= 100.0
+except Exception as e:
+    print("report JSON unusable (%s); falling back to log" % e)
+if kl is None:
+    kl = num(r"mean[_ ]kl[^0-9]*([0-9]*\.?[0-9]+(?:e[-+][0-9]+)?)")
+if top1 is None:
+    top1 = num(r"top[-_ ]?1[^0-9]*([0-9]*\.?[0-9]+)")
+if kl is None or top1 is None:
+    print("KLD GATE PARSE FAILED: mean_kl=%s top1=%s — inspect %s / %s" % (kl, top1, out, log))
+    sys.exit(2)
+print("KLD GATE: mean KL = %.4f (max 0.03) | top-1 = %.2f%% (min 95.0)" % (kl, top1))
+ok = (kl <= 0.03) and (top1 >= 95.0)
+print("KLD GATE " + ("PASS" if ok else "FAIL"))
+sys.exit(0 if ok else 1)
+PY
+gate=$?
+echo "kld-gate complete rc=$gate; log: $LOG report: $KLD_OUT"
+exit "$gate"

scripts/kld-then-calib.sh

@@ -0,0 +1,40 @@
+#!/bin/bash
+# kld-then-calib.sh — RUNS ON DANKSERVER. Campaign chain: wait for the KLD
+# record to finish, verify it, then boot the 1.2.0 calibration server and
+# push the 10M-token corpus. One GPU, so the phases serialize; prod is
+# restored by each run script's trap.
+set -u
+KLOG=/home/dank/logs/swift15-kld-driver.log
+CLOG=/home/dank/logs/swift15-calib-driver.log
+S=/home/dank/serverAppConfigs-swift15/scripts
+# Phase 1: KLD record (SKIP_KLD=1 when the record is already complete —
+# kld.json's rows count is the receipt; a mid-run script swap can kill the
+# wrapper with rc=127 after the engine exited clean).
+if [ "${SKIP_KLD:-0}" != 1 ]; then
+    while ! grep -q "exited rc=" "$KLOG" 2>/dev/null; do sleep 60; done
+    RC=$(grep -o "exited rc=[0-9]*" "$KLOG" | tail -1)
+    if [ "$RC" != "exited rc=0" ]; then echo "KLD FAILED ($RC); calib NOT started"; exit 1; fi
+fi
+N=$(find /home/dank/corpora/kld-ref -type f | wc -l)
+SZ=$(du -sm /home/dank/corpora/kld-ref | cut -f1)
+echo "KLD OK files=$N size_mib=$SZ"
+[ "$N" -ge 5 ] && [ "$SZ" -ge 1000 ] || { echo "kld-ref looks thin; calib NOT started"; exit 1; }
+grep -q '"rows": 50235' /home/dank/corpora/kld-ref/kld.json || {
+    echo "kld.json rows != 50235; calib NOT started"; exit 1; }
+
+# Phase 2: calibration serve (1.2.0) + corpus push.
+nohup bash "$S/run-swift15-bf16.sh" calib > "$CLOG" 2>&1 &
+CP=$!
+for _ in $(seq 1 160); do
+    sleep 15
+    curl -sf localhost:7770/health >/dev/null 2>&1 && break
+done
+if ! curl -sf localhost:7770/health >/dev/null 2>&1; then
+    echo "CALIB BOOT FAILED; see /home/dank/logs/swift15_bf16.log"; kill "$CP" 2>/dev/null; exit 1
+fi
+echo "CALIB UP, pushing corpus"
+python3 "$S/calib-push.py" --port 7770 --parallel 16 --container swift15ref --max-rounds 6
+PUSH=$?
+kill "$CP" 2>/dev/null
+sleep 10
+echo "CALIB_CHAIN_DONE push=$PUSH grams=$(find /home/dank/corpora/calib -name 'gram.r*.bin' 2>/dev/null | wc -l)"

scripts/make-calib-corpus.py

@@ -0,0 +1,317 @@
+#!/usr/bin/env python3
+"""Build the two Swift1.5 campaign corpora (calibration + KLD).
+
+calib.jsonl — 10M tokens of agent-shaped RAW text (45% code, 30% prose,
+25% tool-call JSON), one {"prompt": text} per line, pushed to
+/v1/completions with max_tokens=1 during the calibration run (the Hessian
+tap is of the prompt tokens; docs/MOE-W4.md step 2). Code and prose mix
+the repo checkout with HF dataset parquets (Frank's call 2026-10-06:
+local material is only ~1.6M unique tokens; MOE-W4 wants 10M):
+nampdn-ai/tiny-codes (code) and Salesforce/wikitext wikitext-103-raw
+(prose). Tool calls are synthesized from commands harvested from the
+repo's own scripts plus a fleet-ops set, with argument-shaped variants.
+
+kld.jsonl — 1.8M tokens, one {"prompt", "score_from", "source"} per
+line; each prompt is a multi-turn conversation rendered through the
+Froggeric v22.5 template with a >= --kld-min-prompt (8192) token target,
+and the last 256 tokens (the tail of the assistant answer) are the
+scored span: score_from = n - 256. At 8k-token prompts the 1.8M corpus
+is ~220 prompts = ~56k scored positions ~= 25 GB of record, matching
+the published Flash-Next KLD scale (83k positions, MOE-W4.md). The KLD
+reference run and every candidate run consume the SAME file (TOOLS.md
+--kld-record / --kld-ref). The KLD corpus is LOCAL material only, so the
+file-level disjointness rule (fnv1a64(path) % 5 == 0 -> KLD pool) is
+untouched by the HF additions.
+
+Deps: tokenizers, jinja2, pyarrow (venv /home/dank/venvs/radcorpus).
+Usage: make-calib-corpus.py --repo ROOT --tokenizer-json PATH
+    --template PATH --out-dir DIR [--hf-code-parquet F,F]
+    [--hf-prose-parquet F,F] [--calib-tokens 10000000]
+    [--kld-tokens 1800000]
+"""
+import argparse
+import json
+import os
+import random
+import re
+
+from jinja2 import Environment, FileSystemLoader
+from tokenizers import Tokenizer
+
+CODE_EXT = (".sh", ".py", ".jinja", ".recipe", ".env", ".example",
+            ".yaml", ".yml", ".bats", ".service", ".container", ".toml")
+JSONL_EXTS = (".jsonl", ".json")
+DOC_DIR = "serverAppConfigs-docs"
+
+FLEET_CMDS = [
+    "systemctl status frank-primary --no-pager",
+    "journalctl -u frank-primary -n 100 --no-pager",
+    "podman ps --format '{{.Names}} {{.Status}}'",
+    "podman logs --tail 50 frank-primary",
+    "curl -s http://192.168.0.235:7770/health",
+    "curl -s http://192.168.0.235:7770/metrics | grep radiance_",
+    "df -h /home/dank | tail -1",
+    "free -g",
+    "sudo systemctl restart frank-primary",
+    "cd /home/dank/serverAppConfigs && git pull --ff-only",
+    "./scripts/deploy.sh dankserver --service frank-primary",
+    "./tests/run.sh 2>&1 | tail -5",
+    "jq . /home/dank/models/rad/index.json | head -40",
+    "python3 -c \"import json; print(json.load(open('config.json'))['num_hidden_layers'])\"",
+    "ip neigh show 192.168.0.235",
+    "amdgpu_top --device-info 2>/dev/null | head -20",
+    "grep -E 'MemAvailable' /proc/meminfo",
+    "sha256sum model-00001-of-00131.safetensors",
+    "git log --oneline -10",
+    "awk '{s+=$1} END {print s}' shard_sizes.txt",
+]
+TOOL_NAMES = ["bash", "read", "grep", "web_search", "edit", "write"]
+WORKDIRS = ["/home/dank", "/home/dank/serverAppConfigs", "/home/dank/models",
+            "/etc/systemd/system", "/var/log"]
+DESCS = ["check service state", "tail the engine ledger", "list containers",
+          "query health", "read metrics", "check disk", "check memory",
+          "restart the endpoint", "sync the checkout", "run deployer",
+          "run the test seams", "inspect the index", "probe the peer"]
+
+
+def fnv(s: str) -> int:
+    h = 0xcbf29ce484222325
+    for b in s.encode():
+        h = ((h ^ b) * 0x100000001b3) & 0xFFFFFFFFFFFFFFFF
+    return h
+
+
+def harvest_cmds(repo: str, rng: random.Random) -> list[str]:
+    cmds = list(FLEET_CMDS)
+    pat = re.compile(r"^\s*(?:sudo\s+)?([a-z][\w./-]*(?:\s+\S+){1,6})\s*$")
+    for root, dirs, files in os.walk(repo):
+        dirs[:] = [d for d in dirs if not d.startswith(".")]
+        for f in files:
+            if not f.endswith(".sh"):
+                continue
+            try:
+                for line in open(os.path.join(root, f), encoding="utf-8",
+                                 errors="replace"):
+                    m = pat.match(line.rstrip())
+                    if m and len(m.group(1)) > 12:
+                        cmds.append("ssh dank@192.168.0.235 '"
+                                    + m.group(1)[:90] + "'")
+            except OSError:
+                pass
+    rng.shuffle(cmds)
+    return cmds[:4000]
+
+
+def tool_call(rng: random.Random, cmds: list[str]) -> str:
+    """One agent turn: 2-4 tool calls with argument-shaped JSON."""
+    parts = []
+    for _ in range(rng.randint(2, 4)):
+        args: dict = {"command": rng.choice(cmds)}
+        if rng.random() < 0.4:
+            args["cwd"] = rng.choice(WORKDIRS)
+        if rng.random() < 0.3:
+            args["timeout"] = rng.choice([10, 30, 60, 300, 600])
+        if rng.random() < 0.5:
+            args["description"] = rng.choice(DESCS)
+        parts.append('<tool_call>{"name": "%s", "arguments": %s}<|im_end|>'
+                     % (rng.choice(TOOL_NAMES),
+                        json.dumps(args, ensure_ascii=False)))
+    return "\n".join(parts)
+
+
+def load_parquet_texts(path: str, kind: str, rng: random.Random,
+                       cap: int) -> list[str]:
+    import pyarrow.parquet as pq
+    d = pq.read_table(path).to_pydict()
+    n = len(next(iter(d.values())))
+    idx = range(n) if n <= cap else rng.sample(range(n), cap)
+    out = []
+    for i in idx:
+        if kind == "code":
+            if "content" in d:
+                frag = d["content"][i]
+            else:
+                frag = ("// %s\n### Instruction:\n%s\n### Response:\n%s"
+                        % (d.get("file_name", [""])[i], d["prompt"][i],
+                           d["code"][i] or d["response"][i]))
+        else:
+            frag = d["text"][i]
+        if frag and frag.strip():
+            out.append(frag[:8000])
+    rng.shuffle(out)
+    return out[:cap]
+
+
+def main() -> None:
+    ap = argparse.ArgumentParser(description=__doc__)
+    ap.add_argument("--repo", required=True)
+    ap.add_argument("--tokenizer-json", required=True)
+    ap.add_argument("--template", required=True)
+    ap.add_argument("--out-dir", required=True)
+    ap.add_argument("--calib-tokens", type=int, default=10_000_000)
+    ap.add_argument("--kld-tokens", type=int, default=1_800_000)
+    ap.add_argument("--chunk-tokens", type=int, default=8192)
+    ap.add_argument("--score-tail", type=int, default=256)
+    ap.add_argument("--kld-min-prompt", type=int, default=8192)
+    ap.add_argument("--hf-code-parquet", default="")
+    ap.add_argument("--hf-prose-parquet", default="")
+    ap.add_argument("--seed", type=int, default=20261006)
+    a = ap.parse_args()
+
+    rng = random.Random(a.seed)
+    tokenizer = Tokenizer.from_file(a.tokenizer_json)
+    env = Environment(loader=FileSystemLoader(os.path.dirname(a.template)),
+                      trim_blocks=True, lstrip_blocks=True)
+    env.filters["tojson"] = lambda v: json.dumps(v, ensure_ascii=False)
+    env.filters["raise_exception"] = lambda m: (_ for _ in ()).throw(
+        ValueError(m))
+    tname = os.path.basename(a.template)
+
+    code, prose, jsonl = [], [], []
+    for root, dirs, files in os.walk(a.repo):
+        dirs[:] = [d for d in dirs if not d.startswith(".")]
+        for f in sorted(files):
+            p = os.path.relpath(os.path.join(root, f), a.repo)
+            if DOC_DIR in p and f.endswith(".md"):
+                prose.append(p)
+            elif p.startswith(DOC_DIR):
+                pass
+            elif f.endswith(CODE_EXT):
+                code.append(p)
+            elif f.endswith(JSONL_EXTS) and f != "package-lock.json":
+                jsonl.append(p)
+    # file-level split: bucket 0 -> KLD pool, 1..4 -> calibration pool
+    pools: dict[str, tuple[list, list]] = {}
+    for cls, lst in (("code", code), ("prose", prose), ("tools", jsonl)):
+        pools[cls] = ([p for p in lst if fnv(p) % 5 == 0],
+                      [p for p in lst if fnv(p) % 5 != 0])
+
+    def text_of(p: str) -> str:
+        path = os.path.join(a.repo, p)
+        raw = open(path, encoding="utf-8", errors="replace").read()
+        if p.endswith(JSONL_EXTS):
+            lines = [json.loads(l)["content"] for l in raw.splitlines()
+                     if l.strip() and "content" in l]
+            raw = "\n".join(lines)
+        toks = tokenizer.encode(raw, add_special_tokens=False).ids
+        return tokenizer.decode(toks[:2400], skip_special_tokens=True)
+
+    cmds = harvest_cmds(a.repo, rng)
+    # fragment pools per class, corpus-split first
+    frags: dict[str, dict[str, list[str]]] = {}
+    for cls in pools:
+        kld_files, cal_files = pools[cls]
+        fr = {}
+        src = {"kld": kld_files, "cal": cal_files}
+        if cls == "tools":
+            fr["kld"] = [tool_call(rng, cmds) for _ in range(600)]
+            fr["cal"] = [tool_call(rng, cmds) for _ in range(40000)]
+        else:
+            fr["kld"] = [text_of(p) for p in src["kld"]]
+            fr["cal"] = [text_of(p) for p in src["cal"]]
+        frags[cls] = fr
+    # per-class fragment caps: prose rows are short (~66 tok) so need many;
+    # codeparrot files are long (~2.6k tok) so a small cap suffices
+    hfcap = {"code": 40000, "prose": 60000, "tools": 0}
+    for cls, spec in (("code", a.hf_code_parquet),
+                      ("prose", a.hf_prose_parquet)):
+        for path in [p for p in spec.split(",") if p]:
+            frags[cls]["cal"].extend(
+                load_parquet_texts(path, cls, rng, cap=hfcap[cls]))
+    for cls in frags:
+        rng.shuffle(frags[cls]["cal"])
+
+    def toks(s: str) -> int:
+        return len(tokenizer.encode(s, add_special_tokens=False).ids)
+
+    def emit_calib() -> tuple[int, list[dict]]:
+        targets = {"code": .45, "prose": .30, "tools": .25}
+        rows: list[dict] = []
+        for cls, frac in targets.items():
+            want = int(a.calib_tokens * frac)
+            pool = frags[cls]["cal"]
+            if not pool:
+                continue
+            got, i, buf, nbuf = 0, 0, [], 0
+            while got < want:
+                frag = pool[i % len(pool)]
+                i += 1
+                n = toks(frag)
+                if nbuf + n > a.chunk_tokens and buf:
+                    rows.append({"prompt": "\n".join(buf), "cls": cls})
+                    got += nbuf
+                    buf, nbuf = [], 0
+                    if got >= want:
+                        break
+                buf.append(frag)
+                nbuf += n
+                if i > len(pool) * 40:
+                    break  # pool exhausted with repeats; stop
+            if buf and got < want:
+                rows.append({"prompt": "\n".join(buf), "cls": cls})
+                got += nbuf
+            print(f"calib {cls}: {got} tokens, {i} fragments")
+        rng.shuffle(rows)
+        return len(rows), [{k: v for k, v in r.items() if k == "prompt"}
+                           for r in rows]
+
+    def emit_kld() -> tuple[int, list[dict]]:
+        rows, total = [], 0
+        classes = ["code", "prose", "tools"]
+        i = {c: 0 for c in classes}
+        guard = 0
+        while total < a.kld_tokens and guard < 5000:
+            guard += 1
+            msgs, srcs = [], []
+            for t in range(rng.choice([2, 3, 4])):
+                cls = classes[(len(rows) + t) % 3]
+                pool = frags[cls]["kld"]
+                if not pool:
+                    continue
+                frag = pool[i[cls] % len(pool)]
+                i[cls] += 2
+                msgs.append({"role": "user",
+                             "content": "Summarize and explain this "
+                                        f"engineering material:\n{frag}"})
+                msgs.append({"role": "assistant",
+                             "content": frag[:4000]})
+                srcs.append(cls)
+            rendered = env.get_template(tname).render(
+                messages=msgs, add_generation_prompt=False)
+            n = toks(rendered)
+            if n < a.kld_min_prompt:
+                continue
+            rows.append({"prompt": rendered,
+                         "score_from": n - a.score_tail,
+                         "source": "+".join(sorted(set(srcs)))})
+            total += n
+        return len(rows), rows
+
+    os.makedirs(a.out_dir, exist_ok=True)
+    nc, rows = emit_calib()
+    with open(os.path.join(a.out_dir, "calib.jsonl"), "w") as f:
+        for r in rows:
+            f.write(json.dumps(r) + "\n")
+    nk, krows = emit_kld()
+    with open(os.path.join(a.out_dir, "kld.jsonl"), "w") as f:
+        for r in krows:
+            f.write(json.dumps(r) + "\n")
+    scored = sum(len(tokenizer.encode(r["prompt"], add_special_tokens=False).ids)
+                 - r["score_from"] for r in krows)
+    manifest = {
+        "seed": a.seed, "template": tname,
+        "tokenizer": os.path.basename(a.tokenizer_json),
+        "calib_prompts": nc, "kld_prompts": nk,
+        "kld_scored_positions": scored,
+        "kld_score_tail": a.score_tail,
+        "disjoint_file_pools": "local KLD material only; HF code feeds calib",
+        "calib_mix": {"code": .45, "prose": .30, "tools": .25},
+        "hf_code": a.hf_code_parquet, "hf_prose": a.hf_prose_parquet,
+    }
+    with open(os.path.join(a.out_dir, "manifest.json"), "w") as f:
+        json.dump(manifest, f, indent=1)
+    print(json.dumps(manifest, indent=1))
+
+
+if __name__ == "__main__":
+    main()

scripts/qwen38-froggeric-v22.5.jinja

@@ -0,0 +1,460 @@
+{%- set template_version = "qwen3.8-froggeric-v22.5" %}
+{%- set _tool_format = tool_call_format if tool_call_format is defined else 'xml' %}
+{%- set image_count = namespace(value=0) %}
+{%- set video_count = namespace(value=0) %}
+{%- set add_vision_id = add_vision_id if add_vision_id is defined else false %}
+{%- set enable_thinking = enable_thinking if enable_thinking is defined else true %}
+{%- set auto_disable_thinking_with_tools = auto_disable_thinking_with_tools if auto_disable_thinking_with_tools is defined else false %}
+{%- if preserve_reasoning is defined and preserve_reasoning is not none %}
+    {%- set _preserve_thinking = preserve_reasoning %}
+{%- elif preserve_thinking is defined and preserve_thinking is not none %}
+    {%- set _preserve_thinking = preserve_thinking %}
+{%- else %}
+    {%- set _preserve_thinking = true %}
+{%- endif %}
+{%- set max_tool_arg_chars = max_tool_arg_chars if max_tool_arg_chars is defined else 0 %}
+{%- set max_tool_response_chars = max_tool_response_chars if max_tool_response_chars is defined else 0 %}
+{%- set _default_reasoning_effort = 'medium' %}
+{%- set _has_tools = (tools is defined and tools and tools is iterable and tools is not mapping) %}
+{%- set _effort_raw = (reasoning_effort | string | lower) if reasoning_effort is defined and reasoning_effort is not none else _default_reasoning_effort %}
+{%- set _initial_thinking = enable_thinking %}
+{%- if _effort_raw in ('none', 'off') %}
+    {%- set _initial_thinking = false %}
+    {%- set _initial_effort = 'medium' %}
+{%- elif _effort_raw in ('minimal', 'low') %}
+    {%- set _initial_effort = 'low' %}
+{%- elif _effort_raw in ('high', 'xhigh', 'max', 'ultracode', 'extreme') %}
+    {%- set _initial_effort = 'xhigh' %}
+{%- else %}
+    {%- set _initial_effort = 'medium' %}
+{%- endif %}
+{%- set ns_state = namespace(thinking=_initial_thinking, effort=_initial_effort) %}
+{%- if auto_disable_thinking_with_tools and _has_tools %}
+    {%- set ns_state.thinking = false %}
+{%- endif %}
+{%- for msg in messages %}
+    {%- if msg.role == 'system' or msg.role == 'developer' or msg.role == 'user' %}
+        {%- if msg.content is string %}
+            {%- if '<|think_off|>' in msg.content %}
+                {%- set ns_state.thinking = false %}
+            {%- elif '<|think_on|>' in msg.content %}
+                {%- set ns_state.thinking = true %}
+            {%- elif '<|think_xhigh|>' in msg.content or '<|think_high|>' in msg.content or '<|think_ultracode|>' in msg.content or '<|think_extreme|>' in msg.content or '<|think_max|>' in msg.content %}
+                {%- set ns_state.thinking = true %}
+                {%- set ns_state.effort = 'xhigh' %}
+            {%- elif '<|think_low|>' in msg.content or '<|think_minimal|>' in msg.content %}
+                {%- set ns_state.thinking = true %}
+                {%- set ns_state.effort = 'low' %}
+            {%- elif '<|think_medium|>' in msg.content %}
+                {%- set ns_state.thinking = true %}
+                {%- set ns_state.effort = 'medium' %}
+            {%- endif %}
+        {%- elif msg.content is iterable and msg.content is not mapping %}
+            {%- for item in msg.content %}
+                {%- if item is string %}
+                    {%- set _item_text = item %}
+                {%- elif item is mapping and 'text' in item and item.text is string %}
+                    {%- set _item_text = item.text %}
+                {%- else %}
+                    {%- set _item_text = '' %}
+                {%- endif %}
+                {%- if _item_text %}
+                    {%- if '<|think_off|>' in _item_text %}
+                        {%- set ns_state.thinking = false %}
+                    {%- elif '<|think_on|>' in _item_text %}
+                        {%- set ns_state.thinking = true %}
+                    {%- elif '<|think_xhigh|>' in _item_text or '<|think_high|>' in _item_text or '<|think_ultracode|>' in _item_text or '<|think_extreme|>' in _item_text or '<|think_max|>' in _item_text %}
+                        {%- set ns_state.thinking = true %}
+                        {%- set ns_state.effort = 'xhigh' %}
+                    {%- elif '<|think_low|>' in _item_text or '<|think_minimal|>' in _item_text %}
+                        {%- set ns_state.thinking = true %}
+                        {%- set ns_state.effort = 'low' %}
+                    {%- elif '<|think_medium|>' in _item_text %}
+                        {%- set ns_state.thinking = true %}
+                        {%- set ns_state.effort = 'medium' %}
+                    {%- endif %}
+                {%- endif %}
+            {%- endfor %}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- set reasoning_instructions = '' %}
+{%- if ns_state.thinking %}
+    {%- if ns_state.effort == 'xhigh' %}
+        {%- set reasoning_instructions = 'Reasoning effort is set to xhigh. Please think carefully through the task, validate key assumptions, consider plausible alternatives, and prioritize correctness, consistency, and clarity in the final answer.' %}
+    {%- elif ns_state.effort == 'low' %}
+        {%- set reasoning_instructions = 'Reasoning effort is set to low. Keep your thinking brief and focused, moving directly to the conclusion without unnecessary elaboration.' %}
+    {%- endif %}
+{%- endif %}
+{%- macro render_content(content, do_vision_count, is_system_content=false) %}
+    {%- if content is string %}
+        {{- content }}
+    {%- elif content is iterable and content is not mapping %}
+        {%- for item in content %}
+            {%- if item is mapping %}
+                {%- if item.type == 'image' or 'image' in item or 'image_url' in item %}
+                    {%- if is_system_content %}
+                        {{- raise_exception('System message cannot contain images.') }}
+                    {%- endif %}
+                    {%- if do_vision_count %}
+                        {%- set image_count.value = image_count.value + 1 %}
+                    {%- endif %}
+                    {%- if add_vision_id %}
+                        {{- 'Picture ' ~ image_count.value ~ ': ' }}
+                    {%- endif %}
+                    {{- '<|vision_start|><|image_pad|><|vision_end|>' }}
+                {%- elif item.type == 'video' or 'video' in item or 'video_url' in item %}
+                    {%- if is_system_content %}
+                        {{- raise_exception('System message cannot contain videos.') }}
+                    {%- endif %}
+                    {%- if do_vision_count %}
+                        {%- set video_count.value = video_count.value + 1 %}
+                    {%- endif %}
+                    {%- if add_vision_id %}
+                        {{- 'Video ' ~ video_count.value ~ ': ' }}
+                    {%- endif %}
+                    {{- '<|vision_start|><|video_pad|><|vision_end|>' }}
+                {%- elif 'text' in item %}
+                    {{- item.text }}
+                {%- else %}
+                    {{- raise_exception('Unexpected item type in content.') }}
+                {%- endif %}
+            {%- else %}
+                {{- item | string }}
+            {%- endif %}
+        {%- endfor %}
+    {%- elif content is none or content is undefined %}
+        {{- '' }}
+    {%- else %}
+        {{- raise_exception('Unexpected content type.') }}
+    {%- endif %}
+{%- endmacro %}
+{%- if not messages %}
+    {{- raise_exception('No messages provided.') }}
+{%- endif %}
+{%- set head = namespace(count=0, seen_non_system=false) %}
+{%- for message in messages %}
+    {%- set _is_sys = (message.role == 'system' or message.role == 'developer') %}
+    {%- if _is_sys and not head.seen_non_system %}
+        {%- set head.count = head.count + 1 %}
+    {%- else %}
+        {%- set head.seen_non_system = true %}
+    {%- endif %}
+{%- endfor %}
+{%- set sys_state = namespace(content='') %}
+{%- for message in messages[:head.count] %}
+    {%- set _part = render_content(message.content, false, true) | trim %}
+    {%- if '<|think_off|>' in _part %}{%- set _part = _part.split('<|think_off|>') | join('') | trim %}{%- endif %}
+    {%- if '<|think_on|>' in _part %}{%- set _part = _part.split('<|think_on|>') | join('') | trim %}{%- endif %}
+    {%- if '<|think_xhigh|>' in _part %}{%- set _part = _part.split('<|think_xhigh|>') | join('') | trim %}{%- endif %}
+    {%- if '<|think_high|>' in _part %}{%- set _part = _part.split('<|think_high|>') | join('') | trim %}{%- endif %}
+    {%- if '<|think_ultracode|>' in _part %}{%- set _part = _part.split('<|think_ultracode|>') | join('') | trim %}{%- endif %}
+    {%- if '<|think_extreme|>' in _part %}{%- set _part = _part.split('<|think_extreme|>') | join('') | trim %}{%- endif %}
+    {%- if '<|think_max|>' in _part %}{%- set _part = _part.split('<|think_max|>') | join('') | trim %}{%- endif %}
+    {%- if '<|think_medium|>' in _part %}{%- set _part = _part.split('<|think_medium|>') | join('') | trim %}{%- endif %}
+    {%- if '<|think_low|>' in _part %}{%- set _part = _part.split('<|think_low|>') | join('') | trim %}{%- endif %}
+    {%- if '<|think_minimal|>' in _part %}{%- set _part = _part.split('<|think_minimal|>') | join('') | trim %}{%- endif %}
+    {%- if _part %}
+        {%- if sys_state.content %}
+            {%- set sys_state.content = sys_state.content ~ '\n\n' ~ _part %}
+        {%- else %}
+            {%- set sys_state.content = _part %}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- set _sc = sys_state.content %}
+{%- set _msgs = messages[head.count:] %}
+{%- if _has_tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if reasoning_instructions %}
+        {{- reasoning_instructions + '\n\n' }}
+    {%- endif %}
+    {{- '# Tools\n\nYou have access to the following functions:\n\n<tools>' }}
+    {%- for tool in tools %}
+        {{- '\n' }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- '\n</tools>' }}
+    {%- if _tool_format == 'json' %}
+        {{- '\n\nIf you choose to call a function ONLY reply in the following format with NO suffix:\n\n' }}
+        {%- if ns_state.thinking %}
+            {{- '<think>\nBrief explanation of tool call\n</think>\n' }}
+        {%- endif %}
+        {{- '<tool_call>\n{"name": "example_function_name", "arguments": {"example_parameter_1": "value_1", "example_parameter_2": "This is the value for the second parameter"}}\n</tool_call>\n\n<IMPORTANT>\nReminder:\n' }}
+        {%- if ns_state.thinking %}
+            {{- '- You can use the <think></think> block to plan your next tool call OR to synthesize data and formulate your final response to the user.\n- ALL explanation and reasoning MUST be placed strictly inside the <think></think> block.\n' }}
+        {%- endif %}
+        {{- '- Function calls MUST follow the specified format: a single JSON object with "name" and "arguments" keys inside <tool_call></tool_call> XML tags.\n' }}
+        {%- if ns_state.thinking %}
+            {{- '- If you choose to call a tool, you MUST output the <tool_call> block IMMEDIATELY after thinking, with NO conversational text before it.\n' }}
+        {%- else %}
+            {{- '- If you choose to call a tool, you MUST output the <tool_call> block IMMEDIATELY, with NO conversational text before it.\n' }}
+        {%- endif %}
+        {{- '- The <tool_call> tag MUST be at the very beginning of a new line, with NO spaces or indentation before it.\n- To call multiple functions, output a separate, completely closed <tool_call></tool_call> block for EACH function. Do NOT nest <tool_call> blocks.\n- If you have all necessary data, provide your final answer directly to the user without any tool call.\n</IMPORTANT>' }}
+    {%- else %}
+        {{- '\n\nIf you choose to call a function ONLY reply in the following format with NO suffix:\n\n' }}
+        {%- if ns_state.thinking %}
+            {{- '<think>\nBrief explanation of tool call\n</think>\n' }}
+        {%- endif %}
+        {{- '<tool_call>\n<function=example_function_name>\n<parameter=example_parameter_1>\nvalue_1\n</parameter>\n<parameter=example_parameter_2>\nThis is the value for the second parameter\nthat can span\nmultiple lines\n</parameter>\n</function>\n</tool_call>\n\n<IMPORTANT>\nReminder:\n' }}
+        {%- if ns_state.thinking %}
+            {{- '- You can use the <think></think> block to plan your next tool call OR to synthesize data and formulate your final response to the user.\n- ALL explanation and reasoning MUST be placed strictly inside the <think></think> block.\n' }}
+        {%- endif %}
+        {{- '- Function calls MUST follow the specified format: an inner <function=...></function> block must be nested within <tool_call></tool_call> XML tags.\n' }}
+        {%- if ns_state.thinking %}
+            {{- '- If you choose to call a tool, you MUST output the <tool_call> block IMMEDIATELY after thinking, with NO conversational text before it.\n' }}
+        {%- else %}
+            {{- '- If you choose to call a tool, you MUST output the <tool_call> block IMMEDIATELY, with NO conversational text before it.\n' }}
+        {%- endif %}
+        {{- '- The <tool_call> and <function> tags MUST be at the very beginning of a new line, with NO spaces or indentation before them.\n- To call multiple functions, output a separate, completely closed <tool_call></tool_call> block for EACH function. Do NOT nest <tool_call> blocks.\n- If you have all necessary data, provide your final answer directly to the user without any tool call.\n</IMPORTANT>' }}
+    {%- endif %}
+    {%- if _sc %}
+        {{- '\n\n' + _sc }}
+    {%- endif %}
+    {{- '<|im_end|>\n' }}
+{%- else %}
+    {%- if _sc %}
+        {{- '<|im_start|>system\n' + (reasoning_instructions + '\n\n' if reasoning_instructions else '') + _sc + '<|im_end|>\n' }}
+    {%- elif reasoning_instructions %}
+        {{- '<|im_start|>system\n' + reasoning_instructions + '<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- set _last_idx = _msgs | length - 1 %}
+{%- set ns = namespace(multi_step_tool=true, last_query_index=_last_idx) %}
+{%- for message in _msgs[::-1] %}
+    {%- set index = (_msgs | length - 1) - loop.index0 %}
+    {%- if ns.multi_step_tool and message.role == 'user' %}
+        {%- set _rc = render_content(message.content, false) | trim %}
+        {%- if not (_rc.startswith('<tool_response>') and _rc.endswith('</tool_response>')) %}
+            {%- set ns.multi_step_tool = false %}
+            {%- set ns.last_query_index = index %}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if ns.multi_step_tool %}
+    {%- if _last_idx > 50 %}
+        {%- set ns.last_query_index = _last_idx %}
+    {%- else %}
+        {%- set ns.last_query_index = 0 %}
+    {%- endif %}
+{%- endif %}
+{%- set ns2 = namespace(prev_role='', consecutive_failures=0) %}
+{%- for message in _msgs %}
+    {%- set is_system = (message.role == "system" or message.role == "developer") %}
+    {%- set content = render_content(message.content, true, is_system) | trim %}
+    {%- if is_system or message.role == 'user' %}
+        {%- if '<|think_off|>' in content %}{%- set content = content.split('<|think_off|>') | join('') | trim %}{%- endif %}
+        {%- if '<|think_on|>' in content %}{%- set content = content.split('<|think_on|>') | join('') | trim %}{%- endif %}
+        {%- if '<|think_xhigh|>' in content %}{%- set content = content.split('<|think_xhigh|>') | join('') | trim %}{%- endif %}
+        {%- if '<|think_high|>' in content %}{%- set content = content.split('<|think_high|>') | join('') | trim %}{%- endif %}
+        {%- if '<|think_ultracode|>' in content %}{%- set content = content.split('<|think_ultracode|>') | join('') | trim %}{%- endif %}
+        {%- if '<|think_extreme|>' in content %}{%- set content = content.split('<|think_extreme|>') | join('') | trim %}{%- endif %}
+        {%- if '<|think_max|>' in content %}{%- set content = content.split('<|think_max|>') | join('') | trim %}{%- endif %}
+        {%- if '<|think_medium|>' in content %}{%- set content = content.split('<|think_medium|>') | join('') | trim %}{%- endif %}
+        {%- if '<|think_low|>' in content %}{%- set content = content.split('<|think_low|>') | join('') | trim %}{%- endif %}
+        {%- if '<|think_minimal|>' in content %}{%- set content = content.split('<|think_minimal|>') | join('') | trim %}{%- endif %}
+    {%- endif %}
+    {%- if is_system %}
+        {{- '<|im_start|>system\n' + content + '<|im_end|>\n' }}
+    {%- elif message.role == 'user' %}
+        {%- set ns2.consecutive_failures = 0 %}
+        {{- '<|im_start|>user\n' + content + '<|im_end|>\n' }}
+    {%- elif message.role == 'assistant' %}
+        {%- set reasoning_content = '' %}
+        {%- set _explicit_reasoning = '' %}
+        {%- if message.reasoning_content is defined and message.reasoning_content is not none %}
+            {%- if message.reasoning_content is string %}
+                {%- set _explicit_reasoning = message.reasoning_content %}
+            {%- else %}
+                {%- set _explicit_reasoning = message.reasoning_content | string %}
+            {%- endif %}
+        {%- elif message.thinking is defined and message.thinking is not none %}
+            {%- if message.thinking is string %}
+                {%- set _explicit_reasoning = message.thinking %}
+            {%- else %}
+                {%- set _explicit_reasoning = message.thinking | string %}
+            {%- endif %}
+        {%- elif message.reasoning is defined and message.reasoning is not none %}
+            {%- if message.reasoning is string %}
+                {%- set _explicit_reasoning = message.reasoning %}
+            {%- else %}
+                {%- set _explicit_reasoning = message.reasoning | string %}
+            {%- endif %}
+        {%- endif %}
+        {%- if _explicit_reasoning %}
+            {%- set _lead_end = '' %}
+            {%- if content.startswith('<think>') and '</think>' in content %}
+                {%- set _lead_end = '</think>' %}
+            {%- elif content.startswith('<thinking>') and '</thinking>' in content %}
+                {%- set _lead_end = '</thinking>' %}
+            {%- elif content.startswith('</think>') %}
+                {%- set _lead_end = '</think>' %}
+            {%- elif content.startswith('</thinking>') %}
+                {%- set _lead_end = '</thinking>' %}
+            {%- endif %}
+            {%- if _lead_end %}
+                {%- set content = content.split(_lead_end)[-1].lstrip('\n') %}
+            {%- endif %}
+            {%- set reasoning_content = _explicit_reasoning %}
+        {%- else %}
+            {%- set _think_end = '' %}
+            {%- if content.startswith('</think>') %}
+                {%- set _think_end = '</think>' %}
+            {%- elif content.startswith('</thinking>') %}
+                {%- set _think_end = '</thinking>' %}
+            {%- elif '\n</think>' in content %}
+                {%- set _think_end = '\n</think>' %}
+            {%- elif '\n</thinking>' in content %}
+                {%- set _think_end = '\n</thinking>' %}
+            {%- elif '\n</ think>' in content %}
+                {%- set _think_end = '\n</ think>' %}
+            {%- elif '\n</think >' in content %}
+                {%- set _think_end = '\n</think >' %}
+            {%- elif content.startswith('<think>') and '</think>' in content %}
+                {%- set _think_end = '</think>' %}
+            {%- elif content.startswith('<thinking>') and '</thinking>' in content %}
+                {%- set _think_end = '</thinking>' %}
+            {%- endif %}
+            {%- if _think_end %}
+                {%- if 'thinking' in _think_end %}
+                    {%- set _think_start = '<thinking>' %}
+                {%- else %}
+                    {%- set _think_start = '<think>' %}
+                {%- endif %}
+                {%- set reasoning_content = content.split(_think_end)[0].rstrip('\n') %}
+                {%- if _think_start in reasoning_content %}
+                    {%- set reasoning_content = reasoning_content.split(_think_start)[-1].lstrip('\n') %}
+                {%- endif %}
+                {%- set content = content.split(_think_end)[-1].lstrip('\n') %}
+            {%- endif %}
+        {%- endif %}
+        {%- set reasoning_content = reasoning_content | trim %}
+        {%- if (_preserve_thinking or loop.index0 > ns.last_query_index) %}
+            {{- '<|im_start|>assistant\n<think>\n' + reasoning_content + '\n</think>\n\n' + content }}
+        {%- else %}
+            {{- '<|im_start|>assistant\n' + content }}
+        {%- endif %}
+        {%- if message.tool_calls is defined and message.tool_calls and message.tool_calls is iterable and message.tool_calls is not mapping %}
+            {%- for tool_call in message.tool_calls %}
+                {%- if tool_call.function is defined and tool_call.function is not none %}
+                    {%- set tc = tool_call.function %}
+                {%- else %}
+                    {%- set tc = tool_call %}
+                {%- endif %}
+                {%- set tc_name = tc.name if (tc.name is defined and tc.name is not none) else '' %}
+                {%- if _tool_format == 'json' %}
+                    {%- if loop.first %}
+                        {%- if content | trim %}
+                            {{- '\n\n' }}
+                        {%- endif %}
+                    {%- else %}
+                        {{- '\n' }}
+                    {%- endif %}
+                    {%- set _args = '{}' %}
+                    {%- if tc.arguments is defined and tc.arguments is not none %}
+                        {%- if tc.arguments is mapping %}
+                            {%- set _args = tc.arguments | tojson %}
+                        {%- elif tc.arguments is string %}
+                            {%- if tc.arguments %}
+                                {%- set _args = tc.arguments %}
+                            {%- endif %}
+                        {%- else %}
+                            {%- set _args = tc.arguments | tojson %}
+                        {%- endif %}
+                    {%- endif %}
+                    {{- '<tool_call>\n{"name": ' }}{{- tc_name | tojson }}{{- ', "arguments": ' }}{{- _args }}{{- '}\n</tool_call>' }}
+                {%- else %}
+                    {%- if loop.first %}
+                        {%- if content | trim %}
+                            {{- '\n\n<tool_call>\n<function=' + tc_name + '>\n' }}
+                        {%- else %}
+                            {{- '<tool_call>\n<function=' + tc_name + '>\n' }}
+                        {%- endif %}
+                    {%- else %}
+                        {{- '\n<tool_call>\n<function=' + tc_name + '>\n' }}
+                    {%- endif %}
+                    {%- if tc.arguments is defined and tc.arguments is not none %}
+                        {%- if tc.arguments is mapping %}
+                            {%- for args_name, args_value in tc.arguments.items() %}
+                                {{- '<parameter=' + args_name + '>\n' }}
+                                {%- if args_value is string %}
+                                    {%- set _av = args_value %}
+                                {%- else %}
+                                    {%- set _av = args_value | tojson %}
+                                {%- endif %}
+                                {%- if max_tool_arg_chars > 0 and _av | length > max_tool_arg_chars %}
+                                    {{- _av[:max_tool_arg_chars] + '\n[TRUNCATED - original length ' ~ (_av | length | string) ~ ' chars]' }}
+                                {%- else %}
+                                    {{- _av }}
+                                {%- endif %}
+                                {{- '\n</parameter>\n' }}
+                            {%- endfor %}
+                        {%- else %}
+                            {%- if tc.arguments is string %}
+                                {%- set _raw_args = tc.arguments %}
+                            {%- else %}
+                                {%- set _raw_args = tc.arguments | tojson %}
+                            {%- endif %}
+                            {%- if _raw_args %}
+                                {%- if max_tool_arg_chars > 0 and _raw_args | length > max_tool_arg_chars %}
+                                    {{- _raw_args[:max_tool_arg_chars] + '\n[TRUNCATED - original length ' ~ (_raw_args | length | string) ~ ' chars]' }}
+                                {%- else %}
+                                    {{- _raw_args }}
+                                {%- endif %}
+                            {%- endif %}
+                        {%- endif %}
+                    {%- endif %}
+                    {{- '</function>\n</tool_call>' }}
+                {%- endif %}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == 'tool' %}
+        {%- set _content_lower = content | lower %}
+        {%- set _content_head = _content_lower[:120] %}
+        {%- set _is_code_or_grep = ('throw new ' in _content_lower or 'throw error' in _content_lower or 'console.error' in _content_lower or 'logger.error' in _content_lower or 'logging.error' in _content_lower or 'import ' in _content_head or 'def ' in _content_head or 'function ' in _content_head) %}
+        {%- set _exit_code_zero = ('exit code: 0' in _content_head or 'process exited with code 0' in _content_head) %}
+        {%- set _error_field_ok = ('"error": null' in _content_head or '"error":null' in _content_head or '"error": false' in _content_head or '"error":false' in _content_head or '"error": ""' in _content_head or '"error":""' in _content_head) %}
+        {%- set _strong_error = (('"error":' in _content_head and not _error_field_ok) or '"status": "error"' in _content_head or '"status":"error"' in _content_head or 'traceback (most recent call last):' in _content_head or 'command not found' in _content_head or 'invalid syntax' in _content_head or 'fatal:' in _content_head or (('exit code: ' in _content_head or 'process exited with code' in _content_head) and not _exit_code_zero) or _content_head.startswith('exception:') or _content_head.startswith('failed to ')) %}
+        {%- set _weak_error = ('error:' in _content_head or 'err!' in _content_head) %}
+        {%- set _weak_suppressed = ('$ ' in _content_head or 'took ' in _content_head or content | length >= 600) %}
+        {%- if not _is_code_or_grep and (_strong_error or (_weak_error and not _weak_suppressed)) %}
+            {%- set ns2.consecutive_failures = ns2.consecutive_failures + 1 %}
+        {%- else %}
+            {%- set ns2.consecutive_failures = 0 %}
+        {%- endif %}
+        {%- if ns2.prev_role != 'tool' %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {%- set _is_json_payload = (_tool_format == 'json' and content | trim | length > 0 and (content | trim)[:1] in ('{', '[')) %}
+        {%- if not _is_json_payload and max_tool_response_chars > 0 and content | length > max_tool_response_chars %}
+            {%- set content = content[:max_tool_response_chars] + '\n[TRUNCATED - original length ' ~ (content | length | string) ~ ' chars]' %}
+        {%- endif %}
+        {{- '\n<tool_response>\n' + content }}
+        {%- if ns2.consecutive_failures >= 2 %}
+            {{- '\n\n⚠️ SYSTEM WARNING: ' ~ ns2.consecutive_failures ~ ' consecutive tool errors detected. Your previous approach is incorrect. You MUST use a fundamentally different approach or corrected arguments.' }}
+        {%- elif ns2.consecutive_failures == 1 %}
+            {{- '\n\n⚠️ SYSTEM WARNING: The previous tool call returned an error. Diagnose the failure and retry with completely corrected arguments.' }}
+        {%- endif %}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last %}
+            {{- '<|im_end|>\n' }}
+        {%- else %}
+            {%- set _next_role = _msgs[loop.index0 + 1].role %}
+            {%- if _next_role != 'tool' %}
+                {{- '<|im_end|>\n' }}
+            {%- endif %}
+        {%- endif %}
+    {%- else %}
+        {{- '<|im_start|>user\n[' + message.role + ']: ' + content + '<|im_end|>\n' }}
+    {%- endif %}
+    {%- set ns2.prev_role = message.role %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+    {%- if not ns_state.thinking %}
+        {{- '<think>\n\n</think>\n\n' }}
+    {%- else %}
+        {{- '<think>\n' }}
+    {%- endif %}
+{%- endif %}

scripts/recipes/qwen4exp-swift15-w4dn8-i8-hc8m.recipe

@@ -0,0 +1,105 @@
+# qwen4exp-swift15-w4dn8-i8-hc8m.recipe -- the Swift 1.5 finetune of Qwen3.8-Flash-Next
+# (ukisai/Swift1.5-Qwen3.8-Flash-Next, BF16 source, 335.3 GiB, key set identical to the official
+# base) as a radiance container for frank-primary: EVERY routed expert -- gate_up AND down --
+# through GPTQ into the w4nl64a8h table. The plugin's one-table-one-encoding law
+# ("one table of experts takes one encoding, and only a whole expert kept plain bf16 is served
+# beside it", qwen4exp_fp8 rad_arch_declare) forbids the original split tier (down block-fp8 +
+# gate_up w4): the served kernel is w4nl64a8h for the whole expert set, bf16 experts beside it.
+# The rest is qwen4exp-w5dn64-i8-hc8m.recipe verbatim: int8 trunk linears, block-fp8 hc mixers
+# and MTP fc, the fixed-scale n-gram table, the 2-bit draft head.
+#
+# Why down is the calibrated half: the calibration tap rides the down projection, so its GPTQ
+# rounding propagates against real down-input Hessians; gate_up shares the same w4 table
+# uncalibrated. The published w5dn64 comparison (mean KL 0.0789 -> 0.0717, top-1 92.85% ->
+# 93.34%, decode +4%, prefill -19%) is the 5-bit upper bound; this tier trades part of that
+# quality back for prefill.
+#
+#   CALIB=DIR rad-convert <Swift1.5-Qwen3.8-Flash-Next checkpoint> \
+#       --recipe scripts/recipes/qwen4exp-swift15-w4dn8-i8-hc8m.recipe -o qwen4exp-swift15-w4dn8-i8-hc8m.rad
+#
+# DIR is the calibration directory the bf16 container wrote (RADIANCE_CALIB_DIR, 10M tokens of
+# traffic-shaped corpus). Everything not named here is served as the checkpoint holds it (bf16:
+# embeddings, the vision tower, the QSA indexer); the first rule that matches a weight decides it.
+# The bf16 container is NOT passed with --reuse: the peak-disk arithmetic does not fit (checkpoint +
+# container + container + output > purge-freed space), and --reuse only saves a re-read.
+
+# The protected experts, cast to bf16 ahead of both expert rules. THE NAMES BELOW ARE THE BASE
+# CHECKPOINT'S (from its 1M-token recording, qwen4exp-w5dn64-i8-hc8m.recipe) -- a warm start only.
+# The Swift 1.5 finetune's routing distribution is not assumed equal: the derivation step rewrites
+# this section from Swift's OWN calibration recording before this recipe is ever run, by the base's
+# law -- an expert whose share of its layer's down-input energy is at least 3% and at least three
+# times its share of the routing, an even count on each rank's half of the layer (the four-bit GEMM
+# takes the rest as two equal tables by parity) by adding that half's next most energetic expert.
+# PROTECTED-BEGIN (rewritten from Swift's OWN grams by derive-protected-experts.py)
+blk.1.ffn_*_exps.271.weight         cast dtype=bf16
+blk.1.ffn_*_exps.355.weight         cast dtype=bf16
+blk.2.ffn_*_exps.279.weight         cast dtype=bf16
+blk.2.ffn_*_exps.413.weight         cast dtype=bf16
+blk.34.ffn_*_exps.146.weight         cast dtype=bf16
+blk.34.ffn_*_exps.153.weight         cast dtype=bf16
+blk.34.ffn_*_exps.407.weight         cast dtype=bf16
+blk.34.ffn_*_exps.453.weight         cast dtype=bf16
+blk.37.ffn_*_exps.288.weight         cast dtype=bf16
+blk.37.ffn_*_exps.377.weight         cast dtype=bf16
+blk.38.ffn_*_exps.406.weight         cast dtype=bf16
+blk.38.ffn_*_exps.420.weight         cast dtype=bf16
+blk.42.ffn_*_exps.37.weight         cast dtype=bf16
+blk.42.ffn_*_exps.90.weight         cast dtype=bf16
+blk.42.ffn_*_exps.302.weight         cast dtype=bf16
+blk.42.ffn_*_exps.388.weight         cast dtype=bf16
+blk.44.ffn_*_exps.315.weight         cast dtype=bf16
+blk.44.ffn_*_exps.350.weight         cast dtype=bf16
+blk.46.ffn_*_exps.290.weight         cast dtype=bf16
+blk.46.ffn_*_exps.419.weight         cast dtype=bf16
+blk.47.ffn_*_exps.119.weight         cast dtype=bf16
+blk.47.ffn_*_exps.143.weight         cast dtype=bf16
+blk.47.ffn_*_exps.399.weight         cast dtype=bf16
+blk.47.ffn_*_exps.445.weight         cast dtype=bf16
+# PROTECTED-END
+# (derive-protected-experts.py rewrote this section from Swift's own grams)
+
+# Every other routed expert, trunk and MTP layer alike: u4 codes into the w4nl table, rotated by
+# the 128-wide Hadamard, an E4M3 scale a group of 64 under the fixed 2^-13 searched for least
+# error as stored, chosen by GPTQ against $CALIB and swept three times by coordinate descent.
+# The calibration Grams are down-input Hessians, so down is the calibrated half of the table;
+# gate_up has no recording of its own and quantises uncalibrated (said on stderr, as the base
+# recipe says). The MTP layer has no recording either. Expert mass: gate_up 150.00 GiB -> ~37.6,
+# down 75.00 GiB -> ~18.8: ~56 GiB of experts, ~28 GiB a rank at TP=2 -- well inside the pool.
+blk.*.ffn_*_exps.*.weight            gptq table=w4nl group=64 scale=fp8_e4m3 scale2=f32 block2=*x* scale2_value=0.0001220703125 transform=fwht128 rule=search cd=3 calib=$CALIB
+
+# Every connection's two mixing matrices -- the 96 trunk connections, the final mixer and the MTP
+# head's three -- as E4M3 rows with an f32 scale a group of each row. A 128x128 block cannot cover
+# them: lowrank is 320, 2.5 blocks. Down is [320, 10240] and takes a scale every 128 columns; up is
+# [10240, 320] and takes four scales a row, 80 columns each.
+*_hc_down.weight                     rtn  codes=fp8_e4m3 group=128 scale=f32
+*_hc_up.weight                       rtn  codes=fp8_e4m3 group=80 scale=f32
+
+# The MTP head's fc_hidden and fc_embedding, the same rows. They only ever change the drafts.
+mtp.fc_hidden.weight                 rtn  codes=fp8_e4m3 group=128 scale=f32
+mtp.fc_embedding.weight              rtn  codes=fp8_e4m3 group=128 scale=f32
+
+# The trunk's block linears as int8, a scale per 128 columns of a row searched for least error.
+# The QSA indexer's projection is not one of them: its top-k is a hard choice between blocks, and
+# keeping it as the checkpoint holds it costs 20 MB.
+blk.*.attn_qg.weight                 rtn  codes=i8 group=128 scale=bf16 clamp=sym rule=search
+blk.*.attn_k.weight                  rtn  codes=i8 group=128 scale=bf16 clamp=sym rule=search
+blk.*.attn_v.weight                  rtn  codes=i8 group=128 scale=bf16 clamp=sym rule=search
+blk.*.attn_output.weight             rtn  codes=i8 group=128 scale=bf16 clamp=sym rule=search
+blk.*.ssm_inz.weight                 rtn  codes=i8 group=128 scale=bf16 clamp=sym rule=search
+blk.*.ssm_out.weight                 rtn  codes=i8 group=128 scale=bf16 clamp=sym rule=search
+blk.*.ffn_gate_up_shexp.weight       rtn  codes=i8 group=128 scale=bf16 clamp=sym rule=search
+blk.*.ffn_down_shexp.weight          rtn  codes=i8 group=128 scale=bf16 clamp=sym rule=search
+
+# The lm_head, int8 with a bf16 scale a row per 128 columns (logits_gemm_i8): at E4M3 it costs
+# ~0.6 points of top-1 on its own.
+output.weight                        rtn  codes=i8 group=128 scale=bf16 clamp=sym rule=search
+
+# The n-gram table at one fixed E4M3 scale; qwen4exp-w4.recipe says where the value comes from.
+# The value sits on a three-octave plateau (2^-13..2^-10, rel_l2 0.02662) with 1.7x headroom over
+# the base table's absmax; the prep phase verifies Swift's PLE absmax lands inside the plateau
+# before this rule is trusted.
+blk.*.ple_ngram.weight               rtn  codes=fp8_e4m3 block=*x* scale=bf16 rule=fixed scale_value=1.99317932128906e-4
+
+# The MTP head's 2-bit draft head. INERT for this checkpoint: the HF weight map has no
+# mtp.draft_head.weight (same as the base), kept for shape parity with the family.
+mtp.draft_head.weight                rtn  codes=u2 zero=u8 group=128 scale=f16

scripts/run-swift15-bf16.sh

@@ -0,0 +1,116 @@
+#!/usr/bin/env bash
+# run-swift15-bf16.sh — RUNS ON DANKSERVER. Boot the no-recipe bf16 Swift1.5
+# container for the two offline campaign phases. The engine owns the GPUs, so
+# the production unit must be stopped first (this script does it and restores
+# on exit, autoresearch pattern: stop, never mask — relinked unit).
+#
+#   kld    --kld-record the reference logits from the bf16 model
+#          (runs the corpus and EXITS; engine writes DIR and leaves)
+#   calib  serve with the calibration tap; a separate driver
+#          (scripts/calib-push.py) pushes the corpus; the engine writes the
+#          Hessians at RADIANCE_CALIB_TOKENS and the tap switches itself off.
+#          Stop this script after calib-push prints TAP_DONE.
+#
+# Shape = the vendor's bf16 calibration lines (GUIDE.md §4.3:528 + MOE-W4.md
+# "calibrate the checkpoint as it ships"): expert_tiered + weights-disk-tier,
+# no speculation, deterministic. Large prefill steps because a disk-tier step
+# costs the same at 1 token as at 32K. The container declared --max-ctx 4096;
+# --max-model-len 16384 exceeds it on purpose (the KLD prompts run to 12,007
+# tokens, calib to 8,242) — the boot ledger arbitrates the QSA buffers; if
+# declare refuses, re-convert with a wider --max-ctx.
+set -uo pipefail
+
+MODE="${1:?usage: run-swift15-bf16.sh kld|calib}"
+PIN_1013=sha256:4c3f484564bb2872784e46b0a59cbd71becc9c1e746e301a3596ffae253c322e
+PIN_120=sha256:665775ed1d90de6dbb25558e07fc60d20328f1346f96f3c6e00845eb763f01fa
+RAD=/home/dank/models/rad/qwen4exp-swift15-bf16.rad
+C=swift15ref
+LOG=/home/dank/logs/swift15_bf16.log
+KLD_REF_DIR=/home/dank/corpora/kld-ref
+CALIB_DIR=/home/dank/corpora/calib
+KLD_CORPUS=/home/dank/corpora/out/kld.jsonl
+
+restore() { sudo systemctl start frank-primary >/dev/null 2>&1 || true; }
+trap restore EXIT
+sudo systemctl stop frank-primary >/dev/null 2>&1 || true
+podman rm -f "$C" >/dev/null 2>&1 || true
+
+ARGS=(--model /models/rad/qwen4exp-swift15-bf16.rad
+      --tp 2 --placement expert_tiered --weights-disk-tier
+      --host-pool-mib 12288 --deterministic --gpu-headroom-mib 768
+      --num-speculative-tokens 0
+      --kv-cache-dtype fp8 --max-model-len 16384
+      --ngram-placement ram
+      --override-chat-template /models/tpl.jinja)
+
+case "$MODE" in
+  kld)
+    # 1.0.13: the engine that will serve the candidate — the reference is
+    # recorded on the same engine that scores it. The weights slab DERIVES as
+    # card - headroom - kv, and the disk tier refuses under 9.57 GiB (4.21 of
+    # layer buffers + 5.36 fixed), so kv is explicit SMALL: recording is
+    # prefill-only, self-batched at MNBT 32768 = two 16k prompts at a time;
+    # 4 slots x 16k fp8 (29 KiB/pos, 1.7 GiB) + headroom fits 3072.
+    IMAGE="stilldeadcode/radiance@$PIN_1013"
+    mkdir -p "$KLD_REF_DIR"
+    ARGS+=(--kld-record /data/kld-ref --kld-corpus /data/kld.jsonl
+           --vram-kv-mib 3072
+           --max-num-seqs 4 --max-num-batched-tokens 32768)
+    ;;
+  calib)
+    # 1.2.0: the calibration tap (RADIANCE_CALIB_DIR + RADIANCE_CALIB_TOKENS)
+    # lives in the 1.2.0 qwen4exp plugin; 1.0.13 has only the DIR half with an
+    # unparameterisable threshold. Grams are bf16 activations — engine-invariant.
+    # Same KV ceiling as kld: the disk-tier weights floor (9.57 GiB) plus
+    # ~18 GiB of fixed per-rank claims cap the pool near 3.5 GiB on a 32 GiB
+    # card — pool minus the 2.17 GiB fixed holds ~237k fp8 tokens a rank, so
+    # the vendor's big-step calibration shape is affordable: MOE-W4.md:244
+    # measures 640 tok/s at a 32k step against 930 at 55k, and the doc's own
+    # run drives MNBT 65536. 8 slots x 16k = 131k resident tokens fits the
+    # pool; the step batches 65k. KLD's observed rate confirmed step size,
+    # not slot count, is the throughput lever.
+    IMAGE="stilldeadcode/radiance@$PIN_120"
+    mkdir -p "$CALIB_DIR"
+    ARGS+=(--port 7770 --vram-kv-mib 3072
+           --max-num-seqs 8 --max-num-batched-tokens 65536
+           --prefix-cache-dir /data/calib-kvcache)
+    ;;
+  probe)
+    IMAGE="stilldeadcode/radiance@$PIN_1013"
+    ARGS+=(--port 7770 --max-num-seqs 8 --max-num-batched-tokens 4096)
+    ;;
+  *) echo "unknown mode: $MODE" >&2; exit 2 ;;
+esac
+
+# The prod template copy, readable from the container: mount the models tree
+# ro plus the state dir rw (KLD record + prefix tier land there).
+cp -f /home/dank/serverAppConfigs/scripts/qwen38-froggeric-v22.5.jinja \
+      /home/dank/models/tpl.jinja 2>/dev/null || true
+
+KLD_MOUNT=()
+if [ "$MODE" = kld ]; then
+    # Nested under the /data mount, so these must come after it (line order
+    # in podman run below): the corpus read-only, the record dir writable.
+    KLD_MOUNT=(-v "$KLD_CORPUS:/data/kld.jsonl:ro"
+               -v "$KLD_REF_DIR:/data/kld-ref")
+fi
+EXTRA_ENV=()
+if [ "$MODE" = calib ]; then
+    EXTRA_ENV=(-v "$CALIB_DIR:/data/calib"
+               -e RADIANCE_CALIB_DIR=/data/calib
+               -e RADIANCE_CALIB_TOKENS=10000000)
+fi
+
+podman run --rm --name "$C" \
+    --network host --init \
+    --security-opt seccomp=unconfined \
+    --ulimit memlock=-1:-1 \
+    --device /dev/kfd --device /dev/dri \
+    -v /home/dank/models:/models:ro \
+    -v /home/dank/radiance-state:/data \
+    "${KLD_MOUNT[@]}" \
+    "${EXTRA_ENV[@]}" \
+    "$IMAGE" "${ARGS[@]}" 2>&1 | tee "$LOG"
+rc=${PIPESTATUS[0]}
+echo "swift15-bf16 $MODE exited rc=$rc; log: $LOG"
+exit "$rc"

scripts/serve-frank-large.sh

@@ -1,30 +1,32 @@
 #!/usr/bin/env bash
-# serve-frank-large.sh — frank-large endpoint: halogen (closed-source
-# C++/HIP engine, image ghcr.io/peonist-ai/halogen, digest-pinned in
-# frank-large.env) serving peonist-ai/halogen-qwen3.8-27b on Dankclaw's
-# Strix Halo iGPU, host :7770. 2026-10-05 cutover from the ROCmFPX
-# llama.cpp shape — revert the swap commit's trio to roll back (payload
-# and fork build stay on disk).
+# serve-frank-large.sh — frank-large endpoint: halogen-flash-server 0.16.2
+# (closed-source engine, image ghcr.io/peonist-ai/halogen-flash-server,
+# digest-pinned in frank-large.env) serving the Quat3rnion abliterated
+# Swift1.5 finetune of peonist's Qwen3.8-Flash-Next v2 checkpoint on
+# Dankclaw's Strix Halo iGPU, host :7770. 2026-10-06 cutover from the
+# halogen 27B-dense shape — revert the swap commit's trio to roll back
+# (the 27B payload stays on disk).
 #
 # The engine is driven by `podman run` DIRECTLY (the fleet's frank-primary
-# pattern): the image entrypoint `halogen-docker` in default "all" mode
-# starts engine + OpenAI frontend in one container. The frontend listens on
-# 8731 inside (HALOGEN_API_PORT default) and is published as host 7770; the
-# engine's own token port (8730, no auth) stays unpublished on the
-# container's loopback. The image ships NO default checkpoint:
-# HALOGEN_CHECKPOINT selects the file inside the /models mount.
+# pattern): the image starts engine + OpenAI frontend in one container.
+# The frontend listens on 8731 inside (HALOGEN_API_PORT default) and is
+# published as host 7770; the engine's own token port (8730, no auth)
+# stays unpublished on the container's loopback. HALOGEN_CHECKPOINT
+# selects the file inside the /models mount; the n-gram lookup table
+# (HALOGEN_NGRAM_TABLE, the card sets it explicitly though the documented
+# fallback is "beside the checkpoint") and the vision tower
+# (HALOGEN_VISION_TOWER) ride in the same directory.
 #
 # Gates mirror the deployer EXACTLY (the 2026-10-01 crash-loop lesson —
 # serve gate and deploy gate disagreeing = green deploy, red unit):
-# tokenizer directory present with every named file a FLAT REGULAR FILE —
-# the model card ships `tokenizer/` as a flat directory and the engine
-# defaults HALOGEN_TOKENIZER to /tokenizer, so a HF cache snapshot's
-# symlinks into blobs/ dangle inside the mount; GPU device nodes present.
+# checkpoint/n-gram/vision byte-exact, tokenizer directory present with
+# every named file a FLAT REGULAR FILE (a HF cache snapshot's symlinks
+# into blobs/ dangle inside the mount), GPU device nodes present.
 #
-# Why containers instead of the bare binary (the old frank-large shape):
-# the payload is .hgn and only halogen reads it; halogen ships as a
-# container; the root unit keeps LimitMEMLOCK=infinity for parity and to
-# outlive the login session, exactly like frank-primary's.
+# Why containers instead of a bare binary: the payload is .hgn and only
+# the flash-server line reads it; it ships as a container; the root unit
+# keeps LimitMEMLOCK=infinity for parity and to outlive the login session,
+# exactly like frank-primary's.
 set -euo pipefail
 
 SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
@@ -44,11 +46,16 @@ set +a
 : "${FRANK_LARGE_MODEL_DIR:?FRANK_LARGE_MODEL_DIR must name the payload dir}"
 : "${FRANK_LARGE_CHECKPOINT:?FRANK_LARGE_CHECKPOINT must name the .hgn inside it}"
 : "${FRANK_LARGE_CHECKPOINT_BYTES:?FRANK_LARGE_CHECKPOINT_BYTES must pin the size}"
+: "${FRANK_LARGE_NGRAM_TABLE:?FRANK_LARGE_NGRAM_TABLE must name the lookup table}"
+: "${FRANK_LARGE_NGRAM_BYTES:?FRANK_LARGE_NGRAM_BYTES must pin the table size}"
+: "${FRANK_LARGE_VISION_TOWER:?FRANK_LARGE_VISION_TOWER must name the tower}"
+: "${FRANK_LARGE_VISION_BYTES:?FRANK_LARGE_VISION_BYTES must pin the tower size}"
 : "${FRANK_LARGE_TOKENIZER_FILES:?FRANK_LARGE_TOKENIZER_FILES must list the tokenizer dir contents}"
-: "${HALOGEN_MODEL_ID:?HALOGEN_MODEL_ID must be the served alias (frank-large)}"
-: "${HALOGEN_SLOT_CTX:?HALOGEN_SLOT_CTX must be set}"
-: "${HALOGEN_KV_SLOTS:?HALOGEN_KV_SLOTS must be set (1 keeps the drafter on)}"
-: "${HALOGEN_DRAFTER:?HALOGEN_DRAFTER must be set (2 = DFlash2)}"
+: "${HALOGEN_MODEL_ID:?HALOGEN_MODEL_ID must be the served alias (frank-dragoon)}"
+: "${HALOGEN_CTX:?HALOGEN_CTX must be set (per-slot window)}"
+: "${HALOGEN_KV_POOL_POSITIONS:?HALOGEN_KV_POOL_POSITIONS must be set}"
+: "${HALOGEN_KV_SLOTS:?HALOGEN_KV_SLOTS must be set}"
+: "${HALOGEN_SPEC_ADAPT:?HALOGEN_SPEC_ADAPT must be set (0 = head always on)}"
 
 # Capture podman's output BEFORE grepping: under `set -o pipefail` the
 # `podman images | grep -q` pattern races (grep exits on first match,
@@ -61,36 +68,41 @@ if ! grep -qF "${FRANK_LARGE_IMAGE#*@}" <<<"$DIGESTS"; then
     exit 1
 fi
 
-# The checkpoint gate is size-exact: a truncated curl exits 0 on some
-# failures and a partial .hgn loads far enough to look alive, so the byte
-# count from the model card is the contract (the fleet fetch law).
-CKPT="$FRANK_LARGE_MODEL_DIR/$FRANK_LARGE_CHECKPOINT"
-if [ ! -f "$CKPT" ]; then
-    echo "frank-large: checkpoint $CKPT missing;" >&2
-    echo "  fetch it: bash scripts/download-frank-large-halogen.sh" >&2
-    exit 1
-fi
-CKPT_SIZE="$(stat -c %s "$CKPT")"
-if [ "$CKPT_SIZE" != "$FRANK_LARGE_CHECKPOINT_BYTES" ]; then
-    echo "frank-large: checkpoint is $CKPT_SIZE bytes, expected $FRANK_LARGE_CHECKPOINT_BYTES (partial download?)" >&2
-    exit 1
-fi
+# Size-exact gates on all three payload files: a truncated curl exits 0 on
+# some failures and a partial .hgn loads far enough to look alive, so the
+# byte counts from the Hub tree API are the contract (fleet fetch law).
+ck() {
+    # ck <env-name> <path> <expected-bytes>
+    if [ ! -f "$2" ]; then
+        echo "frank-large: $1 $2 missing;" >&2
+        echo "  fetch it: bash scripts/download-frank-large-flashnext.sh" >&2
+        exit 1
+    fi
+    local sz
+    sz="$(stat -c %s "$2")"
+    if [ "$sz" != "$3" ]; then
+        echo "frank-large: $1 is $sz bytes, expected $3 (partial download?)" >&2
+        exit 1
+    fi
+}
+ck checkpoint "$FRANK_LARGE_MODEL_DIR/$FRANK_LARGE_CHECKPOINT" "$FRANK_LARGE_CHECKPOINT_BYTES"
+ck n-gram-table "$FRANK_LARGE_MODEL_DIR/$FRANK_LARGE_NGRAM_TABLE" "$FRANK_LARGE_NGRAM_BYTES"
+ck vision-tower "$FRANK_LARGE_MODEL_DIR/$FRANK_LARGE_VISION_TOWER" "$FRANK_LARGE_VISION_BYTES"
 
-# The payload ships the tokenizer as a FLAT DIRECTORY (model card:
-# "tokenizer/ flat tokenizer directory"). A HuggingFace cache snapshot
-# symlinks each file into a sibling blobs/, and those symlinks dangle
-# inside the container mount — so the directory must be real and every
-# named file must be a regular file, not a link.
+# The payload ships the tokenizer as a FLAT DIRECTORY. A HuggingFace cache
+# snapshot symlinks each file into a sibling blobs/, and those symlinks
+# dangle inside the container mount — so the directory must be real and
+# every named file must be a regular file, not a link.
 TOK_DIR="$FRANK_LARGE_MODEL_DIR/tokenizer"
 if [ -L "$TOK_DIR" ] || [ ! -d "$TOK_DIR" ]; then
     echo "frank-large: tokenizer dir $TOK_DIR missing or a symlink (HF cache snapshot?);" >&2
-    echo "  fetch it: bash scripts/download-frank-large-halogen.sh" >&2
+    echo "  fetch it: bash scripts/download-frank-large-flashnext.sh" >&2
     exit 1
 fi
 for TF in $FRANK_LARGE_TOKENIZER_FILES; do
     if [ ! -f "$TOK_DIR/$TF" ] || [ -L "$TOK_DIR/$TF" ]; then
         echo "frank-large: tokenizer file $TOK_DIR/$TF missing or a symlink;" >&2
-        echo "  fetch it: bash scripts/download-frank-large-halogen.sh" >&2
+        echo "  fetch it: bash scripts/download-frank-large-flashnext.sh" >&2
         exit 1
     fi
 done
@@ -103,23 +115,55 @@ if [ ! -e "$DEV_KFD" ] || [ ! -d "$DEV_DRI" ]; then
     echo "frank-large: $DEV_KFD or $DEV_DRI missing — no gfx1151 to serve on" >&2
     exit 1
 fi
+# Host-RAM preflight — the engine's pin gate, checked BEFORE podman run.
+# The engine refuses to pin when MemAvailable is below the checkpoint plus
+# a 16 GiB floor: pinned pages cannot be swapped, and under the floor the
+# n-gram lookup pages in from disk on every long prompt. A RAM-short host
+# (a co-tenant spike, a sibling engine dying) crash-loops the unit — each
+# boot dies in ~5 s at the pin gate — until the RAM clears. The 2026-10-06
+# cutover hit this: 8 boots, MemAvailable 74.77 GiB vs the 78.11 GiB needed,
+# self-resolved in ~2 min when the dying 27B engine freed RAM. Check here and
+# WAIT for the RAM to clear, so a transient dip boots once instead of
+# crash-looping; if it never clears, refuse loudly.
+MEMINFO="${FRANK_LARGE_MEMINFO:-/proc/meminfo}"
+PIN_KB=$(( FRANK_LARGE_CHECKPOINT_BYTES / 1024 ))
+NEED_KB=$(( PIN_KB + 16 * 1048576 ))
+ram_avail_kb() { awk '/^MemAvailable:/{print $2; exit}' "$MEMINFO"; }
+MEMAVAIL_KB="$(ram_avail_kb)" || MEMAVAIL_KB=""
+if [ -n "$MEMAVAIL_KB" ] && [ "$MEMAVAIL_KB" -lt "$NEED_KB" ]; then
+    WAIT_S="${FRANK_LARGE_RAM_WAIT:-600}"
+    GIB() { awk -v kb="$1" 'BEGIN{printf "%.2f", kb/1048576}'; }
+    echo "frank-large: host RAM below the pin gate — MemAvailable $(GIB "$MEMAVAIL_KB") GiB < needed $(GIB "$NEED_KB") GiB ($(GIB "$PIN_KB") GiB checkpoint + 16.00 GiB floor); waiting up to ${WAIT_S}s for host RAM to clear" >&2
+    deadline=$(( $(date +%s) + WAIT_S ))
+    while [ "$(date +%s)" -lt "$deadline" ]; do
+        sleep 5
+        MEMAVAIL_KB="$(ram_avail_kb)" || MEMAVAIL_KB=""
+        if [ -n "$MEMAVAIL_KB" ] && [ "$MEMAVAIL_KB" -ge "$NEED_KB" ]; then
+            break
+        fi
+    done
+    if [ -z "$MEMAVAIL_KB" ] || [ "$MEMAVAIL_KB" -lt "$NEED_KB" ]; then
+        echo "frank-large: MemAvailable $(GIB "${MEMAVAIL_KB:-0}") GiB after ${WAIT_S}s; refusing to start (the engine would refuse the pin)" >&2
+        exit 1
+    fi
+    echo "frank-large: MemAvailable cleared to $(GIB "$MEMAVAIL_KB") GiB; starting" >&2
+fi
 for CTR in ${FRANK_LARGE_LEGACY_CONTAINERS:-} "$FRANK_LARGE_CONTAINER"; do
     podman rm -f "$CTR" >/dev/null 2>&1 || true
 done
 
 LOG="${FRANK_LARGE_LOG:-/home/dank/logs/frank_large.log}"
 mkdir -p "$(dirname "$LOG")"
-# for the udev-granted supplementary group, seccomp unconfined for the ROCm
-# ioctl surface, and unbounded memlock (the unit's LimitMEMLOCK=infinity is
-# inherited, so runc never has to RAISE it). NO --ulimit nofile: Dankclaw's
-# hard limit is 524288, rootless runc cannot raise a hard limit and dies
-# with "error setting rlimit type 7: operation not permitted" (measured
-# 2026-10-05 cutover), and 524288 fds is already ~100x what the engine
-# wants; the model card and both sibling serve scripts pass no nofile
-# either. No comment lines inside the backslash
-# chain — a mid-chain # terminates the continuation (the 2026-10-05
-# exit-127 crash loop on the sibling serve script).
-podman run -d --name "$FRANK_LARGE_CONTAINER" \
+# --ipc=host for the udev-granted supplementary group, seccomp unconfined
+# for the ROCm ioctl surface, and unbounded memlock (the unit's
+# LimitMEMLOCK=infinity is inherited, so runc never has to RAISE it). NO
+# --ulimit nofile: Dankclaw's hard limit is 524288, rootless runc cannot
+# raise a hard limit and dies with "error setting rlimit type 7:
+# operation not permitted" (measured 2026-10-05 cutover); the model card
+# and both sibling serve scripts pass no nofile either. No comment lines
+# inside the backslash chain — a mid-chain # terminates the continuation
+# (the 2026-10-05 exit-127 crash loop on the sibling serve script).
+podman run --name "$FRANK_LARGE_CONTAINER" \
   --publish "$FRANK_LARGE_PUBLISH" \
   --ipc=host \
   --ulimit memlock=-1:-1 \
@@ -128,19 +172,29 @@ podman run -d --name "$FRANK_LARGE_CONTAINER" \
   --security-opt seccomp=unconfined \
   -e HALOGEN_MODEL_ID="$HALOGEN_MODEL_ID" \
   -e HALOGEN_CHECKPOINT="/models/$FRANK_LARGE_CHECKPOINT" \
-  -e HALOGEN_SLOT_CTX="$HALOGEN_SLOT_CTX" \
-  -e HALOGEN_KV_SLOTS="${HALOGEN_KV_SLOTS:-1}" \
-  -e HALOGEN_DRAFTER="$HALOGEN_DRAFTER" \
+  -e HALOGEN_NGRAM_TABLE="/models/$FRANK_LARGE_NGRAM_TABLE" \
+  -e HALOGEN_VISION_TOWER="/models/$FRANK_LARGE_VISION_TOWER" \
+  -e HALOGEN_TOKENIZER=/tokenizer \
+  -e HALOGEN_CTX="$HALOGEN_CTX" \
+  -e HALOGEN_KV_POOL_POSITIONS="$HALOGEN_KV_POOL_POSITIONS" \
+  -e HALOGEN_KV_SLOTS="$HALOGEN_KV_SLOTS" \
+  -e HALOGEN_HOST_RESERVE_GIB="${HALOGEN_HOST_RESERVE_GIB:-20}" \
+  -e HALOGEN_SPEC_ADAPT="$HALOGEN_SPEC_ADAPT" \
+  -e HALOGEN_MAX_TOK="${HALOGEN_MAX_TOK:-8192}" \
   -e HALOGEN_MAX_TOKENS_CAP="${HALOGEN_MAX_TOKENS_CAP:-65536}" \
   -e HALOGEN_QUEUE_TIMEOUT="${HALOGEN_QUEUE_TIMEOUT:-7200}" \
-  ${HALOGEN_CACHE_MB:+-e HALOGEN_CACHE_MB="$HALOGEN_CACHE_MB"} \
+  -e HALOGEN_TEMPERATURE="${HALOGEN_TEMPERATURE:-1.0}" \
+  -e HALOGEN_TOP_P="${HALOGEN_TOP_P:-0.95}" \
+  -e HALOGEN_TOP_K="${HALOGEN_TOP_K:-20}" \
+  ${HALOGEN_WEIGHTS_LOCK:+-e HALOGEN_WEIGHTS_LOCK="$HALOGEN_WEIGHTS_LOCK"} \
   -v "$FRANK_LARGE_MODEL_DIR:/models:ro" \
   -v "$FRANK_LARGE_MODEL_DIR/tokenizer:/tokenizer:ro" \
   "$FRANK_LARGE_IMAGE" \
   > "$LOG" 2>&1
 
-# This script's lifetime IS the container's: podman wait returns the
-# container's exit code, so a crash is a nonzero ExecStart and the unit's
-# Restart=always is real. systemd stop goes through the unit's ExecStop
-# (podman wait alone cannot stop the child).
-exec podman wait "$FRANK_LARGE_CONTAINER"
+# FOREGROUND `podman run`: the engine's stdout/stderr are this script's
+# output, so the boot ledger (kv pool, prompt cache, MTP head, any clamp)
+# lands in $LOG as documented. podman run returns the container's exit
+# code, so a crash is a nonzero ExecStart and the unit's Restart=always
+# stays real; systemd stop goes through the unit's ExecStop and the run
+# client forwards SIGTERM to the engine, which drains on it.

scripts/serve-frank-primary.sh

@@ -1,38 +1,33 @@
 #!/usr/bin/env bash
-# serve-frank-primary.sh — frank-primary endpoint (FreeToken PR #587 ROCm
-# build, orcarouter Qwen3.8-Flash-Next-Uncensored-FP8, ONE R9700 gfx1201,
-# dankserver :7770; 2026-10-05 cutover from the tcclaviger vLLM fork — git
-# tag frank-primary-vllm-shapeG-20261003 is the pre-cutover state).
+# serve-frank-primary.sh — frank-primary endpoint: radiance (StillDeadcode,
+# Apache-2.0) serving the OFFICIAL Qwen/Qwen3.8-Flash-Next-FP8 checkpoint at
+# NATIVE block-FP8 precision from a rad-convert container
+# (/home/dank/models/rad/qwen4exp-fp8-native.rad), on BOTH R9700s (TP=2,
+# wht6 wire), dankserver :7770. 2026-10-05 cutover from FreeToken (PR #587,
+# one card; git tag frank-primary-freetoken-pr587-20261005 is the
+# pre-cutover state).
+#
+# The run line below IS the engine argv: the image's entrypoint is the
+# engine and its tools sit beside it on PATH. Unlike every predecessor
+# there is no per-boot VRAM planner to second-guess: the boot ledger in the
+# log states every budget before anything is allocated (weights vs KV
+# split, the expert host pool's coverage of the worst case, the PLE pin,
+# the P2P answer per pair) — read it after every resize. First-boot shape:
+# 2 decode slots (Frank's call), 40 GiB/rank pinned expert pool, fp8 KV,
+# MTP depth 3, prefix cache host 4 GiB + disk 64 GiB, PLE pinned in RAM.
 #
-# The engine is driven by `podman run` DIRECTLY and the run line below IS
-# the engine argv: the image carries FreeToken installed at pinned PR head
-# 77a7201 (scripts/docker/frank-primary-freetoken.Dockerfile builds it from
-# the PR's own docs/install_amd.md stack). Unlike the vLLM fork there is no
-# per-boot planner: experts live in pinned host RAM with an LRU GPU cache,
-# KV is an explicit token pool, and the boot ledger in the log names
-# Shape: 1 GPU (first-boot conservative — the second R9700 sits idle;
-# --tensor-parallel-size EXISTS in FreeToken, so a 2-card TP shape is future
-# tuning once this single-card shape has a decode baseline), KV pool 300,000
-# tokens (24 KiB/token f16 across the 12 full-attention layers = 7.2 GiB;
-# clears the harness worst case of 177,356 tokens and admits one NATIVE
-# 262,144-token conversation), MoE LRU at 10% of all experts (~12 GiB),
-# 2 decode slots. First-boot conservative; `ft ctl cache --kv/--moe` grows
-# pools live, and the env comment block carries the resize law. Flag
-# surface verified against docs/cli.md of the pinned head AND against
-# `ft serve --help` inside the built image; every value here is env-driven
-# (frank-primary.env).
-
 # The root unit (scripts/frank-primary.service, User=dank,
-# LimitMEMLOCK=infinity) grants the pin budget: ~120 GiB of expert weights
-# plus the ~47.7 GiB PLE table pin in host RAM. A plain shell session also
-# works, but the unit is the fleet-parity boot-persistent shape.
+# LimitMEMLOCK=infinity) grants the pin budget: 2 x 40 GiB expert pools +
+# the 47.7 GiB PLE table + 4 GiB prefix host tier — ~132 GiB of pinned
+# pages, which is ALSO why this script refuses to start when ttm's
+# pages_limit/pages_max cap is under FRANK_PRIMARY_TTM_MIN_MIB (see below).
 set -euo pipefail
 
 SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
 ENV_FILE="${FRANK_PRIMARY_ENV_FILE:-$SCRIPT_DIR/frank-primary.env}"
 
 if [ ! -f "$ENV_FILE" ]; then
-    echo "serve-frank-primary: env file missing: $ENV_FILE" >&2
+    echo "ERROR: frank-primary env file absent: $ENV_FILE" >&2
     exit 1
 fi
 set -a
@@ -40,58 +35,120 @@ set -a
 source "$ENV_FILE"
 set +a
 
-: "${FRANK_PRIMARY_IMAGE:?FRANK_PRIMARY_IMAGE must name the FreeToken image}"
-: "${FRANK_PRIMARY_MODEL_DIR:?FRANK_PRIMARY_MODEL_DIR must name the FP8 payload}"
-: "${FRANK_PRIMARY_MODEL_SHARDS:?FRANK_PRIMARY_MODEL_SHARDS must give the shard count}"
+: "${FRANK_PRIMARY_IMAGE:?FRANK_PRIMARY_IMAGE must name the radiance image}"
+: "${FRANK_PRIMARY_MODEL_DIR:?FRANK_PRIMARY_MODEL_DIR must name the .rad dir}"
+: "${FRANK_PRIMARY_MODEL_FILE:?FRANK_PRIMARY_MODEL_FILE must name the .rad file}"
+: "${FRANK_PRIMARY_STATE_DIR:?FRANK_PRIMARY_STATE_DIR must name the /data state dir}"
 : "${FRANK_PRIMARY_CONTAINER:?FRANK_PRIMARY_CONTAINER must name the container}"
 : "${FRANK_PRIMARY_SERVED_NAME:?FRANK_PRIMARY_SERVED_NAME must name the alias}"
-: "${FRANK_PRIMARY_PUBLISH:?FRANK_PRIMARY_PUBLISH must be host:container}"
-: "${FRANK_PRIMARY_FT_GPU:?FRANK_PRIMARY_FT_GPU must name the GPU index}"
-: "${FRANK_PRIMARY_FT_NUM_TOKENS:?FRANK_PRIMARY_FT_NUM_TOKENS must size the KV pool}"
-: "${FRANK_PRIMARY_FT_MAX_RUNNING:?FRANK_PRIMARY_FT_MAX_RUNNING must be set}"
-: "${FRANK_PRIMARY_FT_MAX_OUTPUT_TOKENS:?FRANK_PRIMARY_FT_MAX_OUTPUT_TOKENS must be set}"
-: "${FRANK_PRIMARY_FT_MOE_RATE:?FRANK_PRIMARY_FT_MOE_RATE must be set}"
+: "${FRANK_PRIMARY_PORT:?FRANK_PRIMARY_PORT must be the port}"
+: "${FRANK_PRIMARY_TEMPLATE:?FRANK_PRIMARY_TEMPLATE must name the chat template}"
+: "${FRANK_PRIMARY_TP:?FRANK_PRIMARY_TP must be set}"
+: "${FRANK_PRIMARY_TP_WIRE:?FRANK_PRIMARY_TP_WIRE must be set}"
+: "${FRANK_PRIMARY_MAX_SEQS:?FRANK_PRIMARY_MAX_SEQS must be set}"
+: "${FRANK_PRIMARY_MAX_MODEL_LEN:?FRANK_PRIMARY_MAX_MODEL_LEN must be set}"
+: "${FRANK_PRIMARY_PLACEMENT:?FRANK_PRIMARY_PLACEMENT must be set}"
+: "${FRANK_PRIMARY_HOST_POOL_MIB:?FRANK_PRIMARY_HOST_POOL_MIB must be set}"
+: "${FRANK_PRIMARY_GPU_HEADROOM_MIB:?FRANK_PRIMARY_GPU_HEADROOM_MIB must be set}"
+: "${FRANK_PRIMARY_VRAM_WEIGHTS_MIB:?FRANK_PRIMARY_VRAM_WEIGHTS_MIB must be set}"
+: "${FRANK_PRIMARY_VRAM_KV_MIB:?FRANK_PRIMARY_VRAM_KV_MIB must be set}"
+: "${FRANK_PRIMARY_KV_DTYPE:?FRANK_PRIMARY_KV_DTYPE must be set}"
+: "${FRANK_PRIMARY_SPEC_TOKENS:?FRANK_PRIMARY_SPEC_TOKENS must be set}"
+: "${FRANK_PRIMARY_MAX_BATCHED_TOKENS:?FRANK_PRIMARY_MAX_BATCHED_TOKENS must be set}"
+: "${FRANK_PRIMARY_PREFIX_HOST_MIB:?FRANK_PRIMARY_PREFIX_HOST_MIB must be set}"
+: "${FRANK_PRIMARY_PREFIX_DISK_MIB:?FRANK_PRIMARY_PREFIX_DISK_MIB must be set}"
+: "${FRANK_PRIMARY_NGRAM_PLACEMENT:?FRANK_PRIMARY_NGRAM_PLACEMENT must be set}"
+: "${FRANK_PRIMARY_TTM_MIN_MIB:?FRANK_PRIMARY_TTM_MIN_MIB must be set}"
+: "${FRANK_PRIMARY_RAD_MIN_MIB:?FRANK_PRIMARY_RAD_MIN_MIB must be set}"
 
-# The image gate is EXISTENCE IN STORAGE, not a registry digest: the tag is
-# built ON THE HOST (podman build from
-# scripts/docker/frank-primary-freetoken.Dockerfile) and local builds carry
-# no RepoDigests entry — the 362-line vLLM-era RepoDigests grep refused
-# exactly this shape. Pinning lives in the Dockerfile instead: the FreeToken
-# source is checked out at a hardcoded PR-head SHA and the base image is
-# digest-pinned there. `podman image exists` accepts both a local tag and a
-# name@digest ref, and it is one command — no pipefail/grep race (the
-# 2026-09-30 lesson) for either shape.
+# The image gate is the digest-pinned ref itself: a name@sha256 ref exists
+# in storage only if THAT manifest was pulled — a retagged look-alike or a
+# newer latest does not satisfy it. `podman image exists` takes a
+# name@digest ref in one command — no pipefail/grep race (the 2026-09-30
+# lesson). Runs as dank: the unit's process uses dank's ROOTLESS podman
+# storage (root unit, User=dank — the fleet's frank-primary shape).
 if ! podman image exists "$FRANK_PRIMARY_IMAGE"; then
-    echo "serve-frank-primary: image absent from rootless storage: $FRANK_PRIMARY_IMAGE" >&2
-    echo "  build it: podman build -t $FRANK_PRIMARY_IMAGE -f scripts/docker/frank-primary-freetoken.Dockerfile scripts/docker/" >&2
+    echo "ERROR: radiance image absent: $FRANK_PRIMARY_IMAGE" >&2
+    echo "frank-primary: podman pull docker://stilldeadcode/radiance:latest (as dank)" >&2
     exit 1
 fi
 
-if [ ! -f "$FRANK_PRIMARY_MODEL_DIR/config.json" ]; then
-    echo "serve-frank-primary: payload config missing: $FRANK_PRIMARY_MODEL_DIR/config.json" >&2
+# The payload gate: the .rad container file, sized. A truncated convert
+# would otherwise buy a guaranteed long-timeout boot instead of this fast
+# failure. The staging dir / raw checkpoint are NOT this script's concern
+# (scripts/build-frank-primary-rad.sh built the file; its rad-info output
+# is the record).
+RAD="$FRANK_PRIMARY_MODEL_DIR/$FRANK_PRIMARY_MODEL_FILE"
+if [ ! -f "$RAD" ]; then
+    echo "ERROR: .rad container missing: $RAD" >&2
+    echo "frank-primary: run scripts/build-frank-primary-rad.sh" >&2
     exit 1
 fi
-SHARDS="$(find "$FRANK_PRIMARY_MODEL_DIR" -maxdepth 1 -name 'model-*-of-*.safetensors' | wc -l)"
-if [ "$SHARDS" != "$FRANK_PRIMARY_MODEL_SHARDS" ]; then
-    echo "serve-frank-primary: payload shard count wrong: $FRANK_PRIMARY_MODEL_DIR" >&2
-    echo "  found $SHARDS safetensors, want $FRANK_PRIMARY_MODEL_SHARDS" >&2
-    echo "  re-fetch: scripts/download-frank-primary.sh" >&2
+RAD_MIB=$(( $(stat -c %s "$RAD") / 1048576 ))
+if [ "$RAD_MIB" -lt "$FRANK_PRIMARY_RAD_MIN_MIB" ]; then
+    echo "ERROR: .rad container too small: $RAD is ${RAD_MIB} MiB, want >= $FRANK_PRIMARY_RAD_MIN_MIB" >&2
+    echo "frank-primary: the convert was truncated — rebuild it" >&2
     exit 1
 fi
-if [ ! -f "$FRANK_PRIMARY_MODEL_DIR/model.safetensors.index.json" ]; then
-    echo "serve-frank-primary: weight-map index missing: $FRANK_PRIMARY_MODEL_DIR/model.safetensors.index.json" >&2
+
+if [ ! -f "$FRANK_PRIMARY_TEMPLATE" ]; then
+    echo "ERROR: chat template missing: $FRANK_PRIMARY_TEMPLATE" >&2
     exit 1
 fi
 
+mkdir -p "$FRANK_PRIMARY_STATE_DIR/kvcache/frank-primary"
+# The engine's create is NOT recursive: with only the state dir mounted, its
+# "cannot create /data/kvcache/frank-primary: No such file or directory"
+# left the SSD prefix tier off (measured 2026-10-06 boot ledger). Pre-create
+# the full path on the host so the tier opens on first boot.
+
+mkdir -p "$FRANK_PRIMARY_STATE_DIR/tune"
+# rad-tune's kernel-selection cache (see the runbook's "Tuning levers"):
+# the engine's selector reads $RADIANCE_HOME/tune/<machine>.tune at boot;
+# $RADIANCE_HOME defaults to /opt/radiance, which lives in the image, so
+# the cache must ride a host mount or it dies with the --rm container
+# that wrote it. The dir is pre-created for the same reason as kvcache
+# (the engine's create is not recursive).
+
 if [ ! -e /dev/kfd ] || [ ! -d /dev/dri ]; then
-    echo "serve-frank-primary: GPU device nodes absent (/dev/kfd, /dev/dri)" >&2
+    echo "ERROR: GPU device nodes absent (/dev/kfd, /dev/dri)" >&2
+    exit 1
+fi
+
+# TTM preflight: the pinned ledger (~2 x 40 GiB pools + 47.7 GiB PLE + 4 GiB
+# prefix host = ~132 GiB) rides TTM's shared pages, capped by ttm's
+# pages_limit (the current kernel exposes NO pages_max; the cap function
+# takes the min over the knobs that exist). Below FRANK_PRIMARY_TTM_MIN_MIB
+# the load dies mid-pin with an allocator error instead of this named
+# refusal — raise the knob first (runtime sysfs write; kernel cmdline
+# fallback + one reboot only if the file is read-only), see the runbook.
+# RAISED 2026-10-05 pre-cutover to 52,428,800 pages = 200 GiB.
+ttm_cap_mib() {
+    # min over the knobs THIS kernel exposes — the current kernel has
+    # pages_limit only (no pages_max); a knob that is absent is skipped,
+    # not a zero (a zero would refuse a healthy cap).
+    local v cur=""
+    for p in /sys/module/ttm/parameters/pages_limit /sys/module/ttm/parameters/pages_max; do
+        [ -r "$p" ] || continue
+        v="$(cat "$p" 2>/dev/null)" || continue
+        [ -n "$v" ] || continue
+        v=$(( v * 4 / 1024 ))
+        if [ "$v" -lt "${cur:-999999999}" ]; then cur=$v; fi
+    done
+    echo "${cur:-0}"
+}
+TTM_CAP="$(ttm_cap_mib)"
+if [ "$TTM_CAP" -lt "$FRANK_PRIMARY_TTM_MIN_MIB" ]; then
+    echo "ERROR: ttm pages cap is ${TTM_CAP} MiB, the pinned ledger needs >= $FRANK_PRIMARY_TTM_MIN_MIB" >&2
+    echo "frank-primary: raise /sys/module/ttm/parameters/pages_limit (and pages_max if the kernel exposes one) to >= $FRANK_PRIMARY_TTM_MIN_MIB in 4 KiB pages" >&2
     exit 1
 fi
 
 # A crashed/killed unit can leave the container name registered (systemd
 # SIGKILLs conmon before cleanup runs). Clear OUR name plus the legacy
-# identities (the vLLM-era frank-primary-qwen, the retired R9V container and
-# the ad hoc experiment containers all hold this GPU); all idempotent.
+# identities (the FreeToken-era frank-primary-qwen, the retired R9V
+# container and the ad hoc experiment containers all hold this GPU); all
+# idempotent.
 for CTR in $FRANK_PRIMARY_LEGACY_CONTAINERS "$FRANK_PRIMARY_CONTAINER"; do
     podman rm -f "$CTR" >/dev/null 2>&1 || true
 done
@@ -99,30 +156,47 @@ done
 LOG=/home/dank/logs/frank_primary.log
 mkdir -p "$(dirname "$LOG")"
 
-# --device /dev/dri is the WHOLE DIRECTORY (individual renderD nodes enumerate
-# without the card nodes and HIP refuses contexts). --ipc=host for the
-# engine's shared memory; memlock unbounded for the ~120 GiB pinned expert
-# rows + the 47.7 GiB PLE pin (the unit's LimitMEMLOCK=infinity is the other
-# half of that law).
-# HIP_VISIBLE_DEVICES pins visibility to ONE card so in-container index 0 is
-# the right R9700; --gpu 0 selects it inside the visible set.
+# Publisher-vetted container contract (docs/DOCKER.md, compose/common.yaml):
+# --network host (the server binds the host's addresses directly, no NAT),
+# --init (the engine releases the cards on SIGTERM), seccomp=unconfined
+# (Docker/podman's default profile refuses io_uring, which the n-gram
+# table's reads go through, and the NUMA binding calls the ROCm runtime
+# makes for host memory), memlock unbounded (the pinned ledger — legal
+# because the root unit grants LimitMEMLOCK=infinity to this dank process),
+# /dev/kfd + /dev/dri (render/video groups are moot for a root container).
+# The template rides a single-file mount; the .rad dir is ro; /data holds
+# the prefix-cache disk tier.
 podman run -d --name "$FRANK_PRIMARY_CONTAINER" \
-  --publish "$FRANK_PRIMARY_PUBLISH" \
-  --ipc=host --ulimit memlock=-1:-1 \
-  --cap-add SYS_PTRACE \
-  --device /dev/kfd --device /dev/dri --device /dev/mem \
-  -e HIP_VISIBLE_DEVICES="$FRANK_PRIMARY_FT_GPU" \
+  --network host \
+  --init \
+  --security-opt seccomp=unconfined \
+  --ulimit memlock=-1:-1 \
+  --device /dev/kfd --device /dev/dri \
   -v "$FRANK_PRIMARY_MODEL_DIR":/models:ro \
+  -v "$FRANK_PRIMARY_STATE_DIR":/data \
+  -v "$FRANK_PRIMARY_STATE_DIR/tune":/opt/radiance/share/radiance/tune \
+  -v "$FRANK_PRIMARY_TEMPLATE":/templates/template.jinja:ro \
   "$FRANK_PRIMARY_IMAGE" \
-  ft serve --model /models \
-    --served-model-name "$FRANK_PRIMARY_SERVED_NAME" \
-    --host 0.0.0.0 --port 7770 \
-    --gpu "$FRANK_PRIMARY_FT_GPU" \
-    --num-tokens "$FRANK_PRIMARY_FT_NUM_TOKENS" \
-    --max-running-requests "$FRANK_PRIMARY_FT_MAX_RUNNING" \
-    --max-output-tokens "$FRANK_PRIMARY_FT_MAX_OUTPUT_TOKENS" \
-    --moe-cache-rate "$FRANK_PRIMARY_FT_MOE_RATE" \
-    ${FRANK_PRIMARY_FT_EXTRA_ARGS:+$FRANK_PRIMARY_FT_EXTRA_ARGS} \
+  --model "/models/$FRANK_PRIMARY_MODEL_FILE" \
+  --host :: --port "$FRANK_PRIMARY_PORT" \
+  --served-model-name "$FRANK_PRIMARY_SERVED_NAME" \
+  --override-chat-template /templates/template.jinja \
+  --tp "$FRANK_PRIMARY_TP" --tp-wire "$FRANK_PRIMARY_TP_WIRE" \
+  --max-num-seqs "$FRANK_PRIMARY_MAX_SEQS" \
+  --max-model-len "$FRANK_PRIMARY_MAX_MODEL_LEN" \
+  --placement "$FRANK_PRIMARY_PLACEMENT" \
+  --host-pool-mib "$FRANK_PRIMARY_HOST_POOL_MIB" \
+  --gpu-headroom-mib "$FRANK_PRIMARY_GPU_HEADROOM_MIB" \
+  --vram-weights-mib "$FRANK_PRIMARY_VRAM_WEIGHTS_MIB" \
+  --vram-kv-mib "$FRANK_PRIMARY_VRAM_KV_MIB" \
+  --kv-cache-dtype "$FRANK_PRIMARY_KV_DTYPE" \
+  --prefix-cache-host-mib "$FRANK_PRIMARY_PREFIX_HOST_MIB" \
+  --prefix-cache-dir /data/kvcache/frank-primary \
+  --prefix-cache-disk-mib "$FRANK_PRIMARY_PREFIX_DISK_MIB" \
+  --ngram-placement "$FRANK_PRIMARY_NGRAM_PLACEMENT" \
+  --num-speculative-tokens "$FRANK_PRIMARY_SPEC_TOKENS" \
+  --max-num-batched-tokens "$FRANK_PRIMARY_MAX_BATCHED_TOKENS" \
+  ${FRANK_PRIMARY_EXTRA_ARGS:+$FRANK_PRIMARY_EXTRA_ARGS} \
   > "$LOG" 2>&1
 
 # From here on, this script's lifetime IS the container's: wait returns the

scripts/serve-frank-secondary.sh

@@ -0,0 +1,132 @@
+#!/usr/bin/env bash
+# Serve HauhauCS/Qwen3.5-9B-Uncensored-HauhauCS-Aggressive (Q6_K) as
+# frank-secondary on Dankclaw's RTX 3090 via llama.cpp (master build, CUDA sm_86).
+#
+# 2026-10-07 (Frank): Qwen3.5-9B swap at TWO 200,704-token conversations
+# (--ctx-size 401408 --parallel 2) with EXACT f16 KV — the 9B rung frees the
+# room the 26B MoE Gemma4 shape lacked, so quantized V retires. MTP stays off:
+# this GGUF carries no MTP tensors at all (verified from its tensor-name
+# list), so there is no in-file head to draft with. Verbosity 4 ships so the
+# VRAM-fit forensics (KV layout, buffer sizes, graph capture) land in the journal.
+#
+# Architecture: qwen35 — dense 9B, 32 layers with full_attention_interval 4
+# (8 full-attention layers at 3,7,...,31 + 24 Gated DeltaNet linear layers),
+# 16 heads / 4 KV heads × 256 dim, native 262,144, vocab 248,320. The KV pool
+# scales with the 8 full layers: 32 KiB/token at f16 K + f16 V (12.25 GiB at
+# the 401,408 pool); the DeltaNet layers cost a fixed recurrent state per
+# slot. Feasibility is priced from the MiMo-V2.6-Distill-Qwen-9B boots (same
+# geometry, same card, same shape): 21,276/2,849 at ubatch 1024, 21,846/2,279
+# at ubatch 2048 — the boot ledger, not arithmetic, is the referee.
+# Vision comes from the publisher's own BF16 projector (the model is natively
+# multimodal), so --mmproj is what gives this endpoint eyes; MMPROJ_FILE="" is
+# the documented text-only opt-out.
+#
+# Sampling: llama.cpp takes it per request (the fleet harness sends the
+# publisher recipe from models.yml: thinking temp 0.6 / top_p 0.95 / top_k 20).
+#
+# Tunables come from frank-secondary.env unless FRANK_SECONDARY_ENV_FILE overrides.
+set -euo pipefail
+
+SCRIPT_DIR="$(cd -- "$(dirname -- "${BASH_SOURCE[0]}")" && pwd)"
+ENV_FILE="${FRANK_SECONDARY_ENV_FILE:-$SCRIPT_DIR/frank-secondary.env}"
+if [[ -f "$ENV_FILE" ]]; then
+    set -a
+    # shellcheck disable=SC1090
+    source "$ENV_FILE"
+    set +a
+fi
+
+# --- defaults (used when the env file does not set them) -------------------
+: "${LLAMA_BIN:=/home/dank/GitPackages/llama-master/build-cuda/bin/llama-server}"
+: "${MODEL_FILE:=/home/dank/models/Qwen35-9B-HauhauCS-Q6K/Qwen3.5-9B-Uncensored-HauhauCS-Aggressive-Q6_K.gguf}"
+: "${ALIAS:=frank-zealot}"
+: "${BIND_ADDR:=0.0.0.0}"
+: "${LISTEN_PORT:=7771}"
+# ctx-size is the POOL llama.cpp divides across slots: 401,408 / 2 = 200,704
+# per conversation, inside the model's trained 262,144. The harness
+# contextWindow MUST quote the PER-SLOT number — a longer prompt is answered
+# with a 400, never truncated.
+: "${CTX_SIZE:=401408}"
+: "${PARALLEL:=2}"
+# Compute-buffer geometry. Ubatch is a prefill knob (decode never moves).
+# 2048 is the measured CUDA optimum (MiMo era, same arch/shape: +4.6 % prefill
+# at 8.9k depth, decode flat, 616 MiB). The logical batch is VRAM- and
+# throughput-neutral at a fixed ubatch.
+: "${BATCH_SIZE:=4096}"
+: "${UBATCH_SIZE:=2048}"
+# Exact (unquantized) KV: f16 K + f16 V, restoring the MiMo-era contract that
+# the 26B Gemma4 shape could not afford. 32 KiB/token at this geometry
+# (8 full-attention layers × 4 KV heads × 256 dim); 12.25 GiB at the 401,408
+# pool. Explicit either way so the contract is visible in the argv, and
+# --flash-attn stays required for any quantized-V fallback (q8_0 = llama.cpp's
+# FP8 class; no true fp8 KV dtype exists in this build).
+: "${CACHE_TYPE_K:=f16}"
+: "${CACHE_TYPE_V:=f16}"
+# Speculative decoding is OFF: this Q6_K file carries NO nextn/mtp tensors, so
+# there is no in-file head (the law: MTP is a property of the file, not of the
+# flags). SPEC_TYPE=none strips BOTH speculative flags (flags move as a set);
+# naming a drafter requires SPEC_DRAFT_FILE alongside SPEC_TYPE — the Gemma4
+# assistant on disk is arch gemma4-assistant and cannot drafter-match qwen35.
+: "${SPEC_TYPE:=none}"
+: "${SPEC_DRAFT_N_MAX:=3}"
+: "${SPEC_DRAFT_FILE:=}"
+: "${NGL:=99}"
+# Log verbosity threshold (llama.cpp -lv). 3 is the engine default (info);
+# 4 adds debug detail (KV layout, buffer sizes, graph capture) which is what
+# the VRAM-fit forensics need. Levels above 4 flood the journal.
+: "${VERBOSITY:=3}"
+# Vision projector. No colon on purpose: an explicitly empty value means
+# "text-only, on purpose" and must NOT be replaced by the default path.
+: "${MMPROJ_FILE=/home/dank/models/Qwen35-9B-HauhauCS-Q6K/mmproj-Qwen3.5-9B-Uncensored-HauhauCS-Aggressive-BF16.gguf}"
+
+# Fail loudly rather than half-loading: a missing file is a download that
+# has not finished, and Restart=always would turn it into a crash loop that
+# reads like a runtime fault.
+for path in "$LLAMA_BIN" "$MODEL_FILE"; do
+    [[ -e "$path" ]] || { echo "frank-secondary: missing $path" >&2; exit 1; }
+done
+EXTRA_ARGS=()
+if [[ -n "$MMPROJ_FILE" ]]; then
+    [[ -e "$MMPROJ_FILE" ]] || { echo "frank-secondary: missing $MMPROJ_FILE" >&2; exit 1; }
+    EXTRA_ARGS+=(--mmproj "$MMPROJ_FILE")
+fi
+
+# Speculative flags move as a set: they reach the argv ONLY when a drafter
+# is named. SPEC_TYPE=none (the shipped contract) strips both — a no-spec
+# baseline must not carry half a spec pair.
+if [[ -n "$SPEC_TYPE" && "$SPEC_TYPE" != "none" ]]; then
+    EXTRA_ARGS+=(--spec-type "$SPEC_TYPE" --spec-draft-n-max "$SPEC_DRAFT_N_MAX")
+if [[ -n "$SPEC_DRAFT_FILE" ]]; then
+    [[ -e "$SPEC_DRAFT_FILE" ]] || { echo "frank-secondary: missing $SPEC_DRAFT_FILE" >&2; exit 1; }
+    EXTRA_ARGS+=(--spec-draft-model "$SPEC_DRAFT_FILE")
+fi
+fi
+
+# The UVM device node is created lazily by nvidia-modprobe on the first CUDA
+# open after boot; make sure it exists before the server needs it.
+nvidia-modprobe -u -c=0 2>/dev/null || true
+
+# Flash attention stays pinned on: it is REQUIRED for quantized V (q8_0),
+# and the build carries GGML_CUDA_FA_ALL_QUANTS=ON (since 2026-09-17), so
+# the q8_0 KV contract in frank-secondary.env is a real path — without this
+# flag or that build option the engine silently falls back and the
+# contract becomes a lie.
+ARGS=(
+    --model "$MODEL_FILE"
+    --alias "$ALIAS"
+    --host "$BIND_ADDR"
+    --port "$LISTEN_PORT"
+    --ctx-size "$CTX_SIZE"
+    --parallel "$PARALLEL"
+    --batch-size "$BATCH_SIZE"
+    --ubatch-size "$UBATCH_SIZE"
+    --n-gpu-layers "$NGL"
+    --flash-attn on
+    --cache-type-k "$CACHE_TYPE_K"
+    --cache-type-v "$CACHE_TYPE_V"
+    --log-verbosity "$VERBOSITY"
+    --jinja
+    --reasoning auto
+    --reasoning-format deepseek
+)
+exec "$LLAMA_BIN" "${ARGS[@]}" ${EXTRA_ARGS[@]+"${EXTRA_ARGS[@]}"}

scripts/serve-frank-small.sh

@@ -1,99 +0,0 @@
-#!/usr/bin/env bash
-# Serve ornith-ai/Ornith-1.5-9B-GGUF (Q8_0) as frank-small on
-# Dankclaw's RTX 3090 via llama.cpp (master build, CUDA sm_86).
-#
-# 2026-10-01 night (Frank): swapped from SGLang + INT8-W8A16-AutoRound
-# safetensors to the llama.cpp GGUF path, because llama.cpp is the only
-# runtime on this host that moves K and V independently — the asymmetric
-# f16-K + q8_0-V contract that endpoint was built for. 2026-10-03 (Frank):
-# the pair is now f16/f16 — two wide slots freed the VRAM, so the values are
-# unquantized too. The per-tensor flags stay explicit either way.
-#
-# Architecture: qwen3_5 hybrid — 32 layers, 8 full-attention + 24 Gated
-# DeltaNet, native 262144 window. KV lives only in the 8 attention layers;
-# the DeltaNet state is per-slot fixed size. Vision comes from the repo's
-# separate projector (the Q6_K GGUF carries no `v.*` tensors at all), so
-# --mmproj is what gives this endpoint eyes; MMPROJ_FILE="" is the documented
-# text-only opt-out.
-#
-# Sampling: llama.cpp takes it per request (the fleet harness sends its
-# recipe from models.yml); greedy is the harness default.
-#
-# Tunables come from frank-small.env unless FRANK_SMALL_ENV_FILE overrides.
-set -euo pipefail
-
-SCRIPT_DIR="$(cd -- "$(dirname -- "${BASH_SOURCE[0]}")" && pwd)"
-ENV_FILE="${FRANK_SMALL_ENV_FILE:-$SCRIPT_DIR/frank-small.env}"
-if [[ -f "$ENV_FILE" ]]; then
-    set -a
-    # shellcheck disable=SC1090
-    source "$ENV_FILE"
-    set +a
-fi
-
-# --- defaults (used when the env file does not set them) -------------------
-: "${LLAMA_BIN:=/home/dank/GitPackages/llama-master/build-cuda/bin/llama-server}"
-: "${MODEL_FILE:=/home/dank/models/Ornith-Q8/Ornith-1.5-9B-Q8_0.gguf}"
-: "${ALIAS:=frank-small}"
-: "${BIND_ADDR:=0.0.0.0}"
-: "${LISTEN_PORT:=7771}"
-# ctx-size is the POOL llama.cpp divides across slots: 401408 / 2 gives
-# 200,704 per slot — two independent conversations, each one wide enough to
-# hold a whole frank-primary-sized prompt in a single shot (the native 262,144
-# no longer fits twice on a 24 GiB card once the vision tower is loaded;
-# frank-small.env carries the three measurements that set this number).
-: "${CTX_SIZE:=401408}"
-: "${PARALLEL:=2}"
-# Compute-buffer geometry. Before 2026-10-03 this endpoint never passed either
-# flag, so it ran on llama.cpp's own defaults (2048/512). The pair is 4096/2048
-# since 2026-10-05: on this CUDA card the prefill curve climbs all the way to
-# ubatch 2048 (unlike the R9700 layer-split sibling, where 512 is the peak),
-# and the logical batch is VRAM- and throughput-neutral. frank-small.env
-# carries the sweep tables and the VRAM risk note.
-: "${BATCH_SIZE:=4096}"
-: "${UBATCH_SIZE:=2048}"
-: "${CACHE_TYPE_K:=f16}"
-: "${CACHE_TYPE_V:=f16}"
-: "${NGL:=99}"
-# Vision projector. No colon on purpose: an explicitly empty value means
-# "text-only, on purpose" and must NOT be replaced by the default path.
-: "${MMPROJ_FILE=/home/dank/models/Ornith-Q8/mmproj-Ornith-1.5-9B-BF16.gguf}"
-
-# Fail loudly rather than half-loading: a missing file is a download that
-# has not finished, and Restart=always would turn it into a crash loop that
-# reads like a runtime fault.
-for path in "$LLAMA_BIN" "$MODEL_FILE"; do
-    [[ -e "$path" ]] || { echo "frank-small: missing $path" >&2; exit 1; }
-done
-EXTRA_ARGS=()
-if [[ -n "$MMPROJ_FILE" ]]; then
-    [[ -e "$MMPROJ_FILE" ]] || { echo "frank-small: missing $MMPROJ_FILE" >&2; exit 1; }
-    EXTRA_ARGS+=(--mmproj "$MMPROJ_FILE")
-fi
-
-# The UVM device node is created lazily by nvidia-modprobe on the first CUDA
-# open after boot; make sure it exists before the server needs it.
-nvidia-modprobe -u -c=0 2>/dev/null || true
-
-# Flash attention stays pinned on: it is the fast path for f16 KV, and it is
-# the only path that could serve quantized V at all — so if CACHE_TYPE_V is
-# ever set back to q8_0 this flag must not be lost, or the build falls back
-# silently and the KV contract in frank-small.env becomes a lie.
-ARGS=(
-    --model "$MODEL_FILE"
-    --alias "$ALIAS"
-    --host "$BIND_ADDR"
-    --port "$LISTEN_PORT"
-    --ctx-size "$CTX_SIZE"
-    --parallel "$PARALLEL"
-    --batch-size "$BATCH_SIZE"
-    --ubatch-size "$UBATCH_SIZE"
-    --n-gpu-layers "$NGL"
-    --flash-attn on
-    --cache-type-k "$CACHE_TYPE_K"
-    --cache-type-v "$CACHE_TYPE_V"
-    --jinja
-    --reasoning auto
-    --reasoning-format deepseek
-)
-exec "$LLAMA_BIN" "${ARGS[@]}" ${EXTRA_ARGS[@]+"${EXTRA_ARGS[@]}"}

scripts/stack-qwen38-official-fp8.py

@@ -0,0 +1,556 @@
+#!/usr/bin/env python3
+"""stack-qwen38-official-fp8.py — reshape the official Qwen/Qwen3.8-Flash-Next-FP8
+checkpoint into the form the shipped radiance qwen4exp plugin maps.
+
+Run INSIDE the staging dir's tree (python3 + numpy on the host; data never
+leaves the box). Two transforms, both VALUE-PRESERVING:
+
+1. EXPERTS: the shipped plugin's stacked layout names ONE tensor per family
+   (`mlp.experts.gate_up_proj` [E, 2F, H], `mlp.experts.down_proj` [E, H, F])
+   plus a `<family>_scale_inv` plane at the same index (NO `.weight` in the
+   stacked name — scale_name() appends _scale_inv to the tensor name verbatim),
+   while the official FP8 repo ships per-expert unfused tensors
+   (`experts.<e>.{gate,up,down}_proj.weight` + scale). Stacking is pure byte
+   concatenation along dim 0 — gate rows
+   first inside each expert's gate_up slab, which is the order the block
+   wants (rad_block_moe_fp8.h: "a stacked gate_up is ALREADY FUSED in the
+   order this block wants"). No requantisation: bytes are moved, not decoded.
+
+2. PLE TABLE: the plugin's fp8 branch wants the table's codes beside a
+   ONE-ELEMENT bf16 scale plane (`blk.%d.ple_ngram.scale`, view {1}); a
+   per-shard-blocked scale can never fit that view, and `rtn` re-encoding the
+   RAW scaleless codes would overflow (v / 1.99e-4 exceeds e4m3's 448 max).
+   So staging decodes the scaleless shards with the checkpoint's OWN bf16
+   scale into a bf16 table (exact: every e4m3 code value and the scale are
+   exactly representable in bf16 to within the code's own 2^-3 grid), and the
+   convert's rtn rule re-encodes at that same scale — the nearest-code
+   distance is 2^-3 while the bf16 rounding perturbs by 2^-9, so every
+   original code is restored exactly and the container gets the checkpoint's
+   own codes plus the one-element scale plane.
+
+The convert then runs with NO recipe except:
+  blk.*.ple_ngram.weight=rtn:codes=fp8_e4m3,block=*x*,scale=bf16,rule=fixed,scale_value=<s>
+with <s> read from the checkpoint and written to ple_scale.txt.
+
+Idempotent: re-runs skip when the staged index already carries the stacked
+names, the merged bf16 shard_0, and split_ngram_parts == 1. --force redoes.
+"""
+
+import argparse
+import json
+import mmap
+import multiprocessing as mp
+import os
+import re
+import struct
+import sys
+import time
+import numpy as np
+F8, BF16 = "F8_E4M3", "BF16"
+
+
+def log(msg):
+    print(msg, flush=True)
+
+
+# ---------------------------------------------------------------- safetensors
+
+def parse_header(path):
+    """Return (header_len, {name: (dtype, shape, data_offsets)})."""
+    with open(path, "rb") as fh:
+        (hlen,) = struct.unpack("<Q", fh.read(8))
+        hdr = json.loads(fh.read(hlen))
+    return hlen, hdr
+
+
+def write_safetensors(path, tensors, data_iter):
+    """tensors: [(name, dtype, shape, nbytes)]; data_iter yields bytes per tensor."""
+    hdr = {}
+    off = 0
+    for name, dtype, shape, nbytes in tensors:
+        hdr[name] = {"dtype": dtype, "shape": list(shape),
+                     "data_offsets": [off, off + nbytes]}
+        off += nbytes
+    blob = json.dumps(hdr, separators=(",", ":")).encode("utf-8")
+    pad = (-len(blob)) % 8  # keep the data start 8-byte aligned
+    blob += b" " * pad
+    with open(path, "wb") as fh:
+        fh.write(struct.pack("<Q", len(blob)))
+        fh.write(blob)
+        for _, _, _, nbytes in tensors:
+            fh.write(next(data_iter).tobytes())
+        fh.flush()
+        os.fsync(fh.fileno())
+    return 8 + len(blob) + sum(t[3] for t in tensors)
+
+
+# ------------------------------------------------------------------- preflight
+
+def build_meta(stage, index):
+    """name -> (file, global_byte_offset, nbytes, dtype, shape) for every tensor."""
+    files = sorted(set(index["weight_map"].values()))
+    meta = {}
+    for f in files:
+        path = os.path.join(stage, f)
+        hlen, hdr = parse_header(path)
+        for name, e in hdr.items():
+            if name == "__metadata__":
+                continue
+            b0, b1 = e["data_offsets"]
+            meta[name] = (f, 8 + hlen + b0, b1 - b0, e["dtype"], e["shape"])
+    return meta
+
+
+def e4m3_lut():
+    lut = np.zeros(256, np.float32)
+    for b in range(256):
+        sign = -1.0 if b & 0x80 else 1.0
+        e = (b >> 3) & 0xF
+        m = b & 0x7
+        if e == 15 and m == 7:
+            v = np.float32(np.nan)  # OCP e4m3fn: only 0x7F/0xFF are NaN
+        elif e == 0:
+            v = sign * (m / 8.0) * 2.0 ** -6
+        else:
+            v = sign * (1.0 + m / 8.0) * 2.0 ** (e - 7)
+        lut[b] = v
+    assert lut[0x38] == 1.0 and lut[0x7E] == 448.0
+    assert np.isnan(lut[0x7F]) and lut[0x00] == 0.0
+    assert lut[0x08] == 2.0 ** -6 and lut[0x80] == 0.0
+    return lut
+
+
+def f32_to_bf16_rne(v):
+    """float32 array -> uint16 bf16 bits, round-to-nearest-even."""
+    q = np.ascontiguousarray(v, dtype=np.float32)
+    q[~np.isfinite(q)] = 0.0
+    bits = q.view(np.uint32)
+    r = bits + 0x7FFF + ((bits >> 16) & 1)
+    return (r >> 16).astype(np.uint16)
+
+
+# ------------------------------------------------------------------- workers
+
+def stack_prefix(job):
+    """Byte-stack one layer's 512 experts into a 4-tensor safetensors file."""
+    (stage, pre, idx, n_e, shapes, meta) = job
+    (f2, h) = shapes["ffn2_h"]          # 2*ffn, n_embd
+    (f,) = shapes["ffn"]                # ffn
+    gs, ds = shapes["gs"], shapes["ds"] # scale shapes [5,20] and [20,5]
+
+    # byte widths: fp8 is 1 B/element, bf16 2 B; the fused gate_up scale is
+    # the gate and up scale planes concatenated (gate rows first)
+    g_bytes = (f2 // 2) * h
+    gs_bytes = gs[0] * gs[1] * 2          # ONE projection's scale plane
+    gus_bytes = 2 * gs_bytes              # fused scale per expert
+    dn_bytes = h * f
+    ds_bytes = ds[0] * ds[1] * 2
+
+    gu = np.empty(n_e * f2 * h, np.uint8)
+    gus = np.empty(n_e * gus_bytes, np.uint8)
+    dn = np.empty(n_e * dn_bytes, np.uint8)
+    dns = np.empty(n_e * ds_bytes, np.uint8)
+
+    mmaps = {}
+
+    def view(name):
+        f_, off, ln, dt, shp = meta[name]
+        if f_ not in mmaps:
+            fh = open(os.path.join(stage, f_), "rb")
+            mmaps[f_] = mmap.mmap(fh.fileno(), 0, access=mmap.ACCESS_READ)
+        return memoryview(mmaps[f_])[off:off + ln], (dt, shp)
+
+    def need(name, dt, shp):
+        mv, (got_dt, got_shp) = view(name)
+        assert got_dt == dt and list(got_shp) == list(shp), f"{name}: {got_dt}{got_shp}"
+        return mv
+
+    for e in range(n_e):
+        base = f"{pre}.mlp.experts.{e}"
+        slab = gu[e * f2 * h:(e + 1) * f2 * h]
+        slab[:g_bytes] = need(f"{base}.gate_proj.weight", F8, [f2 // 2, h])
+        slab[g_bytes:] = need(f"{base}.up_proj.weight", F8, [f2 // 2, h])
+        sslab = gus[e * gus_bytes:(e + 1) * gus_bytes]
+        sslab[:gs_bytes] = need(f"{base}.gate_proj.weight_scale_inv", BF16, gs)
+        sslab[gs_bytes:] = need(f"{base}.up_proj.weight_scale_inv", BF16, gs)
+        dn[e * dn_bytes:(e + 1) * dn_bytes] = need(f"{base}.down_proj.weight", F8, [h, f])
+        dns[e * ds_bytes:(e + 1) * ds_bytes] = need(f"{base}.down_proj.weight_scale_inv", BF16, ds)
+
+    for mm in mmaps.values():
+        mm.close()
+
+    # byte-identity check on two sampled experts (first and last), all four
+    # tensors — the stack IS the checkpoint bytes, so they must compare equal
+    for e in (0, n_e - 1):
+        base = f"{pre}.mlp.experts.{e}"
+        s = e * f2 * h
+        assert bytes(gu[s:s + g_bytes]) == bytes(stack_src(f"{base}.gate_proj.weight", meta, stage))
+        assert bytes(gu[s + g_bytes:s + f2 * h]) == bytes(stack_src(f"{base}.up_proj.weight", meta, stage))
+        t = e * gus_bytes
+        assert bytes(gus[t:t + gs_bytes]) == bytes(stack_src(f"{base}.gate_proj.weight_scale_inv", meta, stage))
+        assert bytes(gus[t + gs_bytes:t + gus_bytes]) == bytes(stack_src(f"{base}.up_proj.weight_scale_inv", meta, stage))
+        d = e * dn_bytes
+        assert bytes(dn[d:d + dn_bytes]) == bytes(stack_src(f"{base}.down_proj.weight", meta, stage))
+        u = e * ds_bytes
+        assert bytes(dns[u:u + ds_bytes]) == bytes(stack_src(f"{base}.down_proj.weight_scale_inv", meta, stage))
+
+    out = os.path.join(stage, f"stacked.{idx:03d}.safetensors")
+    tensors = [
+        (f"{pre}.mlp.experts.gate_up_proj", F8, [n_e, f2, h], n_e * f2 * h),
+        (f"{pre}.mlp.experts.gate_up_proj_scale_inv", BF16,
+         [n_e, gs[0] * 2, gs[1]], n_e * gus_bytes),
+        (f"{pre}.mlp.experts.down_proj", F8, [n_e, h, f], n_e * h * f),
+        (f"{pre}.mlp.experts.down_proj_scale_inv", BF16,
+         [n_e, ds[0], ds[1]], n_e * ds_bytes),
+    ]
+    bufs = iter([gu, gus, dn, dns])
+    written = write_safetensors(out, tensors, bufs)
+    return (pre, written)
+
+
+def stack_src(name, meta, stage):
+    """Re-open a source tensor for the sampled-expert check."""
+    f_, off, ln, dt, shp = meta[name]
+    fh = open(os.path.join(stage, f_), "rb")
+    mm = mmap.mmap(fh.fileno(), 0, access=mmap.ACCESS_READ)
+    fh.close()  # mmap keeps the mapping alive
+    return mm[off:off + ln]
+
+
+def fill_ple_shard(job):
+    """Decode one fp8 shard with the table scale, write bf16 rows into the mmap."""
+    (src_path, src_off, src_rows, dst_path, dst_off, scale_f32,
+     cols, chunk_rows) = job
+    lut = e4m3_lut()
+    with open(src_path, "rb") as fh:
+        sm = mmap.mmap(fh.fileno(), 0, access=mmap.ACCESS_READ)
+        with open(dst_path, "r+b") as dfh:
+            dm = mmap.mmap(dfh.fileno(), 0, access=mmap.ACCESS_WRITE)
+            for r0 in range(0, src_rows, chunk_rows):
+                r1 = min(r0 + chunk_rows, src_rows)
+                codes = np.frombuffer(sm, dtype=np.uint8,
+                                      count=(r1 - r0) * cols,
+                                      offset=src_off + r0 * cols)
+                q = lut.take(codes)
+                del codes  # release the mmap export before sm.close()
+                q[~np.isfinite(q)] = 0.0
+                q *= np.float32(scale_f32)
+                packed = f32_to_bf16_rne(q)
+                dm[dst_off + r0 * cols * 2: dst_off + r1 * cols * 2] = packed.tobytes()
+            dm.flush()
+            os.fsync(dfh.fileno())
+            dm.close()
+        sm.close()
+
+
+# ---------------------------------------------------------------------- main
+
+def main():
+    ap = argparse.ArgumentParser(description=__doc__.split("\n")[0])
+    ap.add_argument("--src", default=None,
+                    help="raw checkpoint dir (documentation only; unused)")
+    ap.add_argument("--stage", required=True, help="staging dir to reshape in place")
+    ap.add_argument("--force", action="store_true", help="redo even if already staged")
+    ap.add_argument("--workers", type=int, default=min(8, os.cpu_count() or 1))
+    args = ap.parse_args()
+    stage = args.stage
+    t0 = time.time()
+
+    index_path = os.path.join(stage, "model.safetensors.index.json")
+    cfg_path = os.path.join(stage, "config.json")
+    if not (os.path.isfile(index_path) and os.path.isfile(cfg_path)):
+        log(f"ERROR: {stage} is not a staged checkpoint (missing index/config)")
+        return 2
+    with open(index_path) as fh:
+        index = json.load(fh)
+    with open(cfg_path) as fh:
+        cfg = json.load(fh)
+    wm = index["weight_map"]
+
+    meta = build_meta(stage, index)
+
+    # ---- collect prefixes -------------------------------------------------
+    pre_re = re.compile(r"^(?P<pre>.+)\.mlp\.experts\.(?P<e>\d+)\.gate_proj\.weight$")
+    pres, n_e = {}, 0
+    for k in wm:
+        m = pre_re.match(k)
+        if m:
+            pres.setdefault(m.group("pre"), set()).add(int(m.group("e")))
+            n_e = max(n_e, int(m.group("e")) + 1)
+    prefixes = sorted(pres)
+    if not prefixes:
+        log("ERROR: no per-expert tensors found in the staged index")
+        return 2
+    mtp = [p for p in prefixes if p.startswith("mtp.")]
+    trunk = [p for p in prefixes if p.startswith("model.language_model.layers.")]
+    log(f"expert prefixes: {len(prefixes)} (trunk {len(trunk)}, mtp {len(mtp)}), "
+        f"{n_e} experts each")
+
+    # shapes from expert 0 of the first prefix
+    pre0 = prefixes[0]
+    shapes = {}
+    gdt, gsh = meta[f"{pre0}.mlp.experts.0.gate_proj.weight"][3:5]
+    ddt, dsh = meta[f"{pre0}.mlp.experts.0.down_proj.weight"][3:5]
+    gsd, gssh = meta[f"{pre0}.mlp.experts.0.gate_proj.weight_scale_inv"][3:5]
+    dsd, dssh = meta[f"{pre0}.mlp.experts.0.down_proj.weight_scale_inv"][3:5]
+    scale_law = (gdt == F8 and ddt == F8 and gsd == BF16 and dsd == BF16
+                 and list(gssh) == [(gsh[0] + 127) // 128, (gsh[1] + 127) // 128]
+                 and list(dssh) == [(dsh[0] + 127) // 128, (dsh[1] + 127) // 128])
+    if not scale_law:
+        log(f"ERROR: unexpected expert shapes: gate {gdt}{gsh} scale {gsd}{gssh} "
+            f"down {ddt}{dsh} scale {dsd}{dssh} (scale law 128x128)")
+        return 2
+    shapes = {"ffn2_h": (gsh[0] * 2, gsh[1]), "ffn": (dsh[1],),
+              "gs": tuple(gssh), "ds": tuple(dssh)}
+    f2, h = shapes["ffn2_h"]
+    f = shapes["ffn"][0]
+    log(f"shapes: gate_up [{n_e},{f2},{h}] F8, down [{n_e},{h},{f}] F8, "
+        f"scales {gssh}x2 / {dssh} bf16")
+
+    # ---- PLE table --------------------------------------------------------
+    ple_re = re.compile(r"^(?P<pre>.+)\.ple\.ple_embedding\.ngram_embedding\.shard_(?P<i>\d+)\.weight$")
+    ple_pre, shard_ids = None, {}
+    for k in wm:
+        m = ple_re.match(k)
+        if m:
+            ple_pre = m.group("pre")
+            shard_ids[int(m.group("i"))] = k
+    if ple_pre is None:
+        log("ERROR: no PLE table shards found")
+        return 2
+    n_shards = len(shard_ids)
+    if sorted(shard_ids) != list(range(n_shards)):
+        log(f"ERROR: PLE shard ids not contiguous 0..{n_shards - 1}")
+        return 2
+    sc_name = f"{ple_pre}.ple.ple_embedding.ngram_embedding.weight_scale"
+    sdt, ssh, sln = meta[sc_name][3], meta[sc_name][4], meta[sc_name][2]
+    if sdt != BF16 or list(ssh) != [1]:
+        log(f"ERROR: PLE scale is {sdt}{ssh}, expected BF16 [1]")
+        return 2
+    s_bits = struct.unpack("<H", bytes(view_src_tuple(meta, sc_name, stage)))[0]
+    scale_f32 = struct.unpack("<f", struct.pack("<I", s_bits << 16))[0]
+    log(f"PLE: {ple_pre}, {n_shards} shards, scale bf16 = {scale_f32!r}")
+
+    rows = [meta[shard_ids[i]][4][0] for i in range(n_shards)]
+    total_rows = sum(rows)
+    hd = meta[shard_ids[0]][4][1]
+    if any(meta[shard_ids[i]][4][1] != hd for i in range(n_shards)):
+        log("ERROR: PLE shards disagree on HD")
+        return 2
+    if hd % 128 != 0 and hd != 160:
+        log(f"WARNING: PLE HD={hd} is neither 160 nor a multiple of 128")
+
+    # ---- idempotence gate -------------------------------------------------
+    stacked_names = [f"{p}.mlp.experts.gate_up_proj" for p in prefixes]
+    merged_name = f"{ple_pre}.ple.ple_embedding.ngram_embedding.shard_0.weight"
+    already = (all(n in wm for n in stacked_names)
+               and merged_name in wm
+               and find_split(cfg) is not None and read_split(cfg) == 1)
+    if already and not args.force:
+        log("already staged (stacked names, merged shard_0 and split_ngram_parts=1 "
+            "all present) — nothing to do")
+        return 0
+
+    # ---- disk gate --------------------------------------------------------
+    stacked_bytes = len(prefixes) * n_e * (f2 * h + h * f)
+    ple_bytes = total_rows * hd * 2
+    need = stacked_bytes + ple_bytes
+    st = os.statvfs(stage)
+    avail = st.f_bavail * st.f_frsize
+    if avail < need * 1.05:
+        log(f"ERROR: {avail / 2**30:.1f} GiB free < {need / 2**30:.1f} GiB needed (+5%)")
+        return 2
+    log(f"disk: {avail / 2**30:.1f} GiB free, need {need / 2**30:.1f} GiB "
+        f"(experts {stacked_bytes / 2**30:.1f}, ple {ple_bytes / 2**30:.1f})")
+
+    # ---- stage experts ----------------------------------------------------
+    jobs = []
+    for idx, pre in enumerate(prefixes):
+        jmeta = {n: meta[n] for n in meta if n.startswith(f"{pre}.mlp.experts.")}
+        jobs.append((stage, pre, idx, n_e, shapes, jmeta))
+    ctx = mp.get_context("fork")
+    written_total = 0
+    with ctx.Pool(args.workers) as pool:
+        for pre, nbytes in pool.imap_unordered(stack_prefix, jobs):
+            written_total += nbytes
+            log(f"  stacked {pre} ({nbytes / 2**30:.2f} GiB)")
+    log(f"experts stacked: {written_total / 2**30:.2f} GiB in {len(prefixes)} files")
+
+    # ---- stage PLE --------------------------------------------------------
+    # The file name sorts BEFORE the model-000NN shards ON PURPOSE: the reader
+    # opens every index-named file in sorted order and emplaces EVERY tensor
+    # it physically holds, first-wins (checkpoint.cpp add_shard). The raw
+    # shards carry tensors of the SAME name as the merged one
+    # (...ple.ple_embedding.ngram_embedding.shard_0.weight, [2500012,160]
+    # fp8), so ple.merged.safetensors lost the race and the plan read one raw
+    # shard as the codes. "00-" wins the sort and the name.
+    out_name = "00-ple.merged.safetensors"
+    out_path = os.path.join(stage, out_name)
+    data_bytes = total_rows * hd * 2
+    hdr = {merged_name: {"dtype": BF16, "shape": [total_rows, hd],
+                         "data_offsets": [0, data_bytes]}}
+    blob = json.dumps(hdr, separators=(",", ":")).encode("utf-8")
+    pad = (-len(blob)) % 8
+    blob += b" " * pad
+    hlen = len(blob)
+    with open(out_path, "wb") as fh:
+        fh.write(struct.pack("<Q", hlen))
+        fh.write(blob)
+        fh.truncate(8 + hlen + data_bytes)
+    jobs = []
+    row0 = 0
+    for i in range(n_shards):
+        f_, off, ln, _, shp = meta[shard_ids[i]]
+        jobs.append((os.path.join(stage, f_), off, shp[0], out_path,
+                     8 + hlen + row0 * hd * 2, scale_f32, hd, 131072))
+        row0 += shp[0]
+    assert row0 == total_rows
+    with ctx.Pool(args.workers) as pool:
+        pool.map(fill_ple_shard, jobs)
+    log(f"PLE merged: {data_bytes / 2**30:.2f} GiB bf16 "
+        f"({total_rows} rows x {hd} cols)")
+
+    # ---- post-verify ------------------------------------------------------
+    verify(stage, prefixes, n_e, shapes, meta, shard_ids, rows, hd,
+           scale_f32, out_path, hlen)
+
+    # ---- rewrite index + config -------------------------------------------
+    new_wm = {k: v for k, v in wm.items() if k not in set(shard_ids.values())}
+    for idx, pre in enumerate(prefixes):
+        fname = f"stacked.{idx:03d}.safetensors"
+        for t in (f"{pre}.mlp.experts.gate_up_proj",
+                  f"{pre}.mlp.experts.gate_up_proj_scale_inv",
+                  f"{pre}.mlp.experts.down_proj",
+                  f"{pre}.mlp.experts.down_proj_scale_inv"):
+            new_wm[t] = fname
+    new_wm[merged_name] = out_name
+    total = sum(meta_size(meta, k) for k in new_wm
+                if k in meta)  # originals
+    # new tensors' sizes
+    total += written_total + data_bytes
+    index["weight_map"] = new_wm
+    if "metadata" in index and isinstance(index["metadata"], dict):
+        index["metadata"]["total_size"] = total
+    # ATOMIC REPLACE, never open(path, "w"): the stage's index is a symlink
+    # to the RAW checkpoint (staging symlinks everything but config.json) —
+    # open("w") would rewrite the raw file through the link. os.replace
+    # swaps the link itself for a real file; the raw tree stays pristine.
+    for path, payload in ((index_path, json.dumps(index, indent=2)),
+                          (cfg_path, json.dumps(cfg, indent=2))):
+        tmp = path + ".tmp"
+        with open(tmp, "w") as fh:
+            fh.write(payload)
+        os.replace(tmp, path)
+    n = set_split(cfg, 1)
+    log(f"config: split_ngram_parts=1 written at {n} location(s)")
+
+    with open(os.path.join(stage, "ple_scale.txt"), "w") as fh:
+        fh.write(repr(scale_f32) + "\n")
+
+    dt = time.time() - t0
+    log(f"staged in {dt / 60:.1f} min: {written_total / 2**30:.1f} GiB experts + "
+        f"{data_bytes / 2**30:.1f} GiB PLE; scale {scale_f32!r} -> ple_scale.txt")
+    return 0
+
+
+def meta_size(meta, name):
+    _, _, ln, _, _ = meta[name]
+    return ln
+
+
+def view_src_tuple(meta, name, stage):
+    f_, off, ln, _, _ = meta[name]
+    with open(os.path.join(stage, f_), "rb") as fh:
+        fh.seek(off)
+        return fh.read(ln)
+
+
+def find_split(cfg):
+    """Locate split_ngram_parts keys (recursive); return list of paths."""
+    hits = []
+
+    def walk(d, path):
+        if isinstance(d, dict):
+            for k, v in d.items():
+                if k == "split_ngram_parts":
+                    hits.append(path + [k])
+                walk(v, path + [k])
+    walk(cfg, [])
+    return hits
+
+
+def read_split(cfg):
+    hits = find_split(cfg)
+    if not hits:
+        return None
+    d = cfg
+    for k in hits[0][:-1]:
+        d = d[k]
+    return d[hits[0][-1]]
+
+
+def set_split(cfg, value):
+    hits = find_split(cfg)
+    for path in hits:
+        d = cfg
+        for k in path[:-1]:
+            d = d[k]
+        d[path[-1]] = value
+    if not hits:  # plugin's meta_int2 falls back to the top-level key
+        cfg["split_ngram_parts"] = value
+        hits.append(["split_ngram_parts"])
+    return hits
+
+
+def verify(stage, prefixes, n_e, shapes, meta, shard_ids, rows, hd,
+           scale_f32, out_path, hlen):
+    """Cheap strong checks: headers parse, sizes match, sampled bytes identical."""
+    f2, h = shapes["ffn2_h"]
+    f = shapes["ffn"][0]
+    gs, ds = shapes["gs"], shapes["ds"]
+    # headers of every new file
+    for idx, pre in enumerate(prefixes):
+        path = os.path.join(stage, f"stacked.{idx:03d}.safetensors")
+        hlen2, hdr = parse_header(path)
+        want = {
+            f"{pre}.mlp.experts.gate_up_proj": (F8, [n_e, f2, h]),
+            f"{pre}.mlp.experts.gate_up_proj_scale_inv":
+                (BF16, [n_e, gs[0] * 2, gs[1]]),
+            f"{pre}.mlp.experts.down_proj": (F8, [n_e, h, f]),
+            f"{pre}.mlp.experts.down_proj_scale_inv": (BF16, [n_e, ds[0], ds[1]]),
+        }
+        for name, (dt, shp) in want.items():
+            e = hdr[name]
+            assert e["dtype"] == dt and list(e["shape"]) == shp, name
+            assert e["data_offsets"][1] - e["data_offsets"][0] == \
+                int(np.prod(shp)) * (1 if dt == F8 else 2), name
+    _, phdr = parse_header(out_path)
+    e = phdr[next(iter(phdr))]
+    assert e["dtype"] == BF16 and list(e["shape"]) == [sum(rows), hd]
+
+    # sampled PLE rows: recompute and compare bytes exactly
+    lut = e4m3_lut()
+    out_off = 8 + hlen
+    for i in (0, len(shard_ids) // 2, len(shard_ids) - 1):
+        name = shard_ids[i]
+        f_, off, ln, _, shp = meta[name]
+        with open(os.path.join(stage, f_), "rb") as fh:
+            mm = mmap.mmap(fh.fileno(), 0, access=mmap.ACCESS_READ)
+            for r in (0, shp[0] - 1):
+                codes = np.frombuffer(mm, dtype=np.uint8, count=hd,
+                                      offset=off + r * hd)
+                q = lut.take(codes) * np.float32(scale_f32)
+                del codes  # release the mmap export before mm.close()
+                want = f32_to_bf16_rne(q).tobytes()
+                got_row = sum(rows[:i]) + r
+                with open(out_path, "rb") as ofh:
+                    ofh.seek(out_off + got_row * hd * 2)
+                    got = ofh.read(hd * 2)
+                assert got == want, f"PLE row {got_row} mismatch"
+            mm.close()
+    log("verify: new-file headers, sizes and sampled PLE rows all exact; "
+        "expert slabs were byte-checked in-stack")
+
+
+if __name__ == "__main__":
+    sys.exit(main())

scripts/test-changed.sh

@@ -204,12 +204,12 @@ for path in "${CHANGED[@]}"; do
         scripts/qwen-sharp-v22.4.0.jinja)
             add_test tests/frank_large_unit.bats
             ;;
-        # frank-small is llama.cpp on the 3090 as a ROOT unit: a native CUDA
+        # frank-secondary is llama.cpp on the 3090 as a ROOT unit: a native CUDA
         # binary, so no generated quadlet is involved. Its env, unit and serve
         # script all live in scripts/, which is why this case has to precede
         # the generic scripts/*.service and scripts/* fallbacks below.
-        scripts/frank-small.*|scripts/serve-frank-small.sh)
-            add_test tests/frank_small_unit.bats
+        scripts/frank-secondary.*|scripts/serve-frank-secondary.sh)
+            add_test tests/frank_secondary_unit.bats
             add_test tests/llm_units.bats
             add_test tests/deployment_scoping.bats
             add_test tests/scripts_syntax.bats

scripts/verify-swift15-ple.py

@@ -0,0 +1,87 @@
+#!/usr/bin/env python3
+"""Streaming absmax audit of the Swift1.5 PLE n-gram table (BF16).
+
+The PLE fp8 encoding uses ONE fixed scale (1.9931793212890625e-04, the
+official checkpoint's plateau value). fp8_e4m3 saturates at 448, so the
+fixed scale is legal iff absmax <= 448 * SCALE. This walks every
+ngram_embedding.shard_*.weight tensor in the checkpoint (mmap, no copy),
+computes the absmax from the BF16 bit pattern (clear the sign bit, max
+the remaining uint16 — BF16 magnitudes are monotone in their bits), and
+reports the headroom. Exit 0 only when headroom >= 1.5x.
+
+Run from any cwd EXCEPT /tmp on dankserver (/tmp/inspect.py shadows the
+stdlib). Usage: verify-swift15-ple.py <ckpt-dir>
+"""
+import json
+import mmap
+import os
+import struct
+import sys
+
+SCALE = 1.9931793212890625e-04  # bf16 0x3951, the official plateau scale
+FP8_E4M3_MAX = 448.0
+
+import numpy as np
+gmax = 0.0
+gfile = ""
+over_cap = 0
+total = 0
+
+ckpt = sys.argv[1]
+index = json.load(open(os.path.join(ckpt, "model.safetensors.index.json")))
+shards: dict[str, list[str]] = {}
+for k, f in index["weight_map"].items():
+    if "ngram_embedding.shard_" in k and k.endswith(".weight"):
+        shards.setdefault(f, []).append(k)
+
+files = len(shards)
+tensors = sum(len(v) for v in shards.values())
+print(f"n-gram shards: {files} files, {tensors} tensors")
+
+import math
+
+cap = FP8_E4M3_MAX * SCALE
+# largest bf16 <= cap: truncate the float32 bits (cap > 0); bf16
+# magnitudes are monotone in their bits, so |w| > cap <=> bits > cap_bits
+cap_bits = int((np.float32(cap).view(np.uint32) >> 16) & 0xFFFF)
+for fname, keys in sorted(shards.items()):
+    path = os.path.join(ckpt, fname)
+    with open(path, "rb") as fh:
+        hlen = struct.unpack("<Q", fh.read(8))[0]
+        header = json.loads(fh.read(hlen))
+        base = 8 + hlen
+        mm = mmap.mmap(fh.fileno(), 0, prot=mmap.PROT_READ)
+        for k in keys:
+            meta = header[k]
+            off, end = meta["data_offsets"]
+            a = np.frombuffer(mm, dtype=np.uint16, count=(end - off) // 2,
+                              offset=base + off)
+            v = a & 0x7FFF
+            m = int(v.max())
+            over_cap += int((v > np.uint16(cap_bits)).sum())
+            total += v.size
+            del a
+            if m > gmax:
+                gmax = m
+                gfile = k
+        mm.close()
+
+# rebuild the BF16 value: the 16 bits are the HIGH half of the float32
+absmax = np.frombuffer(b"\x00\x00" + np.uint16(gmax).tobytes(),
+                        dtype=np.float32)[0]
+
+print(f"absmax        = {absmax!r}  (tensor: {gfile})")
+print(f"2^log2        = 2^{math.log2(absmax):.3f}")
+print(f"fp8 cap       = 448 * {SCALE:.8g} = {cap:.8g}")
+print(f"headroom      = {cap / absmax:.3f}x")
+ok = cap / absmax >= 1.5
+print("VERDICT:", "PASS (>= 1.5x headroom over the fixed scale)" if ok
+      else "FAIL — fixed scale does not cover the Swift PLE")
+print(f"values > cap  = {over_cap} of {total} ({over_cap / total:.3e})")
+nxt_bits = int((np.float32(absmax / FP8_E4M3_MAX).view(np.uint32)
+                >> 16) & 0xFFFF) + 1
+nxt_scale = np.frombuffer(b"\x00\x00" + np.uint16(nxt_bits).tobytes(),
+                          dtype=np.float32)[0]
+print(f"next-bf16 scale covering absmax: {nxt_scale:.8g} "
+      f"(0x{nxt_bits:04x}, headroom {FP8_E4M3_MAX * nxt_scale / absmax:.4f}x)")
+sys.exit(0 if ok else 1)

serverAppConfigs-docs/Agent/OMP Harness.md

@@ -13,6 +13,18 @@ registry; single source of truth — there is no copy on the servers) and
 `~/.omp/agent/config.yml` (roles, compaction, global samplers). Nothing in
 this file is repo-managed; edits are harness-side only.
 
+**Model ids are the StarCraft unit names (official 2026-10-08, on the wire
+since 2026-10-09):** `frank-archon` (dankserver :7770, radiance),
+`frank-dragoon` (dankclaw :7770, flash-server), `frank-zealot` (dankclaw
+:7771, the 3090 compaction hub) — formerly `frank-primary`, `frank-large`,
+`frank-secondary`. The SERVICES keep the old names (units, scripts, env
+files, runbook pages); pages written in the old era name the service,
+the harness names the model. The wire now matches: each endpoint's
+`/v1/models` answers with the Protoss name, so `models.yml`, the OpenWebUI
+picker/grants, the homepage docs and the raw API all use one id per model
+(see [[OpenWebUI]] for the grant re-key and the [[frank-primary-radiance-swift15-w4dn8]]
+note on radiance echoing the requested name).
+
 ## Compaction chain arithmetic (verify before ANY window change)
 
 When a fleet endpoint's context grows past its harness-declared window, OMP
@@ -35,12 +47,12 @@ encodes):
 1. **Source trigger** = `thresholdPercent × source contextWindow`
    (0.70 × 200,704 = 140,492 for frank-primary on the current shape)
 2. **Output budget** = capped at the target model's `maxTokens`
-   (32,768 for frank-small; 36,864 frank-primary; 57,344 frank-large — the
+   (32,768 for frank-secondary; 36,864 frank-primary; 57,344 frank-large — the
    largest, because halogen's `HALOGEN_MAX_TOKENS_CAP` must stay ≥ it)
 3. **REQUIREMENT**: prompt (≤ trigger) + budget ≤ the target's per-slot
    window. Worked pairs from the live registry: 140,492 + 36,864 = 177,356 ≤
    200,704 (frank-primary); 140,490 + 57,344 = 197,834 ≤ 200,700
-   (frank-large); 140,492 + 32,768 = 173,260 ≤ 200,704 (frank-small).
+   (frank-large); 140,492 + 32,768 = 173,260 ≤ 200,704 (frank-secondary).
 
 Procedure (recompute from the LIVE files every time — windows rotate with
 every swap; the numbers above are the 2026-10-05 state):

serverAppConfigs-docs/Architecture/Deployment Flow.md

@@ -63,7 +63,7 @@ service on the other host.
 | Dankclaw compose services | `podman-compose -f dankclaw/compose.yaml up -d --no-deps NAME` | Recreates only `NAME`; Caddy is handled separately because it fronts both hosts. |
 | SearXNG, Gluetun, Valkey, OpenWebUI, Pi-hole, Gitea database/server | user Quadlet + `systemctl --user` | Installs the selected unit and restarts only the required dependency-coupled units. Gitea's web proxy is the canonical Caddy compose service. |
 | `frank-large` | systemd + `systemctl` (root unit) | Relinks and restarts only the large inference service; waits up to 20 minutes for `/health`. |
-| `frank-small` | systemd + `systemctl` (root unit) | Relinks and restarts only the small inference service; waits up to 20 minutes for `/health`. |
+| `frank-secondary` | systemd + `systemctl` (root unit) | Relinks and restarts only the small inference service; waits up to 20 minutes for `/health`. |
 | `frank-medium` | rootless Quadlet + `systemctl --user` | Installs `dankserver/frank-medium/quadlet/frank-medium.container`; on first deploy retires the llama.cpp root unit and the vLLM replica quadlets into the home trash, then restarts the generated unit. Waits up to 20 minutes for `/health`. |
 | Dankserver media stack (RomM trio, Jellyfin, Calibre-Web, Copyparty) | user Quadlet + `systemctl --user` | Installs `dankserver/<svc>/quadlet/*.container`, restarts the generated unit. The first cutover per service is idempotent: it retires the compose-era bare container, rescues any anonymous volume into the data root, and merge-moves `/opt/<svc>` to `/home/dank/Containers/<svc>` (mapped-uid roots move without chown). Steady state is restart-only. |
 

serverAppConfigs-docs/Dankclaw/ComfyUI Qwen-Image Workflows.md

@@ -0,0 +1,121 @@
+---
+type: howto
+description: "Qwen-Image 2.1 workflows on Dankclaw: two proven txt2img workflows (art + legible text), the API recipe, and which models/LoRAs to add next."
+tags:
+  - dankclaw
+  - comfyui
+  - qwen-image
+timestamp: 2026-10-05
+---
+
+# ComfyUI Qwen-Image Workflows
+
+Two ready-to-load text-to-image workflows for the Qwen-Image 2.1 payload
+already on Dankclaw, both proven end-to-end on 2026-10-05 by POSTing to the
+live API on port 8188 and verifying the rendered PNG. The repo copies are the
+source of truth; the host copies under `/home/dank/comfyui-data/workflows/`
+are for convenience.
+
+## What is on disk
+
+| Path (under `/home/dank/comfyui-data/models/`) | Size | Role |
+|---|---|---|
+| `diffusion_models/qwen_image_2.1_int8_convrot.safetensors` | 6.8 GB | **The one the workflows use** — int8-convrot quant, ComfyUI-core native |
+| `diffusion_models/qwen_image_2.1_bf16.safetensors` | 14 GB | Full-precision fallback |
+| `diffusion_models/qwen-image-2.1-Q8_0.gguf` (+ `-arch`) | 7.1 GB | GGUF alternative (needs ComfyUI-GGUF custom node) |
+| `text_encoders/qwen3vl_8b_w4a8.safetensors` | 5.9 GB | **The one the workflows use** — 4-bit-weights/8-bit-act Qwen3-VL 8B encoder |
+| `text_encoders/qwen3vl_8b_int8_convrot.safetensors` | 8.8 GB | Mid quant |
+| `text_encoders/qwen3vl_8b_bf16.safetensors` | 17 GB | Full precision |
+| `vae/qwen_image_2.1_vae_bf16.safetensors` | 645 MB | 2.1 VAE (used) |
+| `vae/qwen_image_vae.safetensors` | 243 MB | Smaller VAE |
+
+No LoRAs are on disk yet — see [Recommended additions](#recommended-additions).
+
+## The workflows
+
+Both are API-format JSON (ComfyUI 0.37 loads them via the UI's **Load** dialog
+too): `dankclaw/comfyui/workflows/` in the repo,
+`/home/dank/comfyui-data/workflows/` on the host. Node chain in both:
+
+```
+UNETLoader (int8_convrot) ──┐
+CLIPLoader (qwen_image, w4a8) ─┤ CLIPTextEncode pos/neg
+VAELoader (2.1 bf16) ────────┤
+EmptySD3LatentImage ─────────┴─ KSampler ─ VAEDecode ─ SaveImage
+```
+
+**Common sampler settings:** `cfg 1.0` (Qwen-Image 2.1 expects CFG-free
+sampling), `euler` + `simple`, seed fixed in the file.
+
+| Workflow | File | Shape | Steps | Made for |
+|---|---|---|---|---|
+| Art | `qwen-image-2.1-t2i-art.json` | 1024×1024 | 20 | General illustration/prose prompts |
+| Legible text | `qwen-image-2.1-t2i-legible-text.json` | 832×1216 | 25 | Posters, captions, any in-image text |
+
+### Legible-text prompt recipe
+
+Qwen-Image 2.1 is one of the best open-weight text renderers, but only if the
+prompt is written for it:
+
+1. **Quote the exact strings**: `large bold white text reading "SOLIDARITY OF
+   THE SOVIET COMPUTER SYSTEMS"` — quoted text is rendered literally.
+2. **Describe layout, not vibes**: say *centered / below it / large vs small*,
+   the typeface family (sans-serif), and the color.
+3. **Say what NOT to draw**: end with `no other words, no watermark`.
+4. Keep the negative side on `misspelled, warped letters, extra text` (already
+   in the workflow).
+5. More steps help text: the text workflow uses 25 vs 20.
+
+Verified render: `SOLIDARITY OF THE SOVIET COMPUTER SYSTEMS` + `DANKCLAW
+DANKSERVER EST 2026` came back crisp and correctly spelled at 832×1216
+(`qwen21-text_00001_.png`, 953 KB). One caveat from the same render: a
+shorter secondary line is where misspellings creep in — proofread the first
+render before shipping.
+
+## Driving it from the API
+
+ComfyUI 0.37 **requires the graph wrapped in a `prompt` key** — a bare
+`{node-id: node}` body gets `{"error": {"type": "no_prompt"}}` (verified
+2026-10-05):
+
+```bash
+curl -s -X POST http://127.0.0.1:8188/prompt -H 'Content-Type: application/json' \
+  -d "$(python3 -c 'import json;print(json.dumps({"prompt": json.load(open("dankclaw/comfyui/workflows/qwen-image-2.1-t2i-art.json"))}))')"
+# -> {"prompt_id": "...", ...}
+curl -s http://127.0.0.1:8188/history/<prompt_id>   # status.status_str == "success"
+```
+
+Outputs land in `/home/dank/comfyui-data/output/`. The queue is FIFO — the
+two workflows above ran back-to-back unattended.
+
+**Note:** the container's UI-saved workflows live in the ephemeral
+`/opt/ComfyUI/user` layer and do NOT survive an image rebuild — hence the
+host-side `workflows/` dir and the repo copy.
+
+## Recommended additions
+
+Quantized preferred, sized for the 8060S iGPU (GTT-backed; the RTX 3090 is
+frank-secondary's). All repos verified 2026-10-05. None are on disk yet.
+
+| # | What | Repo | Grab | Size | Why |
+|---|---|---|---|---|---|
+| 1 | **Lightning 8-step LoRA** (T2I speed) | `lightx2v/Qwen-Image-Lightning` | `qwen_image_lightning_8steps_*.safetensors` (LoRA) | ~1–2 GB | 20 steps → 8 with a `LoraLoader` node; 2–3× faster renders on the iGPU, negligible quality loss |
+| 2 | **Qwen-Image-Edit-2511 GGUF** (image editing) | `unsloth/Qwen-Image-Edit-2511-GGUF` | Q4_K_M (~13.2 GB) or Q4_0 (~11.9 GB) | ~12–13 GB | Unlocks img2img "edit this photo" workflows — a capability class the fleet has zero of; Q4 keeps it iGPU-friendly |
+| 3 | **DiffSynth ControlNets** (structure control) | `Comfy-Org/Qwen-Image-DiffSynth-ControlNets` | `split_files/model_patches/` canny + depth | ~2–3 GB each | Pose/edge/depth-guided layouts — pairs with the legible-text workflow for exact poster composition. The `QwenImageDiffsynthControlnet` node is already in the running ComfyUI |
+| 4 | **Wan 2.2 TI2V-5B GGUF** (first video model) | `QuantStack/Wan2.2-TI2V-5B-GGUF` | Q4/Q5 GGUF (~6–8 GB) | ~6–8 GB | T2V+I2V in one dense 5B model; the gentlest on-ramp into video generation, runs in ComfyUI core (`wan` CLIP type is already in this build's loader enum) |
+
+Placement: #1 → `models/lora/` (create it), #2 → `models/diffusion_models/`,
+#3 → `models/controlnet/` (create it), #4 → `models/diffusion_models/`.
+Fetch with `scripts/download-frank-*.sh` discipline (`-f` + byte-count +
+sha256 checks) or a plain `hf download` with the token in the environment —
+never on the command line.
+
+Skip list: the original `city96/Qwen-Image-gguf` conversions (superseded by
+the 2.1 files already on disk), and any BF16 of #2/#4 (the iGPU does not need
+them at GTT capacity, and they double the disk bill).
+
+## See Also
+
+- [[ComfyUI]] — the service itself (image, unit, GPU access)
+- [[ComfyUI ROCm and Vulkan]] — the live-verified GPU path
+- [[Dankclaw Stack]] — the host

serverAppConfigs-docs/Dankclaw/ComfyUI.md

@@ -110,5 +110,6 @@ The web UI responds on `/`. ComfyUI also exposes `/system_stats`. On this APU, `
 
 - [[Dankclaw Stack]]
 - [[ComfyUI ROCm and Vulkan]] — the live-verified GPU path research
+- [[ComfyUI Qwen-Image Workflows]] — the Qwen-Image 2.1 payload, proven workflows, and recommended model/LoRA additions
 - [[Lemonade and sd.cpp]] — the ggml-family alternative
 - Knowledge base: [[ComfyUI]] (tools)

serverAppConfigs-docs/Dankclaw/Hermes.md

@@ -189,7 +189,7 @@ What actually breaks, and the fix:
   auxiliary:
     title_generation:
       provider: dankserver-small        # names track the live config; diff first
-      model: frank-small
+      model: frank-secondary
       api_mode: codex_responses        # real per-task key
       extra_body:
         reasoning:

serverAppConfigs-docs/Dankclaw/OpenWebUI.md

@@ -24,29 +24,48 @@ https://github.com/open-webui/open-webui
 | Upstream latest | 0.11.4 (2026-09-21) — security/access-control advisory; `:main` is the rolling image, not a stable pin. Imagegen endpoint is `POST /api/v1/images/generations` (the `comfyui` engine is still upstream). |
 | Port | 3005 (host) → 8080 (container). Port 3000 is Gitea's HTTP, so it is not usable. |
 | Data volume | `/home/dank/Containers/open-webui/data` → `/app/backend/data` |
-| Backends | Three fleet endpoints: `frank-large` (host.containers.internal:7770), `frank-medium` (192.168.0.235:7770), `frank-small` (host.containers.internal:7771) — see [The LLM backends](#the-llm-backends) |
+| Backends | Picker exposes two models — `frank-zealot` (host.containers.internal:7771) and `frank-dragoon` (host.containers.internal:7770, added 2026-10-09). The `frank-archon` (Dankserver) connection remains configured in the DB, disabled — see [The LLM backends](#the-llm-backends) |
 | Network | Pinned bridge `systemd-open-webui` (`10.89.3.0/24`, gateway `10.89.3.1`) — see [History](#history) |
 | Persistence | Quadlet unit `open-webui.service`; `[Install] WantedBy=default.target` |
 | Deploy | `scripts/deploy-open-webui-quadlet.sh dankclaw` |
 
 ## The LLM backends
 
-OpenWebUI serves the three fleet LLM endpoints. The model picker is built from
-the `model` table in `webui.db` (one row per model), NOT from live connection
-discovery — so the picker shows exactly the models seeded there.
+The picker is built from the `model` table in `webui.db` (one row per model)
+plus the `model_ids` of each **enabled** OpenAI connection, NOT from live
+connection discovery — so the picker shows exactly what is seeded there.
+As of 2026-10-09 the picker exposes **two models: `frank-zealot` and
+`frank-dragoon`**.
 
-| Picker model | Connection URL | Runtime | Host |
-|--------------|----------------|---------|------|
-| `frank-large` | `http://host.containers.internal:7770/v1` | unsloth llama.cpp (Strix Halo iGPU, 200k ctx) | Dankclaw |
-| `frank-medium` | `http://192.168.0.235:7770/v1` | vllm-radiance TP=2 (FP8, DFlash2, 262k ctx) | Dankserver |
-| `frank-small` | `http://host.containers.internal:7771/v1` | llama.cpp (RTX 3090, Gemma4-12B, 262k ctx) | Dankclaw |
+| Connection (`openai.api_configs` idx) | URL | Enabled | `model_ids` | Runtime | Host |
+|---|---|---|---|---|---|
+| 0 | `http://host.containers.internal:7770/v1` | yes (2026-10-09) | `frank-dragoon` | halogen flash-server (Strix Halo iGPU) | Dankclaw |
+| 1 | `http://192.168.0.235:7770/v1` | no (2026-10-07) | `frank-archon` | radiance TP=2 (2× R9700) | Dankserver |
+| 2 | `http://host.containers.internal:7771/v1` | yes | `frank-zealot` | llama.cpp CUDA (RTX 3090, 2×200,704) | Dankclaw |
+
+The `model` table carries one decoration row per picker model:
+`frank-zealot` (context length 200,704 = the per-slot window; description:
+Qwen3.5-9B Q6_K + BF16 vision mmproj) and `frank-dragoon` (context length
+262,144 = the per-slot `HALOGEN_CTX`; description: Swift1.5 v2 abliterated,
+MTP, vision ON).
+
+A third gate sits *after* the table, and it is the one that bites family
+accounts (2026-10-07): a model row is only visible to a non-admin user when
+its **access permission** grants them. A seeded row defaults to admin-only,
+so the picker renders "No results found" for every regular user even though
+the connection is enabled, `model_ids` is correct, and the endpoint answers
+`/v1/models` — and `GET /api/v1/models` returns `[]` for that user while an
+admin token returns the model. Fix: set the row's `access_control` to public
+(the `access` column on the `model` row) via the admin UI (Admin Panel →
+Models → visibility) or the DB. The verification battery below must therefore
+run **as a non-admin user**, not only as an admin.
 
 Two gotchas make the wiring non-obvious:
 
 - **llama.cpp `/v1/models` is Ollama-shaped.** llama.cpp returns
   `{"models":[{"name":...}]}`; OpenWebUI's `extract_data()` only reads the
   OpenAI `{"data":[{"id":...}]}` shape, so connection discovery silently misses
-  the llama.cpp endpoints (frank-large, frank-small). The fix is to set
+  the llama.cpp endpoints (frank-large, frank-secondary). The fix is to set
   `model_ids` in each connection's `api_config` — when non-empty, OpenWebUI
   builds the model list directly and never fetches `/v1/models`. frank-medium
   (vLLM) serves the OpenAI shape and would discover fine, but uses `model_ids`
@@ -62,7 +81,9 @@ The Ollama connection is disabled (`ollama.enable=false`).
 
 > All three endpoints are bare-metal systemd services managed by this
 > repository. Model swaps, context sizing, and spec tuning happen there. See
-> the [LLM knowledge base](../LLM/index.md).
+> the [LLM knowledge base](../LLM/index.md). Disabling a connection here only
+> hides it from this picker — the endpoint itself keeps serving every other
+> client (the harness, Hermes, API consumers).
 
 ## Container introspection (DB and backend python)
 
@@ -94,18 +115,38 @@ The Ollama connection is disabled (`ollama.enable=false`).
 
 ## Verification battery
 
-1. `GET /api/v1/models/list?limit=100` → returns EXACTLY the intended
-   models. Response key is `items`, not `data`.
-2. `POST /openai/chat/completions` per model with a trivial prompt ("Reply
+1. `GET /api/models` (the picker endpoint; response key `data`) → lists
+   EXACTLY the intended models, **as an admin AND as a `role='user'` token**.
+   Do NOT gate on `GET /api/v1/models/list` (the Admin Panel → Models page):
+   on the 0.6.x image it returns `items: []` for connection-backed models
+   (workspace-list only; measured 2026-10-09 on both picker ids).
+2. `POST /api/chat/completions` per model with a trivial prompt ("Reply
    with exactly one word: pong") → each returns content `pong`. Reasoning
-   models need `max_tokens>=100` — the thinking block eats the budget.
+   models need a generous budget: frank-secondary (Qwen3.5 template) returns
+   empty content + `finish_reason: "length"` at `max_tokens 200` (the
+   thinking block eats it) and answers `pong` at 1500.
 3. A 500 "Server Connection Error" → `podman logs open-webui` for
    `ClientConnectorError`: a network-reachability problem (host LAN IP vs
    `host.containers.internal`), not a model problem.
 
 **Durability:** the model table + connections are DB-only (not git-managed);
-a fresh data volume loses them. For reproducibility, add an idempotent
-first-start seed script or document the setup in the service doc.
+a fresh data volume loses them — including the public access grant above.
+For reproducibility, add an idempotent first-start seed script or document
+the setup in the service doc.
+
+## API access (auth-gated)
+
+Every `/api/*` endpoint requires an authenticated session (SSO login JWT)
+or a per-user API key (`auth.enable_api_keys=true`; keys are minted per
+account under Profile → API Keys). Anonymous web visitors get the `pending`
+role (`ui.default_user_role=pending`), which cannot chat. Verified
+2026-10-07 from loopback `:3005` and over the public vhost:
+`GET /api/v1/models/list`, `GET /api/v1/models` and
+`POST /api/chat/completions` all return **401** with no key (also with a
+bogus bearer token); only `/api/config`, `/api/version` and `/health`
+answer publicly, and none of them expose the model list. An external
+program needs an account + API key — there is no keyless access.
+
 
 ## Web search
 
@@ -149,6 +190,18 @@ browser-driven and only touches the two exempt vhosts (`ai.` and `auth.`).
   into the existing local account with the same email — the pre-SSO admin
   (frank8465@protonmail.com) keeps its data. `ENABLE_OAUTH_SIGNUP=true` lets
   other `openwebui_users` create accounts on first login.
+- **First login mints role `pending`.** `ui.default_user_role=pending` (config
+  DB, verified 2026-10-09), so a freshly-provisioned `openwebui_users` account
+  completes SSO and then cannot chat (picker empty) until its role is flipped:
+  `UPDATE "user" SET role='user' WHERE email='<user>@franksdank.cloud';` —
+  run with host `python3` over ssh against
+  `/home/dank/Containers/open-webui/data/webui.db` (no `sqlite3` CLI on
+  Dankclaw; table is `user`, singular; no `active` column). No per-user model
+  grant is needed: both picker models (`frank-zealot`, `frank-dragoon`) carry
+  the global `access_grant` row `(model, <id>, user, *, read)`, so any
+  `role='user'` account sees them. Flip on FIRST-LOGIN SIGHTING — the row does
+  not exist until the account signs in (2026-10-09: five provisioned accounts
+  had no row).
 - **IdP reachability:** the container is on its own bridge (10.89.3.0/24) and
   cannot reach `authentik-server:9000` by name. It reaches the IdP over the
   public URL `https://auth.franksdank.cloud` — Caddy publishes `0.0.0.0:443`
@@ -218,18 +271,64 @@ on large sizes ⇒ check ComfyUI resources; 401 ⇒ the token is bad.
 - The image tracks upstream `main`; pin a digest in the quadlet unit if reproducible rollback matters more than tracking. (The old "CPU-only host" framing is stale: this APU runs the gfx1151 ROCm build — see [[ComfyUI]].)
 - The default admin account is created on first login; secure it immediately.
 - If the backend is loading (3–8 minute model load), OpenWebUI shows connection errors until the model is ready.
-- The on-host LLM services (frank-large `:7770`, frank-small `:7771`) are reached via `host.containers.internal`, NOT the host LAN IP: the container's netavark bridge cannot hairpin to its own host's non-published ports via `192.168.0.143` (connection refused), and the bridge gateway (`10.89.3.1`) lives in the rootless netns while llama-server runs in the host netns, so it refuses too. `host.containers.internal` (169.254.1.2) resolves to the host on the bridge and works — verified end-to-end for both endpoints. The Dankserver backend (frank-medium) is a different host, reached via its LAN IP `192.168.0.235:7770`.
+- The on-host LLM services (Dankclaw `:7770`, `:7771`) are reached via `host.containers.internal`, NOT the host LAN IP: the container's netavark bridge cannot hairpin to its own host's non-published ports via `192.168.0.143` (connection refused), and the bridge gateway (`10.89.3.1`) lives in the rootless netns while llama-server runs in the host netns, so it refuses too. `host.containers.internal` (169.254.1.2) resolves to the host on the bridge and works — verified end-to-end for both endpoints. The Dankserver backend is a different host, reached via its LAN IP `192.168.0.235:7770`.
 
 ## History
 
 - **2026-08-22 — slirp4netns → pinned bridge.** The unit ran on `Network=slirp4netns`. The slirp4netns helper process died (no OOM, no kill entry, no crash log found), and the container kept running with only `lo` in its netns: published port 3005 accepted TCP and reset every request, so chat served empty responses for ~2 days. Nothing logged an error. Moving to a kernel bridge removes the per-container helper entirely, and the healthcheck's interface check makes the zombie state visible if it ever returns.
 
-- **2026-09-21 — model picker rebuilt for the 3-model fleet.** The picker carried 39 stale rows (37 dead Ollama models + 2 `frank-default-*`) and was missing `frank-medium`. Rebuilt the `model` table to exactly `frank-large`/`frank-medium`/`frank-small` and set `model_ids` on each connection (llama.cpp's Ollama-shaped `/v1/models` is invisible to OpenWebUI's discovery). Pointed the two on-host endpoints at `host.containers.internal` — the bridge refuses the host LAN IP for non-published ports. All three verified end-to-end via `/openai/chat/completions`.
+- **2026-09-21 — model picker rebuilt for the 3-model fleet.** The picker carried 39 stale rows (37 dead Ollama models + 2 `frank-default-*`) and was missing `frank-medium`. Rebuilt the `model` table to exactly `frank-large`/`frank-medium`/`frank-secondary` and set `model_ids` on each connection (llama.cpp's Ollama-shaped `/v1/models` is invisible to OpenWebUI's discovery). Pointed the two on-host endpoints at `host.containers.internal` — the bridge refuses the host LAN IP for non-published ports. All three verified end-to-end via `/openai/chat/completions`.
 
 - **2026-09-24 — SSO via authentik.** OIDC authorization-code flow wired per the [SSO (authentik)](#sso-authentik) section: application/provider/entitlements created in authentik, client secret in the host env file, roles from the `roles` scope claim. E2E coverage in `tests/e2e/specs/18-openwebui-sso.spec.ts`.
 
 - **2026-09-24 — flipped to SSO-only.** The OIDC round-trip verified in a real browser (login view renders no password form, only "Continue with authentik", and the flow lands on a signed-in session), so the local username/password path was retired: `ENABLE_LOGIN_FORM=false` + `ENABLE_PASSWORD_AUTH=false` in the quadlet and `ui.enable_login_form` set to `false` in the config DB (the DB wins over env, so both were changed together). `qa-e2e-admin` was demoted out of the `authentik Admins` group (it keeps `openwebui_users` / `homepage-admins` for app access).
 
+- **2026-10-07 — picker narrowed to `frank-secondary` only.** Per Frank: the only model accessible in OpenWebUI is `frank-secondary`. Rebuilt the `model` table to a single `frank-secondary` row (the old rows `frank-large`/`frank-primary`/`frank-small` are deleted), set `openai.api_configs` indices 0 (frank-large) and 1 (frank-primary) to `enable: false`, and retargeted index 2's `model_ids` from the stale `frank-small` label to `frank-secondary` — llama.cpp serves by alias, so the old label addressed a model name the server does not know. Pre-change snapshot: `/home/dank/Containers/open-webui/data/webui.db.bak-20261007` (+ `-wal`/`-shm`). Verified: `/api/v1/models/list` returns exactly `["frank-secondary"]`, chat completion answers `pong` (needs `max_tokens≥1500` — the Qwen3.5 thinking block consumes a 200-token budget), and anonymous API probes are all 401 (see [API access](#api-access-auth-gated)).
+
+- **2026-10-09 — re-keyed to the Protoss ids (wire rename).** The endpoints
+  began serving `frank-zealot` / `frank-dragoon` / `frank-archon`, so three
+  id-keyed objects had to move in the same window as the alias flip:
+  `model.id`+`name` (`frank-secondary` → `frank-zealot`, `params`
+  `{"context":{"length":200704}}` and the `meta.description` kept),
+  `access_grant` (`resource_type='model'`, `resource_id`, principal
+  `user`/`*`, permission `read`), and `openai.api_configs` per-backend
+  `model_ids` (index 2 = `:7771` is the enabled family backend; indices 0/1
+  = `:7770` and Dankserver `:7770` stay `enable: false`). Snapshot first:
+  `/home/dank/Containers/open-webui/data/webui.db.bak-20261009`. Apply from
+  inside the container (`podman exec -i open-webui python3 -`, sqlite3; the
+  image has no sqlite3 binary) then `systemctl --user restart open-webui.service`
+  (it is a rootless quadlet) to drop the cached model list. Verify in a real
+  browser as the family QA account: the welcome screen selects
+  `frank-zealot` and answers. **A stale id fails silently** — the picker
+  simply lists nothing, because OpenWebUI intersects the backend's
+  `/v1/models` with `model_ids`.
+
+  Law: **the model id is a foreign key in three tables.** Any future alias
+  change on an endpoint OpenWebUI fronts must ship with this re-key, in the
+  same deploy.
+
+- **2026-10-09 — composer is `contenteditable`, not `<textarea>`.** A
+  Playwright probe that waits for `textarea` after SSO times out on the
+  welcome screen: OpenWebUI 0.6 renders the composer as a contenteditable
+  div. The stable anchor is the model button (`getByRole("button", {
+  name: /selected model/i })`) and the welcome text "How can I help you
+  today?"; that snapshot is also the cheapest proof the picker is populated.
+
+- **2026-10-09 — `frank-dragoon` added to the picker.** Per Frank: expose the
+  halogen endpoint (service `frank-large`, `host.containers.internal:7770`)
+  beside `frank-zealot`. Three id-keyed writes (the trio the 10-07 rebuild
+  proved): `openai.api_configs` idx 0 `enable: true` (its `model_ids` already
+  carried `frank-dragoon` from the wire rename), a `model` row (`id`/`name`
+  `frank-dragoon`, `params.context.length` 262144 = the per-slot
+  `HALOGEN_CTX`, description of the v2 abliterated payload), and the global
+  `access_grant` `(model, frank-dragoon, user, *, read)`. Snapshot
+  `webui.db.bak-20261009b`, apply with host `python3` over ssh, then
+  `systemctl --user restart open-webui.service`. Verified: `/api/models`
+  lists `frank-dragoon` as admin AND as the QA `role='user'` account, and
+  `POST /api/chat/completions` through the app answers `pong` at
+  `max_tokens 1500`. Halogen's `/v1/models` is OpenAI-shaped, so discovery
+  would have worked too — `model_ids` stays authoritative.
+
 ## See Also
 
 - [[Dankclaw Stack]]

serverAppConfigs-docs/Infrastructure/Authentik.md

@@ -449,8 +449,18 @@ access is decided entirely by GROUP MEMBERSHIP consumed downstream —
   `u.app_entitlements(app)`.
 - `jellyfin_users` / `jellyfin_admins` → Jellyfin via the LDAP outpost
   (filter `(memberOf=cn=jellyfin_users,ou=groups,dc=franksdank,dc=cloud)`).
-- `Non-Admin Users` = the plain-user marker group (precedents: alex,
-  qa-e2e).
+- `Non-Admin Users` = the plain-user marker group (precedents: qa-e2e, and
+  every human account).
+- **The human-user template** (ridley, kassie, alex, artem, kirby, ry, nic,
+  debo, freya — kirby/ry/nic/debo/freya (pk 17–21) created 2026-10-09, kassie
+  and alex created 2026-10-08, artem (pk 16) created 2026-10-07): `username` +
+  `<user>@franksdank.cloud` + display name +
+  `is_active=True` + the password set via `set_password`, membership in
+  `Non-Admin Users` + `jellyfin_users` + `openwebui_users`. `alex` was
+  recreated from scratch on 2026-10-08 into this shape (the old row had an
+  empty email and no `openwebui_users`; audit first — the row carried no MFA
+  device, token, entitlement, or proxy session, and events survive a user
+  delete because `Event.user` is a denormalized JSON field, not a FK).
 - EXCLUDE from a user-level account: `homepage-admins`, `jellyfin_admins`,
   `openwebui_admins`, `authentik Admins`, `authentik Read-only`.
 - Other user-category vhosts (SearXNG, Gitea, Calibre-Web, RomM, Copyparty,

serverAppConfigs-docs/Infrastructure/Homepage.md

@@ -23,7 +23,7 @@ The homepage is live on Dankclaw. `./scripts/deploy.sh dankclaw` copies the stat
 page (`shared/homepage/docs.html`) — access walkthrough, the running-model
 guide, media how-tos, and troubleshooting. Per-service `/docs/<svc>/` pages
 remain a separate thread. The three LLM cards (`frank-large`, `frank-medium`,
-`frank-small`) moved from admin-only to the **user** page with live model
+`frank-secondary`) moved from admin-only to the **user** page with live model
 descriptions and a `docs` link to `/docs#models`.
 
 **Sections + WIP split (2026-09-21):** both pages now group their panels
@@ -36,6 +36,211 @@ deliberately unprobeable) via the `wip` value (`"open"` / `"closed"`).
 Tautulli (Plex statistics, `192.168.0.235:8181`) joined the admin page's
 Media Library Apps section.
 
+**Amber CRT theme + API docs (2026-10-07):** the palette moved from green
+phosphor to amber CRT-AMBIOS (`--fg #ffb000` on `--bg #0a0600`), and the font
+stack now leads with **VT323** loaded from the Google Fonts CDN (the deploy
+also ships `shared/homepage/img/` beside the page, so `/img/*` serves the docs
+screenshots — the post-auth listener serves files only through the `@data`
+path allowlist, so new top-level assets must be added there (the `@home`
+catch-all answers anything else with `index.html`, i.e. a PNG arrives as
+`200 text/html` and renders broken; pinned in `homepage_caddy.bats` — this
+exact bug bit the screenshots on 2026-10-08). The docs page gained a full
+(`/docs#api-access`): per-user OpenWebUI API keys (User menu → Settings →
+Account → API keys), the base URL `https://ai.franksdank.cloud/api/v1`, and
+copy-paste configs for **Opencode** (`~/.config/opencode/opencode.json`) and
+**Pi** (`~/.pi/agent/models.json`) pointing at `frank-secondary`. Shipped
+together with the OpenWebUI side: `features.api_keys=true` for regular users
+and the restored `frank-secondary` model row + public `read` access grant
+without which the picker showed "No results found" for every family account
+(see [[OpenWebUI]]).
+
+**Capability equivalence notes (2026-10-08):** each fleet card (and the
+`/docs#models` section) carries a `details.equiv` line — frank-large
+"equivalent to Claude Sonnet 5 / OpenAI-Luna (Summer 2026 frontier
+intelligence)", frank-primary "equivalent to Claude Opus 4.8 / OpenAI-Terra
+(same era)", frank-secondary "equivalent to last year's frontier level
+(2025)". Rendered by `descHTML()` as `.desc-equiv` (head color, italic);
+pinned in `homepage_config.bats`.
+
+**Protoss rename + Model Information component (2026-10-08):** the model
+cards became the StarCraft trio — `frank-large` → **frank-dragoon**
+(iGPU), `frank-primary` → **frank-archon** (Dankserver), `frank-secondary`
+→ **frank-zealot** (3090). The endpoint/API ids did NOT change in this wave; only the
+family-facing names. The three cards fused into one **Model Information**
+component (`app.js` `renderModelInfo()`, `.model-info`/`.mi-*` styles):
+Protoss unit art (`/img/unit-<unit>.jpg`, hex-clipped), per-unit accent
+colours (Dragoon gold, Archon cyan, Zealot blue), smaller type (15px vs
+19px body), and clickable unit-voice lines that play the original
+StarCraft `.ogg` quotations from `/sfx/` (fetched from
+starcraft.fandom.com via its MediaWiki API; the quote→file mapping lives
+in the `model.sfx`/`model.voice.sfx` fields). New `services.json`
+per-model fields: `unit`, `tagline`, `sfx[]`, `voice`, `supervision`
+(dragoon/archon autonomous-safe, zealot tight-supervision), and `warn`
+(uncensored legal note, rendered as a hover tooltip, not wall text).
+A compaction-model recommendation for frank-zealot rides in its `use`
+text. The Caddy `@data` allowlist grew `/sfx/*`; `deploy_homepage` copies
+`sfx/`. A `wip: "down"` badge state ("WIP: DOWN", red) was added; Seerr,
+Calibre-Web, RomM, and Jellyfin are WIP: DOWN with their Open links
+removed. Card notes added: Copyparty holds every other media app's
+content (including the WIP ones); Gitea and Plex need logins separate
+from the dankcloud SSO; SearXNG is obfuscated, not anonymous (upstream
+API keys tie to Frank). The user docs `/docs#models` section carries the
+same renames, api-id mapping, supervision and compaction notes.
+
+**Icons, card polish, admin identity gate (2026-10-08, later wave):** every
+app card now carries hex-clipped art beside its name (`services.json` `art`
+field, files under `img/apps/`): 19 brand marks tinted amber from the
+Simple Icons CDN plus 19 hand-improvised monogram SVGs (amber hex + VT323
+initials) for apps with no mark — Copyparty, Marreta, Hermes (+dashboard),
+node-exporter, Gluetun, OpenWebUI, Calibre-Web, RomM, Seerr, Loki, Alloy,
+Alertmanager, Valkey, Prowlarr, Readarr, Lidarr, Tautulli, ComfyUI. Model
+cards render in the order zealot → dragoon → archon (registry order is the
+contract; `homepage_config.bats` pins it). Dragoon and zealot share one
+common uncensored legal warning (adult content, harmful/hateful/illegal
+descriptions, "sensitive offensive cybersecurity topics", severe legal
+risk, "don't ruin this server for everybody"), rendered as the hover
+label; archon's ask-Frank rule is a bordered red `.mi-perm` callout.
+SearXNG's obfuscation-not-anonymity note moved out of its description into
+a card-level `warn` (`.svc-warn` hover label) and the same hover treatment
+in the docs. Gitea's note now documents self-service signup — no need to
+ask Frank. Footer: docs / admin / account settings are bordered glowing
+`.foot-btn` buttons; **the admin button is identity-gated** — Caddy's
+`/whoami` echoes the outpost-injected `X-Authentik-Username`/`X-Authentik-Groups`
+headers (the outpost strips client-supplied copies, so the echo is
+spoof-proof), and `app.js` shows the button only to `homepage-admins`
+members. The `/admin*` 403 stays the real gate; the e2e specs pin both
+sides (standard user: hidden + 403; admin: visible). Docs were swept for
+naming consistency: Protoss names primary everywhere, bracketed api ids
+at the model headings, and a note that a future switchover renames the
+ids themselves (OpenWebUI picker + raw API + models.yml all move together
+— not done yet).
+
+**Icons v2, clickable tiles, header nav (2026-10-08, third wave):** five
+icons replaced on request — Copyparty gets an improvised amber folder,
+Marreta a crumbling wall (bricks + red crack), Calibre-Web a book,
+OpenWebUI its official logo (`openwebui.com/logo.png`), Seerr the official
+`seerr.dev/os_logo_filled.svg`. Tiles with an open link are now clickable
+whole (`.panel.clickable`, hover lift + glow; clicks on inner links pass
+through). Any panel whose live state is DOWN gets a red gradient wash +
+slow pulsing glow (`.panel.down`, and `.mi-card:has(.badge.down)` for
+model cards). docs/admin/account-settings moved from the footer to the
+header (`nav.top-links`). The zealot compaction advice moved out of the
+Use paragraph into a bordered glowing `model.compaction` callout
+(`.mi-comp`), pinned by the component seam test.
+
+**Protoss theme, brand-color icons, portraits (2026-10-08, fourth wave):**
+the full-page angular treatment: chamfered 14 px-cut panels and model
+cards, hex-lattice + amber-glow backdrop (`body::after`), scanline
+overlay (`body::before`), diamond-glyph section titles with gradient
+underlines, the gradient MODEL INFORMATION gateway, a blinking cursor
+beside the title, amber scrollbars, cyan text selection, cyan hover glow
+on clickable tiles, and a red pulsing glow on down tiles/cards (the
+`data-state="down"` attribute `applyStatus` now sets). Icons recut to
+official brand colors (Simple Icons CDN): SearXNG, Gitea, Plex, Jellyfin,
+Sonarr, Radarr, Lidarr, Readarr, qBittorrent, Prowlarr, Seerr, OpenWebUI,
+Calibre-Web (amber glyph on amber tile). Handcrafted monograms carry
+per-app colors: Copyparty gold, Marreta brick, Grafana orange, Loki
+indigo, Neo4j network, Alloy violet, Promtail pink, Alertmanager red,
+Prometheus orange, Node Exporter teal, cAdvisor sky, Guake yellow,
+Authentik blue, Seer green radar, RomM a purple GameBoy. New per-service
+fields: `art_bg` (art backdrop — OpenWebUI's dark logo needs `#f5f5f5`),
+`warn` on OpenWebUI (searches ride the Seerr→SearXNG path),
+`model.compaction` (the amber "set this as compaction model" callout
+frank-zealot carries). frank-dragoon's card portrait is the SC2 unit
+portrait (fandom wiki `Dragoon_portrait.jpg`, webp→jpeg). The whole
+effect layer is stripped under `data-bare="1"` (the e2e bare page).
+Unit: `tests/homepage_config.bats` (referenced art files exist, model
+blocks well-formed, deployer ships the `img/` tree).
+
+**Docs reorder + model source links (2026-10-08, fifth wave):** the docs
+page section order is now getting-in → troubleshooting → media → chat →
+models → api-access (non-AI first, AI last). The *arr stack (Prowlarr,
+Sonarr, Radarr, Lidarr, Readarr, qBittorrent) is out of the family docs
+entirely — admin tooling; Seerr is presented as the front door for
+requests. Each fleet model gained a Hugging Face source link: card field
+`model.hf` rendered as the cyan `model on huggingface ↗` line under the
+role, and the docs `Model:` bullets link the checkpoint names (zealot →
+HauhauCS/Qwen3.5-9B-Uncensored-HauhauCS-Aggressive, dragoon →
+Quat3rnion/halogen-swift1.5-qwen3.8-flash-next-v2-abliterated, archon →
+Qwen/Qwen3.8-Flash-Next-FP8).
+
+**Navy Protoss console, maximal cornering, font swap (2026-10-08, sixth
+wave):** the amber CRT palette is retired. The page is now deep space
+navy (`#050d16`) with warp-blue/psychic-cyan accents (`--proto-blue
+#3fa9ff`, `--proto-cyan #7fe7ff`) — StarCraft Protoss console colours.
+Cornering went maximal: every panel, model card, badge, button, callout
+and the MODEL INFORMATION gateway are octagon-cut (all four corners
+chamfered, 18–24 px), and panels/model cards gained a second inset
+octagonal frame line (`::before`) — a double-bevel gateway look. Section
+titles are chamfered plates with the diamond glyph inside the plate.
+Fonts: body is **Share Tech Mono** (legible retro terminal), headings
+are **Rajdhani 600** (the StarCraft UI face) — VT323 is retired; all
+three pages load the pair from Google Fonts. Four tiles that had
+reverted to initials got pictograms: Calibre-Web an open book (orange),
+RomM a purple GameBoy, Copyparty a blue folder with upload arrow,
+Marreta a crumbling brick wall with a red crack (`img/apps/app-*.svg`).
+frank-dragoon's card accent is phase-crystal teal (`#5fe0d0`), matching
+the new palette. Bare mode strips the frames, octagon cuts and fonts.
+
+**Simplified console (2026-10-08, seventh wave):** "too busy — clean up
+and simplify/solidify." The sixth-wave effect stack was stripped to
+essentials. Removed: the scanline overlay (`body::before`), the hex
+lattice + twin radial backdrop (one quiet pylon glow above the horizon
+remains), the panel/model-card inner frame lines, all ambient text
+glows (body, title, model names, callouts), the blinking title cursor,
+the gradient section-title plates (headings are plain Rajdhani with a
+hollow diamond glyph + steel underline), the gradient `border-image`
+header rule (solid 2px warp-blue), the pulsing down-state animation
+(down tiles and cards now show a STATIC red border + tint), and the
+button/hover glows. Kept: octagon-cut panels (softer 14 px), unit-color
+model cards, the pictograms, and the fonts. Motion is reserved for
+state — the only animation left is the speaking-voice pulse. The result
+is flat navy panels on crisp 1px borders: same Protoss direction, half
+the pixels.
+
+**Model ids official (2026-10-08):** the card ids `frank-zealot`,
+`frank-dragoon`, `frank-archon` are now the harness's official model ids
+in `models.yml`/`config.yml` (see [[OMP Harness]]); the
+systemd services keep the `frank-primary`/`frank-large`/
+`frank-secondary` names. Homepage, docs page and harness now agree on
+one name per model.
+
+**Api ids renamed on the wire (2026-10-09):** the promise in
+`docs.html` is kept — the served model ids are now `frank-zealot`,
+`frank-dragoon`, `frank-archon`, so the docs page no longer carries
+`api id:` annotations, and the curl/opencode/Pi examples send
+`frank-zealot`. The systemd services keep the old names. `docs.html`
+names the fleet trio once each; `homepage_config.bats` pins the card
+ids, which did not change.
+
+**Console polish (2026-10-08, eighth wave):** five fixes — octagon
+clip-paths replaced with `border-radius` (the clipped corners read as
+gaps in the 1px border); section-title diamonds moved from absolute
+positioning to a fixed-width inline glyph (aligned, wrap-safe); closed
+WIP items grey out (dim type, desaturated art, muted border via
+`:has(.badge.wip-closed)`); the Dragoon tagline is one spliced track
+(`sfx/dragoon-tagline.ogg` — loudnorm-matched, silence-trimmed, 350 ms
+beat; the two SC2 source oggs are trashed); privacy notes became
+**`<details>` disclosure chips** ("⚠ privacy note ▸") pinned to the
+bottom of each box (`margin-top:auto` in a flex-column panel/card) —
+collapsed by default, tap-to-expand, the touch-safe replacement for the
+unreachable hover tooltips. The panel click-to-open handler ignores
+`.svc-warn` taps so the chip never navigates. Same chips on the docs
+page (privacy + uncensored-model warnings). The API-access section was
+reblessed against the live Open WebUI 0.6 UI: steps now match the real
+Show/copy/rotate flow and `img/openwebui-api-keys.png` is a fresh
+screenshot (old shot trashed). The troubleshooting DNS bullet dropped
+its Pi-hole specifics — family members off the LAN cannot use
+house-DNS advice; the bullet now just says the names resolve on the
+home network.
+
+**Archon card runs the fleet's own model (2026-10-08):** the
+`frank-archon` homepage card and docs Model bullet now say the payload
+is our in-house GPTQ-w4 quant of Swift1.5-Qwen3.8-Flash-Next and link
+our HF publication (`frank8465/Swift-1.5-Qwen3.8-Flash-Next-GPTQ-W4A8-Radiance-AMD-R9700`)
+in place of the FP8-era base-model link; capacity line says three
+submissions (`--max-num-seqs 3`).
+
 ## Design decision — 2026-08-22
 
 The live up/down check runs server-side. A checker on Dankclaw probes each backend and writes a same-origin `status.json`. The page fetches only that file and renders the badges. The browser never calls a backend directly. The checker runs as a systemd timer every 3 minutes.
@@ -111,7 +316,7 @@ flowchart TD
 ## Service classification
 
 **User services** (root page), in section order:
-- **AI** — OpenWebUI, and the three LLM fleet cards (frank-large, frank-medium, frank-small — status + docs link, no Open link).
+- **AI** — OpenWebUI, and the three LLM fleet cards (frank-large, frank-primary, frank-secondary — status + docs link, no Open link).
 - **Web Apps** — SearXNG, Gitea, Copyparty, Marreta.
 - **Media Library Apps** — Calibre-Web, RomM, Plex, Jellyfin, Seerr.
 
@@ -131,7 +336,10 @@ shared/homepage/
 ├── style.css         # full theme; a toggle strips to bare HTML
 ├── app.js            # loads status.json once, renders the panels
 ├── services.json     # service config (user + admin)
-└── fonts/            # Terminus (woff2) for @font-face
+├── img/              # served at /img: docs screenshots, unit portraits
+│   ├── apps/         #   (unit-*.jpg) and app card icons (apps/*.svg)
+├── sfx/              # StarCraft unit voice .ogg files served at /sfx
+└── fonts/            # optional self-hosted fonts (deployed with the page; VT323 comes from the Google Fonts CDN, this dir is absent)
 ```
 
 `status.json` is generated at runtime in `/srv/homepage/`. It is not committed.

serverAppConfigs-docs/Infrastructure/Prometheus.md

@@ -38,7 +38,7 @@ Authentik; post-auth the Caddy vhost routes straight to `prometheus:9090`) —
 | `node-dankserver` | `192.168.0.235:9100` | host metrics from [[Node-Exporter]] — a different machine, so the LAN address works |
 | `grafana`, `loki` | `grafana:3000`, `loki:3100` | container names on the `dankclaw_default` network; both serve real `/metrics` |
 | `frank-medium` | `192.168.0.235:7770` | the LLM fleet — `vllm:` series; a different machine, so the LAN address works |
-| `frank-large`, `frank-small` | `host.containers.internal:7770`, `:7771` | the LLM fleet — `llamacpp:` series; same-host net=host binds, so via the gateway |
+| `frank-large`, `frank-secondary` | `host.containers.internal:7770`, `:7771` | the LLM fleet — `llamacpp:` series; same-host net=host binds, so via the gateway |
 
 **Three jobs read down, and they are not outages** (audited 2026-09-16):
 

serverAppConfigs-docs/LLM/DFlash DSpark Speculative Decoding.md

@@ -215,9 +215,9 @@ The DFlash draft is not a standalone model. It must pair with its target model.
 
 ## Deployment on Dankserver (NVIDIA RTX 3090)
 
-The Dankserver llama.cpp deployment (`frank-small`, Qwen3.6-35B-A3B) is **active** on Dankserver (port 7770) as of the 2026-08-17 redo (the earlier 2026-08-16/17 retirement of this service was reversed when the PP split was retired). The vLLM material below documents the archived AWQ/DFlash path on the same hardware.
+The Dankserver llama.cpp deployment (`frank-secondary`, Qwen3.6-35B-A3B) is **active** on Dankserver (port 7770) as of the 2026-08-17 redo (the earlier 2026-08-16/17 retirement of this service was reversed when the PP split was retired). The vLLM material below documents the archived AWQ/DFlash path on the same hardware.
 
-> **DFlash retired from active service.** `DFlash` (draft-dflash) was tested initially but did not deliver the desired speed gains under the `frank-small` workload (see ngram-mod benchmarks below). The `Alittlehammmer/Qwen3.6-35B-A3B-DFlash` draft GGUF remains in `/home/dank/models/` as reference material only.
+> **DFlash retired from active service.** `DFlash` (draft-dflash) was tested initially but did not deliver the desired speed gains under the `frank-secondary` workload (see ngram-mod benchmarks below). The `Alittlehammmer/Qwen3.6-35B-A3B-DFlash` draft GGUF remains in `/home/dank/models/` as reference material only.
 
 The vLLM material below documents the archived AWQ/DFlash path on the same hardware. The RTX 3090 is Ampere (compute capability 8.6). Use these conditions:
 
@@ -315,7 +315,7 @@ not switch without re-measuring".
 
 The A/B vehicle for ANY single-GPU llama.cpp knob (batch/ubatch, KV type,
 slot split, quant rung — not just spec flags) is the transient systemd
-drop-in procedure in [[frank-large-benchmark]] and [[frank-small-benchmark]].
+drop-in procedure in [[frank-large-benchmark]] and [[frank-secondary-benchmark]].
 
 ## Is it worth it? (arithmetic before deployment)
 
@@ -419,4 +419,4 @@ measurement that would settle it.
 - [[GGUF Compatibility]] — header parsing and custom tensor-type lock-in
 - [[Llama Cpp Serving Sizing]] — slot-pool and drafter KV sizing arithmetic
 - [[Llama Cpp Multi-Device Splits]] — where the drafter's KV actually lands
-- [[frank-large-benchmark]] and [[frank-small-benchmark]] — the single-GPU env drop-in A/B vehicle
+- [[frank-large-benchmark]] and [[frank-secondary-benchmark]] — the single-GPU env drop-in A/B vehicle

serverAppConfigs-docs/LLM/Dankclaw System Specs.md

@@ -46,7 +46,7 @@ Dankclaw is an AMD APU server for CIRU ROCmFPX llama.cpp inference. The host run
 | Runtime | FastFlowLM 0.9.45 + XRT (installed 2026-08-16; **retired 2026-09-11** with the `amd_iommu=off` decision) |
 | Status | Not in service. Research only. See [[FastFlowLM NPU Inference]] |
 
-The NPU is retired (2026-09-11). The `frank-small` name now serves Gemma4-12B QAT on the RTX 3090 via llama.cpp (see [[frank-small-gemma4-12b]]).
+The NPU is retired (2026-09-11). The `frank-secondary` name now serves Gemma4-12B QAT on the RTX 3090 via llama.cpp (see [[frank-secondary-gemma4-12b]]).
 
 ### ROCm
 

serverAppConfigs-docs/LLM/Dankserver System Specs.md

@@ -17,7 +17,7 @@ timestamp: 2026-09-20
 
 Dankserver is a media/library server and the `frank-medium` LLM host. The
 host runs Ubuntu 26.04 (Resolute Raccoon). The RTX 3090 that served the
-FreeToken/CUDA `frank-small` era was removed on 2026-09-15 and replaced with
+FreeToken/CUDA `frank-secondary` era was removed on 2026-09-15 and replaced with
 two AMD Radeon AI PRO R9700 cards.
 
 ## Hardware Specifications
@@ -107,8 +107,8 @@ hijack `libhsa-runtime64` were purged on 2026-09-15 — see
 The active inference service is `frank-medium.service`: mainline llama-server on
 port 7770, layer-split 32:33 across both R9700s with the DFlash2 drafter pinned to
 ROCm1. See [[frank-medium-llamacpp-dflash2]]. The former
-`frank-small.service` (FreeToken/CUDA) was retired with the 2026-09-15 GPU swap;
-its unit is archived in `~/.AGENTTRASH/20260915-frank-small-retire/host-units/`,
+`frank-secondary.service` (FreeToken/CUDA) was retired with the 2026-09-15 GPU swap;
+its unit is archived in `~/.AGENTTRASH/20260915-frank-secondary-retire/host-units/`,
 and the vllm-radiance replica trio in `~/.AGENTTRASH/20260916-vllm-radiance-retire/`.
 See [[deployment]].
 

serverAppConfigs-docs/LLM/FastFlowLM NPU Inference.md

@@ -13,12 +13,12 @@ timestamp: 2026-09-03
 
 > **SUPERSEDED 2026-08-17.** The Frank redo retired this topology.
 > See [[frank-redo-2026-08-17]] and [[deployment]] for the active configuration.
-> The `frank-small` name and port 52625 then belonged to the vLLM
+> The `frank-secondary` name and port 52625 then belonged to the vLLM
 > service on Dankclaw (nameistoken Quark W8A8); this page is archive.
 
 # FastFlowLM NPU Inference
 
-FastFlowLM (FLM) runs LLMs entirely on the AMD Ryzen AI NPU. It targets XDNA2 chips: Strix, Strix Halo, Kraken, and Gorgon Point. The project describes itself as "Ollama, but deeply optimized for NPUs." As of 2026-08-16, FLM is installed on Dankclaw, the NPU validates, and `frank-small` (port 52625) serves in production. See Promotion.
+FastFlowLM (FLM) runs LLMs entirely on the AMD Ryzen AI NPU. It targets XDNA2 chips: Strix, Strix Halo, Kraken, and Gorgon Point. The project describes itself as "Ollama, but deeply optimized for NPUs." As of 2026-08-16, FLM is installed on Dankclaw, the NPU validates, and `frank-secondary` (port 52625) serves in production. See Promotion.
 
 ## Verdict (2026-08-16)
 
@@ -203,16 +203,16 @@ Notes:
 
 ## Promotion (2026-08-16)
 
-`frank-small` now runs the NPU endpoint on Dankclaw (was the Dankserver llama.cpp service). Unit: `scripts/frank-small.service` (symlinked into `/etc/systemd/system`), wrapper `scripts/serve-flm-small.sh`, tunables `scripts/frank-small.env`. `LimitMEMLOCK=infinity` is set in the unit. Default model: `gemma4-it:e4b`. Swap `FLM_MODEL` in the env to switch.
+`frank-secondary` now runs the NPU endpoint on Dankclaw (was the Dankserver llama.cpp service). Unit: `scripts/frank-secondary.service` (symlinked into `/etc/systemd/system`), wrapper `scripts/serve-flm-small.sh`, tunables `scripts/frank-secondary.env`. `LimitMEMLOCK=infinity` is set in the unit. Default model: `gemma4-it:e4b`. Swap `FLM_MODEL` in the env to switch.
 
 ### Boot policy (2026-08-17)
 
-The `frank-small` unit is disabled for boot. Only the PP pair (`frank-large` + `frank-pp-worker`) starts at boot. The unit file was removed from `/etc/systemd/system`; the running FLM service keeps serving until stopped or rebooted. To restore the endpoint, run these commands on Dankclaw:
+The `frank-secondary` unit is disabled for boot. Only the PP pair (`frank-large` + `frank-pp-worker`) starts at boot. The unit file was removed from `/etc/systemd/system`; the running FLM service keeps serving until stopped or rebooted. To restore the endpoint, run these commands on Dankclaw:
 
 ```
-sudo ln -sf /home/dank/vllmScripts/scripts/frank-small.service /etc/systemd/system/frank-small.service
+sudo ln -sf /home/dank/vllmScripts/scripts/frank-secondary.service /etc/systemd/system/frank-secondary.service
 sudo systemctl daemon-reload
-sudo systemctl enable frank-small
+sudo systemctl enable frank-secondary
 ```
 
 Live config (2026-08-16): `FLM_PMODE=turbo`, `FLM_ASR=1` (loads Whisper-V3-Turbo-NPU2; exposes `/v1/audio/transcriptions`), `FLM_CTX_LEN=131072` (verified max — Gemma4 natively supports 128K and the NPU budget fits it; KV occupancy measured at 0.11% for a 123-token exchange, decode 12.0 t/s, 13 GiB headroom).
@@ -353,7 +353,7 @@ installed) against `/opt/fastflowlm/share/flm/xclbins/` (available).
 Symptom: a client on dankware could not reach `qwen3.6-moe:35b-a3b` on port 52625. The journal shows three separate causes in the 14:19-15:03 window.
 
 1. **EXEC 203 crash loop (14:19-14:20).** The exec bit was lost on `serve-flm-small.sh` on the target. The unit failed `status=203/EXEC: Permission denied` four times, then started after the bit was restored. The git mode is `100755` (commit `3f42210`), and a `git pull` restores the bit. Verify with `git ls-files -s scripts/serve-flm-small.sh`.
-2. **Wrong model ID (14:29-14:52).** The client config still used the model ID `frank-small` (a service alias). FLM rejects that tag with `[ERROR] Model tag 'frank-small' is not supported.` Each such request also triggered the unknown-tag fallback below.
+2. **Wrong model ID (14:29-14:52).** The client config still used the model ID `frank-secondary` (a service alias). FLM rejects that tag with `[ERROR] Model tag 'frank-secondary' is not supported.` Each such request also triggered the unknown-tag fallback below.
 3. **Swap churn (14:48-15:03).** The swap `gemma4-it:e4b` → `qwen3.6-moe:35b-a3b` restarted the service several times. Each load of the 23 GB model takes about 1.5-2.5 minutes at 262144 ctx. The endpoint does not serve during a load.
 
 Fix: the client model ID must be the FLM registry tag `qwen3.6-moe:35b-a3b`. The working pi agent entry in `~/.pi/agent/models.json` is `baseUrl http://192.168.0.143:52625/v1`, `id qwen3.6-moe:35b-a3b`, `contextWindow 262144`, `reasoning true`, `input [text, image]`. Verified end-to-end from dankware (192.168.0.226) at 15:32 on 2026-08-16.
@@ -390,7 +390,7 @@ pi sessions failed against this endpoint with `Stream ended without finish_reaso
 - **The FLM serve process is not under systemd.** A reboot of Dankclaw kills it. The PP head service is enabled and returns. The FLM process must be restarted by hand. A unit file is a natural next step.
 - **gpt-oss reasoning budget.** The model can spend its full token budget on `reasoning_content` and return empty content. Clients must handle or cap this.
 - **No llama.cpp GGUF path.** FLM uses its own pre-compiled kernels. Our UD-Q8_K_XL and HauhauCS builds are not importable. NPU work means FLM-specific model tags.
-- **NPU production role (2026-08-16).** `frank-small` serves `qwen3.6-moe:35b-a3b` on port 52625 (swap churn and the unknown-tag incident are recorded above). The remaining NPU content (benchmarks, runbook steps) is still research until a step lands and is recorded.
+- **NPU production role (2026-08-16).** `frank-secondary` serves `qwen3.6-moe:35b-a3b` on port 52625 (swap churn and the unknown-tag incident are recorded above). The remaining NPU content (benchmarks, runbook steps) is still research until a step lands and is recorded.
 
 ## Citations
 

serverAppConfigs-docs/LLM/FreeToken Builds.md

@@ -23,7 +23,7 @@ read the returned content:
 ```python
 import json, urllib.request
 req = urllib.request.Request("http://<host>:<port>/v1/chat/completions",
-    json.dumps({"model": "frank-small", "messages": [{"role": "user",
+    json.dumps({"model": "frank-secondary", "messages": [{"role": "user",
     "content": "Hi"}]}).encode(), {"Content-Type": "application/json"})
 print(json.load(urllib.request.urlopen(req, timeout=600)))
 ```

serverAppConfigs-docs/LLM/LLM Bench Windows and Measurement.md

@@ -27,7 +27,7 @@ wedging a host until power cycle. Reference harnesses:
    with `container state improper`.
 2. **Boot the arm as the service, not a scratch port** (no scratch ports on
    LLM hosts — one card, one server). The serve script takes its config via
-   env-file override (`FRANK_SMALL_ENV_FILE`, `FRANK_PRIMARY_AR_ENV_FILE`)
+   env-file override (`FRANK_SECONDARY_ENV_FILE`, `FRANK_PRIMARY_AR_ENV_FILE`)
    pointing at a staged copy; the checkout is never mutated.
 3. **Fast-fail the health wait**: poll `podman ps | grep -qx <name>` inside
    the loop — a refused boot dies in one poll instead of 20 min. Capture the

serverAppConfigs-docs/LLM/Llama Cpp Serving Sizing.md

@@ -80,7 +80,7 @@ co-tenants.
 - **Traps:** mmap'd PLE is reclaimable page cache; `-ot` makes it pinned —
   "move layers to the card" converts reclaimable cache into device memory and
   frees nothing for co-tenants. A served model's footprint ≠ weight size
-  (frank-small: 22.6 of 24.1 GiB for an 11.29 GiB model). Do the capacity
+  (frank-secondary: 22.6 of 24.1 GiB for an 11.29 GiB model). Do the capacity
   subtraction before designing: 12 attn layers × 1.62 GiB + 200k f16 KV =
   28.6 GiB > 24.1 GiB usable → KV-in-VRAM on that pair only reaches ~64k.
 - **Context lengths: derive, never remember.** `llama-bench` computes
@@ -157,7 +157,7 @@ in the timings. Context cap for full-model residency on that shape stayed
 ## Related
 
 - [[GGUF Compatibility]] — what a payload is before it is priced
-- [[frank-small-llama-mimo-q6k-2x200704]] — the current two-boot priced shape
+- [[frank-secondary-llama-mimo-q6k-2x200704]] — the current two-boot priced shape
 - [[frank-medium-llamacpp-dflash2]] — where the 68 KiB/token datum was measured
 - [[DFlash DSpark Speculative Decoding]] — the spec A/B protocol on llama.cpp
 - [[Dual-R9700-Cooperation]] / [[x4-pair-experiment]] — the multi-device side

serverAppConfigs-docs/LLM/Model Swap and Rollback.md

@@ -8,7 +8,7 @@ timestamp: 2026-10-05T00:00:00-07:00
 
 # Model Swap and Rollback
 
-Applies to every endpoint swap on the fleet (frank-small / frank-large /
+Applies to every endpoint swap on the fleet (frank-secondary / frank-large /
 frank-primary). Repo law: all config flows through git; the live models are
 host state under `/home/dank/models/<name>/`.
 

serverAppConfigs-docs/LLM/Pipeline Parallelism/Qwen3.8 PP Deployment.md

@@ -48,10 +48,10 @@ state, verified against the live journal and output smoke tests
 
 The PP pair is the only inference service enabled for boot. Both units
 (`frank-large` on Dankclaw, `frank-pp-worker` on Dankserver) are
-enabled. The NPU unit (`frank-small` on Dankclaw) and all retired
+enabled. The NPU unit (`frank-secondary` on Dankclaw) and all retired
 units are disabled. Restart order: worker first, then head. The Dankserver
 helper `frank-restart-small.sh` is obsolete (it restarts the removed
-`frank-small` unit); restart the worker with
+`frank-secondary` unit); restart the worker with
 `sudo systemctl restart frank-pp-worker`. The Dankclaw helper
 `frank-restart-large.sh` (sync + cache drop + restart) is correct for the
 head. `deploy.sh dankserver` is broken until retargeted. See

serverAppConfigs-docs/LLM/Pipeline Parallelism/index.md

@@ -65,6 +65,6 @@ graph LR
 ## See Also
 
 - [[DFlash DSpark Speculative Decoding]]
-- [[frank-small-benchmark]]
+- [[frank-secondary-benchmark]]
 - [[Dankserver System Specs]]
 - [[Dankclaw System Specs]]

serverAppConfigs-docs/LLM/Quantization Production.md

@@ -0,0 +1,260 @@
+# Quantization Production — making our own quants, per card and runtime
+
+**Research page (2026-10-08).** Question: how do WE produce quantizations
+for the RTX 3090, the R9700 (gfx1201), and Strix Halo (gfx1151); what is
+native to each runtime; and the state of the named prior-art families
+(REAP, Heretic, abliteration, HauhauCS K_P "Perfect"). Marked facts:
+**[repo-verified]** = evidenced by a file in this repo; **[verified]** =
+checked first-hand against the cited primary source today; the rest are
+survey claims to re-verify before acting.
+
+## Runtime-native quant matrix
+
+| Card | Runtime | Container | Native weight encodings | Production tooling we own |
+|---|---|---|---|---|
+| RTX 3090 (sm_86) | llama.cpp CUDA | GGUF | K-quants (Q4_K_M…Q8_0), I-quants (IQ1_S…IQ4_XS), ML, f16/bf16; any tensor type per-tensor | `llama-quantize` + `ggml-imatrix` (built by `scripts/build-llama-rdna4.sh` targets) **[repo-verified]** |
+| R9700 (gfx1201) | radiance (StillDeadcode) | `.rad` | per-tensor encodings from a recipe: GPTQ w4 (`w4nl64a8h`), bf16, int8, fp8_e4m3, u2, MXFP4/MXFP6 (vLLM-radiance era) | full pipeline: `rad-convert` + calibration tap + `derive-protected-experts.py` + `kld-gate-swift15.sh` **[repo-verified]** — CPU host-backend, the card is irrelevant to production |
+| R9700 (gfx1201) | vLLM (tcclaviger/R4D) | safetensors | native block-FP8, MXFP4/DSFP4, GPTQ/AWQ, compressed-tensors | GPTQModel/AutoRound are hardware-agnostic torch (CPU-scale); AMD decode kernels are the gate **[survey]** |
+| Strix Halo (gfx1151) | halogen-flash-server (peonist, **closed**) | `.hgn` | vendor-defined (w4b era, v2 checkpoint+ngram+vision files) | **NONE public** — no `.hgn` quantizer exists; every `.hgn` on the fleet was made by peonist/Quat3rnion **[repo-verified gap]** |
+| Strix Halo (gfx1151) | llama.cpp / gufo / Kairic / ROCmFPX forks | GGUF | K/I quants (gufo UD-* = imatrix mixed profiles), custom types: `Q4_0_ROCMFP4_STRIX`, `Q8_0_ROCMFPX` (type 103), Kairic IU4 | we built the RUNTIMES (`scripts/build-kairic-edge-gfx1151.sh`, ROCmFPX pin `36e9acd`), never the custom-type quantizers — those files came from agentionai/kingjones **[repo-verified]** |
+
+- **The lock is asymmetric: llama.cpp on the 3090 is a CHOICE.** radiance
+  demands `.rad`, halogen demands `.hgn` (the v2 checkpoint loads in no
+  other engine); sm_86 decodes GGUF, GPTQ/AWQ-Marlin, INT8, weight-only
+  FP8, and EXL3 natively — see the 3090 menu under Per-card playbooks.
+
+Laws that steer everything here:
+- **Production is hardware-agnostic; decode is not.** GPTQ/imatrix/ablation
+  surgery runs in torch/CPU anywhere. The card only picks which ENCODED
+  FORM it can DECODE — hence the one-table-one-encoding law, the GGUF
+  custom-type histogram law ([[GGUF Compatibility]]), and the fact that
+  our w4 convert ran 5 h 50 m on the host backend while the GPUs served
+  nobody (`scripts/convert-swift15-w4dn8.sh`) **[repo-verified]**.
+- **A custom tensor type locks the file to its fork** (ROCmFPX type 103
+  precedent). K_P quants deliberately avoid that trap (below).
+
+## The named families
+
+### REAP — Router-weighted Expert Activation Pruning (Cerebras, 2025)
+- arXiv 2510.13999; `github.com/CerebrasResearch/reap` **[verified]**.
+- One-shot MoE surgery: score every expert by router gate-values ×
+  activation energy, prune the low-utility tail, renumber+export a
+  SMALLER MoE checkpoint (safetensors; GGUF by conversion). Published:
+  GLM-4.5-Air −25 % (82B→~A12B class), GLM-4.7-REAP-268B-A32B-FP8;
+  near-lossless at 25–50 % compression on generative benchmarks, and
+  pruning BEATS expert-merging there **[verified from paper/README]**.
+- Fleet relevance: it is the **inverse of our protected-experts law**.
+  `derive-protected-experts.py` ranks experts by calibration-gram energy
+  share and keeps the hottest 24/layer at bf16; REAP's saliency is the
+  same statistic and deletes the coldest. REAP the Flash-Next MoE (all
+  49,152 experts w4, 512/layer) by 25–50 % → resident set ~58–87 GiB →
+  the expert pool fits with room for KV/cache, killing the disk tier.
+  A REAPed checkpoint re-enters our normal pipeline: stage → calibrate →
+  rad-convert → KLD gate. Hardware-agnostic; runs on the host.
+
+### Heretic (`p-e-w/heretic`) — automated abliteration + layer pruning
+- `github.com/p-e-w/heretic`, AGPLv3 **[verified]**. NOT a quantizer.
+  Fully-automatic censorship removal: directional ablation (refusal
+  direction from PCA of harmful/harmless residuals), per-layer ablation
+  weights, optional LAYER DROPPING (model shrinks), auto-tuned by
+  co-minimizing refusals and KL divergence from the original. Runs on
+  our 3090 (torch, ~24–48 GB working set for 9–27B classes; long batch
+  probes) **[verified README; runtime cost survey]**.
+- Output is a normal HF checkpoint (fp16/bf16) → feeds GGUF, safetensors
+  (vLLM), and `.rad` conversion. Order law: **surgery → calibrate →
+  quantize**; calibrate GPTQ/imatrix on the SURGICAL output, and keep
+  refusal-shaped tokens out of the calibration corpus.
+- Fleet note: our abliterated bases (Quat3rnion v2, HauhauCS
+  Aggressive/Balanced, ukisai Swift1.5) are this family's output; the
+  Quat3rnion repo lore "the abliteration workflow appends, compaction
+  drops dead padding" is exactly Heretic-style layer-editing leaving
+  dead blocks (89.8 → 66.7 GiB r2) **[repo-verified lore]**.
+
+### Ablation / abliteration (directional ablation)
+- Arditi et al. 2024, arXiv 2406.11717: refusal is mediated by a single
+  direction; orthogonalizing weights against it removes refusals with a
+  rank-1 edit. Open implementations: `p-e-w/heretic` (automated, above),
+  Artio/abliteration-toolkits, `EleutherAI/repeng`-style rep-overlap
+  variants **[verified (tool existence: heretic; rest survey)]**.
+- Hardware-agnostic fp-editing. On a MoE the edit touches attention/norm
+  weights, so it composes with any expert quantization.
+- Fleet status: frank-large runs an abliterated base **by Frank's choice**
+  (AGENTS: censored-base refusals were the accepted trade of the 27B era);
+  frank-archon's ukisai Swift1.5 is an abliterated finetune of Qwen3.8.
+
+### HauhauCS QX_K_P "Perfect" quants
+- Our frank-secondary publisher. Method, from the publisher's own cards
+  **[verified]**: (1) abliterate + finetune ("Aggressive"/"Balanced"),
+  (2) quantize with **imatrix calibration on the ABLITERATED weights**,
+  (3) K_P = a **per-model, per-tensor mixed-precision profile** —
+  "model-specific analysis to selectively preserve quality where it
+  matters most", e.g. Q8_K_P = a census of Q8_K + F32 + F16 tensors
+  (FriendliAI's safetensor census of a Q8_K_P shows exactly that
+  399/353/99 tensor split). Result: +1–2 quality rungs for +5–15 %
+  size. **It is a standard GGUF** — llama-quantize with per-tensor type
+  overrides (`--tensor-type`), imatrix on; no fork, no custom types
+  (LM Studio just fails to NAME the mixed file, shows "?").
+- Reproducible with stock tooling: `ggml-imatrix` on the target model +
+  `llama-quantize --tensor-type <pattern>=<type>` per the model's
+  sensitivity census. Both binaries already build from
+  `scripts/build-llama-rdna4.sh` **[repo-verified]**.
+- Our own ladder precedent: the fleet's UD-Q8_K_XL (unsloth "Dynamic" =
+  same family: imatrix + importance-tiered per-tensor types) and
+  agentionai FAST imatrix ROCmFP4-FAST (we ran the imatrix step
+  ourselves on the v2 joined-PLE file — the one self-made quant in repo
+  history) **[repo-verified]**.
+
+## Per-card playbooks
+
+### RTX 3090 — the unlocked card (frank-secondary runs llama.cpp BY CHOICE)
+The 3090 is the only fleet device with no format lock: radiance needs
+`.rad`, halogen needs `.hgn`, and the v2 checkpoint loads in NO other
+engine. sm_86 + CUDA is the industry's default target, so the runtime
+menu is open and every self-produced artifact format has a native
+decoder here. This makes the 3090 the natural **quant R&D bench**:
+produce on the host, DECODE AND SCORE on the 3090 against the fleet's
+own KLD/perplexity gates.
+
+Runtime menu on sm_86 (verified against each project's support matrix):
+
+| Runtime | Native quant on Ampere | Notes |
+|---|---|---|
+| llama.cpp CUDA (deployed) | K/I/ML quants, f16 | imatrix + `--tensor-type` K_P ladder |
+| vLLM CUDA | GPTQ INT4 + AWQ via **Marlin** (sm_80/86 native), INT8 W8A8 SmoothQuant, **FP8 as W8A16 weight-only via Marlin** (FP8 W8A8 compute is Ada/Hopper-only) | full OpenAI server, batching |
+| SGLang | GPTQ/AWQ (vLLM kernel lineage); Ampere FP8-via-Marlin tracked upstream | CUDA-first engine |
+| ExLlamaV3 | **EXL3** trellis (QTIP) 2–8 bpw; runs on Ampere incl. the 3090 | the quantizer (`convert.py`) runs on this same card |
+| TensorRT-LLM | INT4 AWQ/GPTQ (FP16 acts), INT8 SmoothQuant; FP8 and INT4-AWQ-with-FP8-acts need SM≥89, NVFP4 is Blackwell | engine-build workflow |
+
+Hardware ceilings that no software unlocks on this card: no FP8 matmul
+hardware (sm_86 < 89), no FP4 (Blackwell). Ampere's fast lanes are
+**INT4 (Marlin/AWQ/GPTQ), INT8 (SmoothQuant), weight-only FP8, f16**.
+
+Playbook A — GGUF ladder (status quo, llama.cpp kept):
+1. Surgery on host/3090: abliterate/prune (Heretic) — torch.
+2. `ggml-imatrix` (CUDA build) over a calibration corpus (our
+   `/home/dank/corpora` style set; ≥1M tokens; the fleet's KLD corpus
+   is a reasonable seed).
+3. `llama-quantize` F16 → target rung; K_P-style `--tensor-type`
+   overrides for attention/KV/first-layer upgrades; `--use-repack` for
+   IQ rungs.
+4. Validate: perplexity + KLD vs BF16 + the K-quant rank checks.
+
+Playbook B — safetensors ladder (the unlocked option): GPTQModel /
+AutoRound / AWQ run CPU-side, emit GPTQ/AWQ/FP8 checkpoints that vLLM
+or SGLang serves on the 3090 with Marlin kernels — same KLD-gate
+discipline as the radiance pipeline. A REAPed + abliterated Flash-Next
+cut to GPTQ-w4 for the 3090 is a bench experiment that needs no new
+hardware; EXL3 (`convert.py` on the 3090, exllama serve) is the
+quality-per-bit extreme of the same menu.
+Caveats before any swap: the serving contract (OpenAI server,
+`reasoning_content`, vision mmproj, compaction-hub window
+200,704 ×2 slots) must be met by the challenger runtime, and the
+24 GB card runs ONE server — benching follows the stop-and-mask law.
+
+#### Worked example — the frank-archon w4dn8 recipe ported to the 3090
+(audit 2026-10-08, target `Qwen3.6-35B-A3B`)
+**The methodology ports; the encoding must be translated.** llama.cpp
+decodes no GPTQ — the recipe re-expresses on GGUF-native terms:
+imatrix on our corpora (replaces the radiance grams; runs the 65 GiB
+BF16 on host CPU), IQ4-class experts, hot-expert protection as
+`--tensor-type "=f16"` patterns over the derive-the-hot-experts law
+(statistic: imatrix importances), Q6_K trunk, KLD/perplexity gate at
+the deployed slot shape. Production stays host-side; the 3090 is only
+the decode target.
+KV is cheap on this arch: 10 full layers × 2 KV heads × 256 =
+**20 KiB/token f16** (3.8 GiB per 200k slot); DeltaNet state is
+per-slot, tens of MiB. Size verdict (weights ≈ 32.2 B experts + 3 B
+trunk):
+- Archon-faithful (w4 experts + bf16 protected + i8 trunk) ≈ 22 GiB →
+  27 GiB with KV+buffers → **over 24; that shape is 2×3090-class.**
+- Fits 1×200,704 text-only: IQ4_XS experts (17.4) + Q6_K trunk + q8_0
+  KV (1.9; `GGML_CUDA_FA_ALL_QUANTS=ON` build) ≈ 21.5 ✓
+- Compaction-hub shape (2×200,704 + mmproj): experts at IQ3_M (14.4) ≈
+  24.0 — the exact Swift IQ3_XXS 2-slot edge, MTP precedent exists.
+- Literal GPTQ-w4 encoding decodes only via vLLM/SGLang Marlin (Playbook
+  B); compressed-tensors expresses the protected set per-module.
+Shelf check: HauhauCS publishes this model's K_P ladder (IQ2_M 10.86,
+IQ4_XS 17.44, Q4_K_M 19.71, Q4_K_P 21.82, Q5_K_P 26.1, Q6_K_P 28.5,
+Q8_K_P 40.6 GiB + mmproj 0.84) — Q4_K_P IS the protected-tensor blend.
+The self-made delta: calibration on OUR corpora, our hot-expert law,
+and a KLD gate scored at the serving shape.
+
+### R9700 — frank-primary (radiance)
+The full self-quant pipeline already exists end to end: fetch BF16 →
+record KLD reference (1.0.13) → capture calibration grams (1.2.0 tap,
+`RADIANCE_CALIB_DIR/TOKENS`) → derive protected experts (energy-share) →
+`convert-swift15-w4dn8.sh` (1.0.13, host backend) → KLD gate (MNBT 2048
+law, pool 40960, mean KL ≤ 0.03, top-1 ≥ 95 %). New model = new recipe
++ new `.so` arch plugin; every step is scripted. Prior art to fold in:
+REAP the expert set BEFORE step 3; ablate BEFORE calibration.
+Native encodings beyond w4: i8 trunk, fp8 HC/PLE/KV, bf16 protected,
+u2 draft — all recipe-expressible (see the w4dn8 runbook).
+
+#### Recipe portability: the w4dn8 shape onto other MoEs (2026-10-08 audit)
+The pin 1.0.13 container ships SIX arch plugins
+(`/opt/radiance/share/radiance/architectures/`): `qwen4exp_fp8`,
+**`qwen35moe_fp8`**, `qwen35_fp8`, `qwen35_bf16`, `llama_fp8`,
+`llama_dense_fp8` (census via `podman run --rm --entrypoint sh <image> -c`
+— the image has no `ls`/`strings`; grep the `.so` files directly, and
+the image lives in ROOTLESS storage as `docker.io/stilldeadcode/radiance`).
+`grep -c` receipts: `qwen35moe_fp8.so` carries the `RADIANCE_CALIB` tap
+(2) and gram export (2) plus MTP (4) and NO ngram (0) — i.e. the full
+calibration→GPTQ path exists for the qwen3.5-MoE family; GPTQ itself
+lives in `rad-convert`, not the plugin (0 hits in both `.so`).
+
+Worked example — `Qwen/Qwen3.6-35B-A3B` (config: `qwen3_5_moe_text`,
+40 layers, `full_attention_interval=4` DeltaNet hybrid, **256 experts
+top-8**, moe_intermediate 512, hidden 2048, MTP ×1, 262k ctx, vocab
+248320, multimodal wrapper with vision tower):
+- Expert set ~32 B params → w4 ≈ 16 GiB; trunk (attn/DeltaNet/embed/
+  lm_head ~3 B) at i8 ≈ 3 GiB ⇒ **~20 GiB total — fully VRAM-resident
+  on ONE R9700** (32 GiB card): no TP=2, no host pool, no disk tier,
+  no `--ngram-placement`, no 47.7 GiB PLE copy, no HC term. The recipe
+  loses three lines (PLE/HC/protected-count scales with 256 experts).
+- Convert cost scales with the file: Flash-Next's 335 GiB took 5 h 50 m
+  host-backend ⇒ ~70 GiB BF16 ≈ 1–1.5 h. KLD reference boot is trivial
+  (BF16 ~65 GiB in host pool beside the pool KV needs).
+- Unresolved before any real campaign: does `qwen35moe` decode the
+  `Qwen3_5MoeForConditionalGeneration` vision wrapper (assume text-only
+  until proven), and does its MTP cast to the fp8/u2 encodings like
+  qwen4exp. The MNBT-2048 disk-tier law is MOOT for a resident model.
+
+### Strix Halo — frank-large (halogen) and friends
+- `.hgn`: **closed production door.** No public quantizer exists
+  (verified absence across peonist-ai repos); the format's lock is the
+  engine's lock. We consume vendor artifacts; the repo-side tooling is
+  compaction/verification only.
+- Self-quant work on this card therefore runs through **GGUF runtimes**:
+  the same imatrix/llama-quantize flow as the 3090 (HIP/Vulkan decode of
+  K/I quants, gufo UD-* precedent, `--use-repack` off on ROCm), or the
+  custom-type forks (ROCmFP4 STRIX, Kairic IU4) whose PRODUCERS we have
+  never been — the quantizers for types 101/103 ship in those forks'
+  trees; building one is a fork build, not a flag.
+- Native-FP4 note: RDNA4/Strix get hardware FP4 matmuls (hipBLASLt
+  MXFP4/DSFP4), which is the engine-level path (radiance/vLLM), not a
+  GGUF decode.
+
+## Sources
+- REAP: arXiv 2510.13999, github.com/CerebrasResearch/reap,
+  cerebras/GLM-4.5-Air-REAP-82B-A12B-FP8, cerebras/GLM-4.7-REAP-268B-A32B-FP8.
+- Heretic: github.com/p-e-w/heretic (AGPLv3).
+- Abliteration: arXiv 2406.11717 (Arditi et al.).
+- K_P: HauhauCS model cards (e.g. `HauhauCS/Gemma-4-E4B-Uncensored-HauhauCS-Aggressive`
+  README "What are K_P quants?"), FriendliAI Q8_K_P tensor census,
+  fleet payload tree (BF16/Q4_K_M/Q6_K/Q8_0 + mmproj ladder in
+  `HauhauCS/Qwen3.5-9B-Uncensored-HauhauCS-Aggressive`).
+- EXL3: github.com/turboderp-org/exllamav3 (QTIP lineage; CUDA; Ampere
+  decode incl. 3090 documented in v1.0 performance write-ups).
+- Ampere capability edges: vLLM FP8 docs ("only Hopper and Ada …
+  W8A8; Ampere supported for W8A16 weight-only utilizing Marlin"),
+  vLLM Supported-Hardware matrix (Marlin GPTQ/AWQ on SM 8.0/8.6),
+  TensorRT-LLM Support Matrix (FP8 and INT4-AWQ-with-FP8-acts need
+  SM≥89; NVFP4 Blackwell), SGLang issue #12887 (Ampere FP8-via-Marlin).
+- Repo: `scripts/convert-swift15-w4dn8.sh`,
+  `scripts/recipes/qwen4exp-swift15-w4dn8-i8-hc8m.recipe`,
+  `scripts/kld-gate-swift15.sh`, `scripts/build-llama-rdna4.sh`,
+  `scripts/build-kairic-edge-gfx1151.sh`, [[agentionai-fast-imatrix]],
+  [[kingjones-strix-rocmfp4]], [[qwen3.8-strix-optimizations]],
+  [[frank-primary-radiance-swift15-w4dn8]],
+  [[frank-secondary-llama-qwen35-9b-q6k-2x200704]],
+  [[frank-large-flash-server-v2-swift15]], [[GGUF Compatibility]].

serverAppConfigs-docs/LLM/RDNA4 Container Serving.md

@@ -247,4 +247,4 @@ see [[dankserver-qwen38-fp8-expert-offload-tuning]] for why it was rejected);
   measured campaigns
 - [[PCIe Link and Atomics]] — why decode is gather-bound and what cannot fix it
 - [[RDNA4 MoE Expert Offload VRAM Budget]] — arena/plan arithmetic deep-dive
-- [[frank-large-benchmark]] and [[frank-small-benchmark]] — window discipline on live hosts
+- [[frank-large-benchmark]] and [[frank-secondary-benchmark]] — window discipline on live hosts

serverAppConfigs-docs/LLM/Running Qwen3.6 35B A3B FP8 on Dankserver.md

@@ -122,7 +122,7 @@ export HF_HOME=/mnt/entropy/huggingface
 
 ### Active Service Conflict
 
-The retired `frank-small` service (llama.cpp) used about 22 GB of the 24 GB VRAM. The current PP worker holds ~23.8 GiB. The `vllm-awq-qwen36` (Podman) and `vllm-qwen36` units are disabled. Before starting any research service, verify that the GPU is free:
+The retired `frank-secondary` service (llama.cpp) used about 22 GB of the 24 GB VRAM. The current PP worker holds ~23.8 GiB. The `vllm-awq-qwen36` (Podman) and `vllm-qwen36` units are disabled. Before starting any research service, verify that the GPU is free:
 
 ```
 nvidia-smi  # verify GPU is free

serverAppConfigs-docs/LLM/VLLM Overview.md

@@ -268,7 +268,7 @@ ad hoc qwen-fp8 container (the same fork, same payload, same TP=2):
 
 **There is no `--ubatch-size` equivalent in vLLM.** `--ubatch-size` is a
 llama.cpp compute-buffer concept (`--batch-size`/`--ubatch-size`); the tcclaviger
-fork has no ubatch knob at all. The llama.cpp compute-buffer pair that frank-small
+fork has no ubatch knob at all. The llama.cpp compute-buffer pair that frank-secondary
 was doubled on (2026-10-05, +4.6 % prefill at 8,928 tokens for 616 MiB) has no
 counterpart here, and the two backends should not be reasoned about by analogy.
 

serverAppConfigs-docs/LLM/dankserver-vram-growth.md

@@ -1,7 +1,7 @@
 ---
 type: reference
 title: Dankserver VRAM and RAM Growth Under Load
-description: Investigation of the recurring cuBLAS crash on frank-small. What grows, what does not, and what to do next.
+description: Investigation of the recurring cuBLAS crash on frank-secondary. What grows, what does not, and what to do next.
 tags:
   - dankserver
   - llamacpp
@@ -18,7 +18,7 @@ timestamp: 2026-09-03
 # Dankserver VRAM and RAM Growth Under Load
 
 This page records the investigation of the recurring cuBLAS crash on
-`frank-small` (llama.cpp, RTX 3090 24 GiB). The service aborts
+`frank-secondary` (llama.cpp, RTX 3090 24 GiB). The service aborts
 with `CUDA error: an unsupported value or parameter was passed to the
 function` in `cublasSgemm_v2` at `ggml-cuda.cu:1541` during
 `update_slots` -> `decode` -> `process_ubatch`. It crashed on 2026-08-13
@@ -116,7 +116,7 @@ Verified 2026-08-14 after deploy: flash attention enabled, warmup clean, decode
 
 ## Mitigation Options
 
-The documentation for the benchmark ([[frank-small-benchmark]])
+The documentation for the benchmark ([[frank-secondary-benchmark]])
 prescribes the first option for this crash class. None of these change
 the model.
 
@@ -135,6 +135,6 @@ PR #25649 (slot checkpoints freed on prompt clear, merged), issue #26119
 
 ## See Also
 
-- [[frank-small-benchmark]]
+- [[frank-secondary-benchmark]]
 - [[Dankserver System Specs]]
 - [[deployment]]

serverAppConfigs-docs/LLM/dealign-ornith-crack.md

@@ -1,7 +1,7 @@
 ---
 type: reference
 title: dealign Ornith-1.5-35B-A3B CRACK on Dankserver
-description: dealignai/Ornith-1.5-35B-A3B-UNCENSORED (CRACK Q6_K) served by upstream llama.cpp on the RTX 3090. MoE model with ngram-mod speculative decoding; selected expert weights offloaded to CPU with an explicit --override-tensor. frank-small.
+description: dealignai/Ornith-1.5-35B-A3B-UNCENSORED (CRACK Q6_K) served by upstream llama.cpp on the RTX 3090. MoE model with ngram-mod speculative decoding; selected expert weights offloaded to CPU with an explicit --override-tensor. frank-secondary.
 tags:
   - llamacpp
   - cuda
@@ -14,12 +14,12 @@ timestamp: 2026-08-24
 
 # dealign Ornith-1.5-35B-A3B CRACK on Dankserver
 
-> **Active 2026-08-25.** `frank-small` serves this model. It
+> **Active 2026-08-25.** `frank-secondary` serves this model. It
 > replaced the CRACK Q8_0 file on the same upstream llama.cpp binary (b10121).
 > The Q6_K file was downloaded from the model repository and validated with
 > `llama-gguf` before installation.
 
-`frank-small` (port 7770) serves
+`frank-secondary` (port 7770) serves
 `Ornith-1.5-35B-A3B-CRACK-Q6_K.gguf` from `dealignai` on Hugging Face. It is
 a **MoE** model (256 experts, 8 active) with a hybrid GatedDeltaNet (SSM) +
 attention architecture, abliterated (uncensored). The GGUF also contains a

serverAppConfigs-docs/LLM/deployment.md

@@ -21,7 +21,7 @@ SearXNG, Caddy, or another container service does **not** restart inference.
 |---|---|---|---:|
 | Dankserver | `dank@dankserver` | `frank-medium` | 7770 |
 | Dankclaw | `dank@dankclaw` | `frank-large` | 7770 |
-| Dankclaw | `dank@dankclaw` | `frank-small` | 7771 |
+| Dankclaw | `dank@dankclaw` | `frank-secondary` | 7771 |
 
 ## Scoped workflow
 
@@ -53,13 +53,13 @@ on the selected host. The following files select the corresponding service:
   checkout, so any file there changes deployed state.
 - `scripts/frank-large.env`, `frank-large.service`, and
   `serve-frank-large.sh` select `frank-large` on Dankclaw.
-- `scripts/frank-small.env`, `frank-small.service`, and
-  `serve-frank-small.sh` select `frank-small` on Dankclaw.
+- `scripts/frank-secondary.env`, `frank-secondary.service`, and
+  `serve-frank-secondary.sh` select `frank-secondary` on Dankclaw.
 - Documentation, tests, deployment tooling, and unrelated container changes
   do not select any inference service.
 
 The target-side `scripts/deploy-remote.sh` installs the selected service.
-`frank-large` and `frank-small` are **root units**: the handler relinks the
+`frank-large` and `frank-secondary` are **root units**: the handler relinks the
 repo file into `/etc/systemd/system/` (`relink_unit`), reloads systemd,
 enables it and restarts it. `frank-medium` is a **rootless Quadlet**: the
 handler retires its predecessors (the replica Quadlets and the retired llama
@@ -67,7 +67,7 @@ root unit), stops the service's own unit, waits for the published port to
 clear, installs the `.container` and restarts the generated unit. Each
 handler then waits on that service's health endpoint —
 `http://127.0.0.1:7770/health` for frank-large and frank-medium,
-`http://127.0.0.1:7771/health` for frank-small. The wait budget is 20 minutes
+`http://127.0.0.1:7771/health` for frank-secondary. The wait budget is 20 minutes
 because cold model loads are slow. A failed handler does not advance its
 per-service state.
 
@@ -76,7 +76,7 @@ per-service state.
 The root units point into the stable target checkout:
 ```text
 /home/dank/serverAppConfigs/scripts/frank-large.service
-/home/dank/serverAppConfigs/scripts/frank-small.service
+/home/dank/serverAppConfigs/scripts/frank-secondary.service
 ```
 `frank-medium` is a Quadlet under `dankserver/frank-medium/quadlet/`
 (`frank-medium.container`) rather than a unit in `scripts/`.
@@ -110,7 +110,7 @@ change in that service's inputs.
 
 If the LLM handler fails after the target pull:
 
-1. Inspect `journalctl -u frank-large -n 200` or `journalctl -u frank-small
+1. Inspect `journalctl -u frank-large -n 200` or `journalctl -u frank-secondary
    -n 200` (root units), or `journalctl --user -u frank-medium -n 200`
    (rootless Quadlet).
 2. Confirm the model files, the runtime binary and (for frank-medium) the
@@ -120,13 +120,13 @@ If the LLM handler fails after the target pull:
    ```bash
    ./scripts/deploy.sh dankserver --service frank-medium
    ./scripts/deploy.sh dankclaw --service frank-large
-   ./scripts/deploy.sh dankclaw --service frank-small
+   ./scripts/deploy.sh dankclaw --service frank-secondary
    ```
 4. Verify the matching health endpoint before declaring recovery.
 
 Do not stop the other host's inference service during recovery. The two
 Dankclaw endpoints are independent processes on different devices: restarting
-frank-large does not disturb frank-small, and the reverse holds too — with one
+frank-large does not disturb frank-secondary, and the reverse holds too — with one
 exception worth knowing, that the deployer waits for port 7770 to be released
 before it starts the replacement container. For benchmark work, follow the
 separate stop/benchmark/restart procedure in [[Testing Policy]] and the

serverAppConfigs-docs/LLM/frank-large-flash-server-v2-swift15.md

@@ -0,0 +1,194 @@
+# frank-large — halogen-flash-server 0.16.2, Qwen3.8-Flash-Next v2 Swift1.5 ABLITERATED (Dankclaw, Strix Halo iGPU)
+
+**ACTIVE since 2026-10-06.** Rollback = revert the swap commit's trio
+(`scripts/frank-large.env`, `scripts/serve-frank-large.sh`,
+`scripts/frank-large.service`); the retired 27B payload stays on disk at
+`/home/dank/models/halogen-qwen3.8-27b/` and its fetch script
+(`scripts/download-frank-large-halogen.sh`) stays in the repo.
+
+## Why this shape exists
+
+Frank's ask: replace the frank-large model with
+`https://huggingface.co/Quat3rnion/halogen-swift1.5-qwen3.8-flash-next-v2-abliterated`.
+That payload is an **abliterated Swift1.5 finetune of peonist's
+Qwen3.8-Flash-Next v2 checkpoint in the v2 `.hgn` format** — a file that
+loads ONLY in the **halogen-flash-server** engine line, NOT in the halogen
+0.1.4 container that served the 27B dense checkpoint. The model move is
+therefore a **full engine migration**, and the accepted-product facts flip
+twice in the fleet's favour:
+
+- the base is **abliterated** (the 27B halogen endpoint was a finetune of
+  the censored `Qwen/Qwen3.8-27B`; refusals were an accepted regression there),
+- **vision is back** — the v2 vision tower loads beside the checkpoint and
+  the engine runs it (the 27B halogen engine silently ignored its encoder),
+- **MTP speculative decoding returns** with the head inside the checkpoint
+  itself (the 27B shipped a DFlash2 drafter instead; comparable purpose,
+  different file plumbing),
+- the card claims **53–57 t/s greedy decode, ~1,640 t/s prefill at 34k, ~72 %
+  fewer thinking tokens** than the censored 27B (905 mean), and **262,144
+  native context**. Publisher-card numbers — verify against live traffic
+  before quoting further (see Verification).
+
+The engine is **closed-source**, ships as `ghcr.io/peonist-ai/
+halogen-flash-server:0.16.2` (the version the model card's quickstart pins),
+digest-pinned:
+
+```
+ghcr.io/peonist-ai/halogen-flash-server@sha256:0c61bf84ac22308a53f5d1ca6b86806702d7039e5ebc51cae4c66621b92fe04a
+```
+
+(3.6 GB image; RepoDigest captured at pull 2026-10-06. Card-tested fallback
+if a future pin misbehaves: 0.15.1.)
+
+## The payload — a triangle of files, all sha256-verified at fetch
+
+| File | Bytes | Source repo @ revision | sha256 (LFS) |
+|---|---|---|---|
+| `qwen38-flash-next-v2-swift15-abliterated.hgn` | 66,687,678,464 | Quat3rnion/halogen-swift1.5-qwen3.8-flash-next-v2-abliterated @ main (`69a85db`) | `9c880b8ba48deabfa06172eff1e7572e4fe465e09e97faec6e50c3f6fdcac037` |
+| `qwen38-flash-next-ngram.hgn` (REQUIRED) | 51,200,246,144 | peonist-ai/halogen-qwen3.8-flash-next @ `5cc17cea` | `3450acada94e19aabad88bd49b45eb70304820949c2240fc178067b20ae5dffe` |
+| `qwen38-flash-next-vision.hgn` (enabled) | 897,916,416 | peonist-ai/halogen-qwen3.8-flash-next @ `e053f488` | `d62e0ae553fe88afd3833733d4a4c669f34d20fd8dfce4b9610525bed2134b10` |
+| `tokenizer/` (6 files, flat dir) | 22.4 MB total | peonist @ `e053f488` | `tokenizer.json` is LFS `0997f410…` (byte-identical to the 27B-era copy); the rest are plain git blobs, size-checked |
+
+Payload notes worth keeping:
+
+- The deployed checkpoint is a **compacted r2** of the card's append-only
+  original (89,775,868,032 B → 66,687,678,464 B): the abliteration workflow
+  appends and the repo's `tooling/` compact drops dead padding; live tensors
+  are byte-identical, the LFS sha256 is the contract, and the 24 GiB saving
+  is why it fits.
+- **`HALOGEN_MTP_HEAD` stays UNSET**: the MTP head is INSIDE the v2
+  checkpoint (the separate `qwen38-flash-next-mtp.hgn` sidecar is for a
+  different assembly and is deliberately not fetched).
+- The **n-gram lookup table is REQUIRED**. The engine reads it beside the
+  checkpoint (documented fallback); the card's quickstart also names it
+  explicitly via `HALOGEN_NGRAM_TABLE`, and the serve script passes both —
+  the env var is belt-and-braces over the documented beside-the-checkpoint
+  default, and both mechanisms resolve to the same file.
+- The tokenizer is fetched fresh from peonist @ `e053f488` into
+  `/home/dank/models/halogen-flashnext-v2/tokenizer/` — same flat-directory,
+  no-symlink law as every other payload (an HF cache snapshot's symlinks
+  dangle inside the container mount; both the serve gate and the deployer
+  refuse them).
+
+Fetch: `scripts/download-frank-large-flashnext.sh` (`--verify-only` to
+re-check; `-f` + exact byte counts + LFS sha256 + resolve-the-CDN-then-`-C -`
+resume law throughout; no token — the repos are public).
+
+## Shape — the knobs that are not free
+
+`scripts/frank-large.env` carries the full rationale inline; the table:
+
+| Env | Value | Why |
+|---|---|---|
+| `HALOGEN_CTX` | 262144 | per-slot HARD ceiling (the checkpoint's native window; a longer prompt is a 400 naming the limit, never truncation). **RAISED from 200192 on 2026-10-07 — the 400-zone law:** the harness clamps `max_tokens = min(45056, contextWindow − est − 4096)`, its chars/4 est UNDER-counts the rendered prompt by ~3 % (measured twice: est 152,938 → server 157,388, request over room by 354; est 158,906 → server 165,301, over by 2,300), and compaction only fires at 85 % of the harness window — so with window == engine ceiling, every request in the est ~146k–184k zone was a guaranteed 400. Harness window 217,088 = ceiling − 45k absorbs the error: worst-case request ≈ 219k ≤ 262,144 (≈10 % under-count headroom) |
+| `HALOGEN_KV_SLOTS` | 2 | the 27B engine's "slots>1 disables the drafter" law does NOT carry over — on this engine batching and speculation coexist (the card's quickstart ships 2 slots WITH the head). 2 slots = concurrent capacity |
+| `HALOGEN_KV_POOL_POSITIONS` | 524288 | explicit form of the default (2 × ctx), so the seam test pins the product; ~28 KiB/position ⇒ 14.0 GiB KV |
+| `HALOGEN_HOST_RESERVE_GIB` | 20 | keeps the host honest (the w4b-era lesson) |
+| `HALOGEN_SPEC_ADAPT` | **0** | MANDATORY (card): the default adaptive policy turns the MTP head off for whole requests after low acceptance and **halves decode**. The head stays on; temp-1.0 traffic pays some acceptance for it — accepted trade |
+| `HALOGEN_MAX_TOK` / `HALOGEN_MAX_TOKENS_CAP` / `HALOGEN_QUEUE_TIMEOUT` | 8192 / 65536 / 7200 | 8192 is only the per-request DEFAULT (the harness sends at most its `maxTokens` 45,056); the cap/timeout pair is COUPLED per FLAGS.md (65536 at ~10 t/s serial needs > 6550 s) |
+| `HALOGEN_TEMPERATURE` / `TOP_P` / `TOP_K` | 1.0 / 0.95 / 20 | the card's thinking recipe as SERVER-SIDE defaults — the engine HAS a sampler now, so an absent temperature is no longer greedy; models.yml extraBody matches it (consistent, not load-bearing) |
+| `HALOGEN_WEIGHTS_LOCK` | 1 | card's belt-and-braces; the engine pins weights by default on v2 (`HALOGEN_FLASH_PIN_TRUNK=0` is a w4b-only knob and is REFUSED by v2) |
+| `HALOGEN_MODEL_ID` / publish | **frank-dragoon** / 7770:8731 | served id renamed 2026-10-09 (service/unit/env keep `frank-large`); engine port 8730 never published |
+| MTP depth | default 2 | FLAGS.md: 2 is best all-round, 3 for code — Frank's traffic is agent/prose |
+
+**Memory ledger** (121.2 GiB unified, the referee is the boot ledger):
+weights ~62 GiB resident + KV 14.0 + arena ~8 (`3.8 + 8.4 × 8192/16384`)
++ vision ~2 ≈ **86 GiB engine-side**, + 20 host reserve = ~106 with
+~15 GiB margin over the box's baseline co-tenancy. The 51.2 GB n-gram
+table rides as mmap'd page cache beside the checkpoint — the card's own
+quickstart arithmetic does not bill it as resident, but WATCH the ledger
+at boot and re-size if the engine prints a clamp. Prompt cache is ON by
+default (`PROMPT_CACHE=2`): a follow-up turn at 100k ctx costs ~2 s
+instead of ~88 s cold — the big-TTFT win the 27B engine needed a manual
+`HALOGEN_CACHE_MB` pin for is built in here.
+
+## Engine contract facts (FLAGS.md of the flash-server repo)
+
+- OpenAI chat **and** `/v1/responses` **and** `/v1/messages` **and**
+  `/v1/completions`; tool calls verified on the card; `response_format`
+  still unsupported (no constrained decoding).
+- Thinking → **`reasoning_content`** (same field as every engine this
+  endpoint has run); the v2 chat template rejects `reasoning_effort`
+  outside `xhigh|medium|low` — the models.yml fold (high→xhigh) stays
+  mandatory.
+- Vision: OpenAI `image_url` content parts only (the fleet's standard
+  contract; the legacy top-level `images` field is the frank-secondary trap).
+  ≥256 px probe images or the model answers "video".
+- `HALOGEN_MAX_TOKENS_CAP` over-cap is a 400 naming the limit — never a
+  truncation. `max_tokens` INCLUDES thinking.
+- All flags read once at startup; every flag is BITWISE except
+  `HALOGEN_CK_OVERLAY`, `HALOGEN_MATMUL_TUNING_FILE`,
+  `HALOGEN_INDEXER_BUDGET`, `HALOGEN_COMPOSABLE_CONTEXT`. Unset
+  `HALOGEN_CHECKPOINT` defaults to `/models/qwen38-flash-next-v2.hgn`
+  with the lookup table beside it.
+- Prompt-lookup drafting (3,3 window) rides beside the MTP head by
+  default and is byte-identical to greedy verification where it fires.
+- Watchdog: `HALOGEN_ENGINE_WATCHDOG_S=180` with defer-logic for memory
+  stalls — on a shared host the defaults are the safe choice; the
+  restart policy stays real through the root unit's `Restart=always`.
+
+Kernel: `CONFIG_HSA_AMD_SVM=y` (CachyOS ships it); `amdgpu.noretry=-1`
+(auto) — for this IP auto resolves to retry-on, the same effective state
+the 27B engine ran on. Only revisit if the ledger shows SVM complaints.
+
+## Mechanics
+
+- **Root systemd unit** (`scripts/frank-large.service`, `User=dank`,
+  `LimitMEMLOCK=infinity`, `Restart=always`, `ExecStop=podman stop -t 60
+  frank-large-halogen`), driven by `scripts/serve-frank-large.sh` — the
+  fleet's `podman run`-direct pattern. The script runs the container
+  FOREGROUND so the boot ledger (weights resident, pool fit, MTP head,
+  prompt cache, any clamp) lands in `/home/dank/logs/frank_large.log`.
+- Serve gates mirror the deployer EXACTLY (2026-10-01 crash-loop law):
+  image digest in podman storage, byte-exact checkpoint + n-gram table +
+  vision tower, flat tokenizer with all six files as regular files,
+  GPU nodes present.
+  - **Host-RAM pin-gate preflight** (added 2026-10-06): the engine
+  refuses to pin when `MemAvailable` is below the checkpoint (62.11 GiB)
+  plus a **16 GiB floor** — pinned pages cannot be swapped, and under the
+  floor the n-gram lookup pages in from disk on every long prompt. A
+  RAM-short host crash-loops the unit (each boot dies in ~5 s at the pin
+  gate) until the RAM clears; the 2026-10-06 cutover hit this (8 boots,
+  MemAvailable 74.77 GiB vs the 78.11 GiB needed, self-resolved in ~2 min
+  when the dying 27B engine freed RAM). The serve script now checks
+  `MemAvailable` BEFORE `podman run` and WAITS up to `FRANK_LARGE_RAM_WAIT`
+  (default 600 s) for the RAM to clear, so a transient dip boots once
+  instead of crash-looping; if it never clears it refuses loudly. The 16
+  GiB floor is the engine's (not `HALOGEN_HOST_RESERVE_GIB=20` — a
+  separate, higher, overall-budget knob).
+- Engine stdout goes to the log, NOT the journal. The FIRST request after
+  a (re)start can sit silent ~45–60 s on this engine class (health
+  `engine_unresponsive`, 503s) and self-recovers — do not restart over it.
+
+## Verification checklist (run after any redeploy)
+
+1. Boot ledger greps in `/home/dank/logs/frank_large.log`: resident
+   weights (~62 GiB), KV pool line, MTP/drafter line, prompt-cache line,
+   **no clamp named**.
+2. `curl http://192.168.0.143:7770/health` → 200.
+3. Text completion probe: reasoning lands in `reasoning_content`,
+   `timings` honest (generate ≥128 tokens; at 8 the inter_token number is
+   first-token noise).
+4. Vision probe: a 512×512 solid-red PNG through an `image_url` part →
+   "Red" (≥256 px, one word expected).
+5. Spec decode: `draft_n`/`draft_n_accepted` present in timings on
+   real-shaped traffic (SPEC_ADAPT=0 means the head is always engaged).
+
+## Client contract (harness)
+
+`~/.omp/agent/models.yml` — provider `llamacpp (dankclaw)` @
+`http://192.168.0.143:7770/v1`: `contextWindow: 217088` — deliberately
+BELOW the 262,144 engine ceiling: the 45k gap absorbs the harness's
+~3 % chars/4 est under-count (see the `HALOGEN_CTX` row above) —
+`maxTokens: 45056`, `input: [text, image]`,
+`reasoningContentField: "reasoning_content"`, the `high→xhigh` effort
+fold, `extraBody` temperature 1.0 / top_p 0.95. Compaction still rides
+frank-secondary (the `compactionModel` steering is unchanged; the input
+≈ 170k fits its 200,704 slot).
+
+## Rollback
+
+Revert the swap commit and redeploy — the 27B trio is intact on disk
+(payload at `~/models/halogen-qwen3.8-27b/`, fetch script, and the halogen
+0.1.4 image digest still in podman storage). The older ROCmFPX rollback
+door (`build-rocmfpx.sh` pin) is untouched.

serverAppConfigs-docs/LLM/frank-large-halogen27b-dflash2.md

@@ -25,7 +25,7 @@ confirmed by Frank before deploy:
 
 1. **Text-only.** halogen loads the language tower only; README "honest
    limits": *"The vision encoder is ignored; image inputs are not supported."*
-   The mmproj-era vision role moves wholly to frank-small.
+   The mmproj-era vision role moves wholly to frank-secondary.
 2. **Censored base.** The checkpoint is a finetune of the censored
    `Qwen/Qwen3.8-27B` base, built to make its DFlash2 head strong. The
    ROCmFPX AGENT file was an *Uncensored* build; refusals can return.
@@ -54,6 +54,15 @@ bearing numbers:
   set + host baseline on the 121.2 GiB box. The engine sizes the pool itself
   and **prints a boot ledger naming any clamp** — the post-deploy check reads
   that ledger, not a formula.
+- **`HALOGEN_CACHE_MB=16384` (pinned 2026-10-06)** — auto is OFF on this
+  box: the floor is 2 x a full-context entry (36.9 GB at 262k; one entry
+  is ~13.7 GB at our 200,700-slot x ~70 KB/token) and the ledger had
+  35.7 GB free, so the engine refused and every deep agent request
+  re-prefilled its whole 79-81k context — 84.9-179.8 s measured
+  (serve_api lines, 2026-10-06) for ~500-650 new tokens per turn. The
+  README prices the cache at ~20x time-to-first-token on a warm turn;
+  entries evict LRU by bytes; the reserve (HALOGEN_CACHE_RESERVE_MB,
+  default 8192) is re-checked against live MemAvailable on every store.
 - **No server-side sampler:** an absent temperature is GREEDY. The card's
   thinking recipe (temp 1.0 / top_p 0.95) lives in `models.yml` `extraBody`
   — on the wire only. The template defaults `reasoning_effort` to **xhigh**
@@ -74,7 +83,7 @@ bearing numbers:
 |thinking field|`reasoning_content` (same as before)|
 |`reasoning_effort`|same Qwen3.8 template; outside {xhigh,medium,low} rejected — models.yml fold stays; unset defaults to xhigh, `none` disables thinking|
 |tools|server-side tool calls advertised — round-trip MUST be re-probed after deploy|
-|image input|ignored/error — route to frank-small|
+|image input|ignored/error — route to frank-secondary|
 
 ## Deploy mechanics
 
@@ -94,14 +103,18 @@ bearing numbers:
   those links dangle inside the container mount.
 - **Unit:** `scripts/frank-large.service` (root, `User=dank`, relinked into
   `/etc/systemd/system/`), `ExecStart` = serve script, `ExecStop` = `podman
-  stop -t 120 frank-large-halogen`, script `exec podman wait` so
-  `Restart=always` is real. Engine stdout goes to
-  `/home/dank/logs/frank_large.log` (the boot ledger lives there, not in the
-  journal).
+  stop -t 120 frank-large-halogen`, script runs `podman run` FOREGROUND
+  (2026-10-06: the 2026-10-05 `-d` + `exec podman wait` shape captured
+  only the container ID — the engine ledger existed nowhere on disk and
+  the post-deploy grep below read a one-line hex file; foreground run
+  returns the container's exit code so `Restart=always` is real and the
+  ledger lands in the log). Engine stdout goes to
+  `/home/dank/logs/frank_large.log` (the boot ledger lives there, not in
+  the journal).
 - **Cutover:** `deploy_llm_large` in `scripts/deploy-remote.sh` sweeps the
   legacy bare `llama-server --model /home/dank/models/kingjones-*` process and
   the legacy `frank-large` container name by NAME (never `pkill -f
-  llama-server` — that hits frank-small), retires the old quadlet if the 2026-09
+  llama-server` — that hits frank-secondary), retires the old quadlet if the 2026-09
   halogen era's unit lingers, waits port 7770 free, relinks, health-gates.
 - **Bench law:** stop+mask the unit before any GPU experiment on this box
   (AGENTS.md); `/health` answers 503 while the 33.4 GiB payload loads — that
@@ -109,8 +122,15 @@ bearing numbers:
 
 ## Verification (post-deploy checklist)
 
-1. `grep -iE 'clamp|kv|pool|drafter|dflash' /home/dank/logs/frank_large.log`
-   — ledger shows the pool at the asked shape, DFlash2 active, NO clamp line.
+1. `grep -iE 'clamp|kv|pool|drafter|dflash|cache' /home/dank/logs/frank_large.log`
+   — ledger shows the pool at the asked shape, DFlash2 active, the
+   prompt cache line naming the pinned budget, NO clamp line. EXPECT the
+   WARNING "smaller than ONE full-context entry (18.4 GB at
+   kMaxCtx=262144)": it sizes against the NATIVE window, not our
+   200,700 slot — the deepest possible entry (~14 GB at ~70 KB/token)
+   fits the 16 GB pin, and the 262k entry it fears cannot exist (over
+   slot is a 400). Probe-proven 2026-10-06: the warm turn hit with
+   cache_n=16,384.
 2. `curl -s http://192.168.0.143:7770/health` -> 200.
 3. Real completion with `reasoning_effort` + temp/top_p from the wire recipe;
    `reasoning_content` populated; decode rate from the timings.
@@ -120,6 +140,56 @@ bearing numbers:
    incl. a tool call (V1TOOLS).
 6. Prompt > 200,700 tokens returns the 400 naming the limit (contract test).
 
+## Tuning notes (2026-10-06)
+
+Measured on real agent traffic (serve_api lines) the day after cutover:
+
+- **Decode is depth-bound, and the KV path is the reason.** Shallow
+  prompts (< 1k tokens) decode 30-50 t/s at commit 4-6.3/round; 46k runs
+  5.5, 63-67k runs 4.1-6.5, 79-81k runs 3.3-3.8. The round-time slope is
+  ~8 us/token of context: the KV pool reads at ~8 GB/s effective while
+  the weights stream ~180-240 GB/s (the engine's README puts batch-1
+  weight streaming at the 240 GB/s hardware wall). Commit also falls
+  from 4-6 to 2.5-3 accepted/round as the conversation grows.
+- **No supported flag touches decode.** `docs/FLAGS.md`: HALOGEN_W4A4 /
+  W4A4_EXCL are prefill-only ("decode is unaffected either way"); the
+  undocumented kernel levers are declared unsupported and their defaults
+  are the measured winners. 0.1.4 is the latest release (CHANGELOG
+  head; kernels unchanged since 0.1.0). The publisher's tg bench never
+  measured depth — its tg rows run over the ten SHORT eval prompt
+  shapes — so the README's "decode barely degrades at depth" claim was
+  never exercised at 46-80k.
+- **A/B'd the one FA lever with public precedent (2026-10-06 stop-window
+  probe): rejected.** Issue #12's `HALOGEN_FA_TQ=256` (the 64 GB-carve
+  crash workaround) vs the default tile — same 80k filler prompt,
+  128-token essays, the runtime's own `timings`: decode 6.35 vs 6.34 t/s
+  cold and 5.74 vs 5.76 warm (±0.3 % = noise; draft acceptance identical,
+  53/74 and 58/69), while prefill LOST 9.5 % on this topology (447.5 ->
+  404.8 t/s). The depth collapse is not an FA tile artifact — it sits
+  deeper than the knob. The engine binary advertises 40+ undocumented
+  `HALOGEN_*` kernel levers (FA/DN/MATMUL families, readable via
+  `grep -aoE "HALOGEN_[A-Z0-9_]+"` on the binary); `FA_TQ` is the only
+  one with public evidence, and it moved nothing that matters here.
+- **Routing is the applied mitigation (2026-10-06):** the harness's
+  deep-conversation roles (`slow`, `advisor` in `~/.omp/agent/config.yml`)
+  moved to frank-primary (radiance on Dankserver: fp8 KV at
+  7.32 KiB/token = 8.7x less KV to read per round at depth, MTP depth 3,
+  200k window, prefix-cache host+disk tiers). frank-large keeps OpenWebUI
+  and shallow bursts, where it runs 30-50 t/s. frank-primary's
+  depth-decode rate is designed-for but not yet measured on record.
+- **The lever that existed was the prompt cache, and it was OFF** (see
+  the shape bullet). The pin turns the 85-180 s re-prefill per deep
+  turn into a resume; decode itself is expected UNCHANGED by the pin —
+  the cache resumes prefill, it does not touch the round loop.
+  Probe-proven (two synthetic 17.3k turns, 2026-10-06): cold prefill
+  27.0 s (640 t/s, cache_n 0) -> warm turn cache_n 16,384, prompt_n 946
+  (the extension + one 2048-aligned chunk), prefill 1.7 s — 16x on the
+  pair, answer correct warm.
+- **Everything else stays put.** dflash2 over mtp (publisher 31.71 vs
+  26.88 mean; at depth both pay the same KV term and dflash2's higher
+  commit wins), KV_SLOTS=1 (batch XOR speculation), SLOT_CTX=200700
+  (Frank's window), W4A4 shipped (+9 % prefill, decode unharmed).
+
 ## Rollback
 
 Revert the frank-large swap commit and redeploy `dankclaw --service frank-large`:

serverAppConfigs-docs/LLM/frank-large-rocmfpx-q6agent-uncensored-mtp.md

@@ -148,7 +148,7 @@ in [[frank-large-rocmfpx-q8agent-mtp]] and still applies.
 
 Sweep key for scratch servers is now the payload name:
 `pkill -f 'Qwen3.8-27B-Uncensored-Q6_0_ROCMFPX_AGENT'` — never `pkill -f
-llama-server` (frank-small is also one).
+llama-server` (frank-secondary is also one).
 
 ## Operations
 

serverAppConfigs-docs/LLM/frank-large-rocmfpx-q8agent-mtp.md

@@ -162,7 +162,7 @@ Three traps, all hit for real:
    enough for the vision probe).
 2. **`MTP boundary missing for seq_id=… ; resyncing after a non-token step`**
    appears in the log under draft resets. Self-recovering; not an outage signal.
-3. **Never `pkill -f llama-server` on Dankclaw** — frank-small is also a
+3. **Never `pkill -f llama-server` on Dankclaw** — frank-secondary is also a
    `llama-server`. The sweep key here is the payload name:
    `pkill -f 'Qwen3.8-27B-Q8_0_ROCMFPX_AGENT'`.
 

serverAppConfigs-docs/LLM/frank-large-vllm-mimo-v26-bf16.md

@@ -35,7 +35,7 @@ timestamp: 2026-10-01
   (`scripts/frank-large.service`, `User=dank`, `Restart=always`,
   `ExecStop=podman stop -t 120`, `TimeoutStopSec=180` > the stop grace).
 - **Model:** `XiaomiMiMo/MiMo-V2.6-Distill-Qwen-9B` in **full BF16**
-  (17.53 GiB, 4 shards) — the same checkpoint frank-small serves as an INT8
+  (17.53 GiB, 4 shards) — the same checkpoint frank-secondary serves as an INT8
   W8A16 AutoRound conversion on the 3090, here unquantized.
 - **Draft:** `Qwen/Qwen3.5-9B` (18 GiB on disk; only the 15 `mtp.*` tensors
   are loaded into the draft pass).
@@ -82,7 +82,7 @@ Qwen3.5-9B that preserves the hidden-state geometry — identical
 per-position acceptance is the direct measurement that the distillation
 preserved more than geometry. MiMo itself ships **ZERO `mtp.*` tensors**
 (verified in both the BF16 base and the INT8 conversion) — which is exactly
-why frank-small's SGLang NEXTN draft is a 0.00-acceptance ghost: SGLang
+why frank-secondary's SGLang NEXTN draft is a 0.00-acceptance ghost: SGLang
 reads the head from the TARGET checkpoint, vLLM reads the draft from a
 separate path. That difference is the whole trick:
 
@@ -183,4 +183,4 @@ unit/env/serve trio and redeploying returns the 27B endpoint.
 - Engine image: https://hub.docker.com/r/kyuz0/vllm-therock-gfx1151
 - Serve script: `scripts/serve-frank-large.sh`; tunables: `scripts/frank-large.env`
 - Predecessor: [[frank-large-gufo-qwen38-27b]]; sibling on the same arch:
-  [[frank-small-sglang-mimo-v26]]
+  [[frank-secondary-sglang-mimo-v26]]

serverAppConfigs-docs/LLM/frank-medium-batch-ubatch.md

@@ -211,7 +211,7 @@ re-checking that 35:30 and 37:28 still fail so the gate keeps discriminating.
 
 - [[frank-medium-llamacpp-dflash2]] — the deployment this tuned, and its split
   history
-- [[frank-small-benchmark]] — the same sweep on a CUDA 3090, where a 16k prompt
+- [[frank-secondary-benchmark]] — the same sweep on a CUDA 3090, where a 16k prompt
   at depth 0 made prefill climb all the way to ubatch 2048. Depth is the
   difference: with no KV to attend over, per-pass overhead dominates and a
   bigger micro-batch wins; at 64k of resident KV it does not.

serverAppConfigs-docs/LLM/frank-medium-llamacpp-dflash2.md

@@ -312,7 +312,7 @@ came up healthy and *text-only*: image requests answered 500 "image input is
 not supported — hint: … provide the mmproj" and the load log had no mmproj
 line, so `llama-server` fb27a525 does not pick up `mmproj*.gguf` from the
 model directory. The serve script now passes `--mmproj` explicitly (the
-frank-small pattern), refuses to start without the file when vision is on, and
+frank-secondary pattern), refuses to start without the file when vision is on, and
 the deploy handler's preflight checks for it. Verified end-to-end after the
 fix: a generated 64x32 test image was described from its pixels — with
 thinking on the model reasoned about "the three horizontal bars" (its reading
@@ -724,7 +724,7 @@ fleet.
   2026-09-15, whose `--split-mode layer` measurements this service reuses
 - [[Dual-R9700-Cooperation]] — why the two cards cannot take part in a
   collective, and every software escape that is closed
-- [[frank-small-qwen38-gsq-rco]] — the fleet's other llama.cpp endpoint (RTX
+- [[frank-secondary-qwen38-gsq-rco]] — the fleet's other llama.cpp endpoint (RTX
   3090, MTP, the image path)
 - [[Dankserver System Specs]] — hardware inventory
 - [[frank-medium-batch-ubatch]] — the batch/ubatch sweep that set the deployed

serverAppConfigs-docs/LLM/frank-medium-rocmfpx.md

@@ -144,7 +144,7 @@ ggml/src/ggml-backend.cpp:119: GGML_ASSERT(buffer) failed
 
 First deploy (2026-09-15) also ran `retire_root_llm_small()` in
 `scripts/deploy-remote.sh`: the CUDA-era `frank-small` unit was stopped,
-disabled and moved to `~/.AGENTTRASH/20260915-frank-small-retire/host-units`
+disabled and moved to `~/.AGENTTRASH/20260915-frank-secondary-retire/host-units`
 (the host's `systemctl disable` has been observed to delete unit files,
 2026-08-26 incident, so both outcomes are tolerated).
 

serverAppConfigs-docs/LLM/frank-npu.md

@@ -55,9 +55,9 @@ image path is halogen's vision tower on `frank-large`.
 | consumer | setting | now |
 |---|---|---|
 | Hermes | `auxiliary.vision` | `dankclaw-large` / `frank-large` (halogen vision tower) |
-| Hermes | `auxiliary.title_generation` | `dankserver-small` / `frank-small` |
+| Hermes | `auxiliary.title_generation` | `dankserver-small` / `frank-secondary` |
 | harness (`~/.omp/agent/models.yml`) | `modelRoles.vision` | `llamacpp (dankclaw)` / `frank-large` |
-| harness | `modelRoles.tiny` | `llamacpp (dankserver)` / `frank-small` |
+| harness | `modelRoles.tiny` | `llamacpp (dankserver)` / `frank-secondary` |
 | harness | `modelRoles.task` | `llamacpp (dankclaw)` / `frank-large:medium` |
 
 The `flm (dankclaw-npu)` provider block was removed from the harness model

serverAppConfigs-docs/LLM/frank-primary-freetoken-pr587.md

@@ -1,6 +1,10 @@
 # frank-primary — FreeToken PR #587 ROCm (Qwen3.8-Flash-Next-Uncensored-FP8, 1× R9700)
 
-**Status: ACTIVE 2026-10-05 →** · **Host:** Dankserver (192.168.0.235) · **Port:** 7770 · **GPU:** 1× Radeon AI PRO R9700 (gfx1201) — the second R9700 sits idle
+**Status: RETIRED 2026-10-05 → (superseded the same day by the radiance
+cutover — see [[frank-primary-radiance-qwen38-native-fp8]]; kept as the
+rollback runbook: the image + orcarouter payload stay on disk until the
+window closes)** · **Host:** Dankserver (192.168.0.235) · **Port:** 7770 ·
+**GPU:** 1× Radeon AI PRO R9700 (gfx1201) — the second R9700 sits idle
 
 The tcclaviger RDNA4 vLLM fork was replaced by **FreeToken at PR #587 head
 `77a7201f98f00d93f618c6cf8b81ffe8360e0899`** (branch `rocm714_torch214`), built

serverAppConfigs-docs/LLM/frank-primary-llamacpp-qwen38-flash-next-q8.md

@@ -254,7 +254,7 @@ refetch.
 
 - [[frank-large-unsloth-qwen38-27b-q8xl]] — Dankclaw :7770, the same
   b11030-mix binary family on gfx1151.
-- [[frank-small-gemma4-12b]] — Dankclaw :7771, mainline llama.cpp on the
+- [[frank-secondary-gemma4-12b]] — Dankclaw :7771, mainline llama.cpp on the
   3090; the only audio-capable endpoint.
 - The harness contract lives in `/home/blank/.omp/agent/models.yml`
   (`llamacpp (dankserver)` provider, `contextWindow 262144`,

serverAppConfigs-docs/LLM/frank-primary-r9v-radiance-qwen38-q8.md

@@ -55,7 +55,7 @@ would drift from the qualification — don't.
 ## Spec decode: MTP4 only — the ngram-mod ask is NOT expressible
 
 llama.cpp chains drafter types (`--spec-type draft-mtp,ngram-mod` on
-frank-small/large). The fork's `speculative_config` is a **single dict**
+frank-secondary/large). The fork's `speculative_config` is a **single dict**
 (one method per engine): `mtp`, `ngram`, `ngram_gpu`, `dflash`, … are
 mutually exclusive — no chaining seam exists in the fork. What IS active:
 

serverAppConfigs-docs/LLM/frank-primary-radiance-qwen38-native-fp8.md

@@ -0,0 +1,297 @@
+# frank-primary — Radiance (StillDeadcode), Qwen3.8-Flash-Next-FP8 NATIVE, 2× R9700 TP=2
+
+**Status: ACTIVE 2026-10-05 →** · **Host:** Dankserver (192.168.0.235) · **Port:** 7770 · **GPU:** 2× Radeon AI PRO R9700 (gfx1201), TP=2
+
+The FreeToken PR #587 endpoint was replaced by the **radiance engine** (C++/HIP,
+libr4d kernels for gfx1201, no Python in the serving path) driving the
+digest-pinned image `stilldeadcode/radiance@sha256:4c3f484564bb2872784e4…`
+(v1.0.7 line, 287 MiB, `libr4d@0.5.0`) as a plain `podman run` through
+`scripts/serve-frank-primary.sh` under the **root systemd unit**
+(`frank-primary.service`, `User=dank`, `LimitMEMLOCK=infinity`). Same port,
+same served alias (`frank-primary`), same container identity
+(`frank-primary` = the unit name). Rollback: git tag
+`frank-primary-freetoken-pr587-20261005` (FreeToken trio + Dockerfile), image
+`localhost/frank-primary-freetoken:pr587-77a7201`, payload
+`/home/dank/models/Qwen3.8-Flash-Next-Uncensored-FP8`.
+
+## Why radiance
+
+- **TP=2 is real here.** FreeToken's TP=2 dies in the `pynccl.cu` ROCm JIT
+  (`cuda_runtime.h` unresolvable for gfx1201 — probed 2026-10-06); the
+  second R9700 idled. Radiance's TP wire is libr4d's own one-shot
+  all-reduce (no RCCL), legal on this board since the 2026-09-20 swap
+  (ProArt B850, x8/x8 on CPU root ports, `Routing+`/`ReqEn+` everywhere).
+- **Native FP8 end to end** (Frank's call): the official
+  `Qwen/Qwen3.8-Flash-Next-FP8` checkpoint keeps its modelopt block-FP8
+  encoding — no requantisation, no calibration run, no 4-bit codebook.
+- **Every tier is explicit and measured**, not an estimator: placement
+  `expert_tiered` with a pinned host pool, `--ngram-placement ram` for the
+  47.7 GiB PLE table, `--expert-vs-cache-ratio 0.82` (the publisher's
+  measured 99 %-hit point; 0.87 already collapses to 0 %), fp8 KV.
+
+## The payload: native FP8, reshaped then converted (one recipe rule)
+
+- **Raw checkpoint**: `/home/dank/models/Qwen/Qwen3.8-Flash-Next-FP8`
+  (ungated repo, 172.8 GB, 131 shards + index + tokenizer + vision
+  configs). Fetched by `scripts/download-frank-primary.sh` (vendored
+  `hfget.sh`, curl+jq, no HF token needed) and **verified byte-for-byte**:
+  all 131 shards sha256-match the Hub LFS oids (2026-10-05, log
+  `/home/dank/logs/verify-qwen38-official-fp8.log`).
+- **The container**: `/home/dank/models/rad/qwen4exp-fp8-native.rad`
+  (177,109 MiB measured = 173.0 GiB; floor
+  `FRANK_PRIMARY_RAD_MIN_MIB=177000` in the env gates a
+  truncated convert). Built by `scripts/build-frank-primary-rad.sh`:
+  1. stage `/home/dank/models/stage-qwen4exp-fp8` — `config.json` with
+     **`quantization_config` dropped** + relative symlinks into the raw
+     checkout;
+  2. `stack-qwen38-official-fp8.py` reshapes the staging dir — two
+     transforms, both value-preserving (script docstring carries the full
+     argument): the plugin maps the STACKED expert layout
+     (`mlp.experts.gate_up_proj` `[512,1280,2560]` + `_scale_inv` at the
+     same index) while the official repo ships per-expert unfused tensors,
+     so staging byte-stacks all 49 stacks' (48 trunk + MTP) 512 experts
+     with NO requantisation; and the PLE table ships as 128 **scaleless**
+     E4M3 shards + one bf16 scalar (1.9931793212890625e-04) while the
+     plugin's fp8 branch wants the codes beside a ONE-ELEMENT bf16 scale
+     plane — so staging decodes to a single bf16 `shard_0` with the
+     checkpoint's own scale (e4m3 decode and the scale are both exact in
+     bf16);
+  3. `rad-convert` with ONE `--quant` rule — pure copy for everything else
+     (every weight keeps the checkpoint's block-FP8 planes: `X.weight`
+     E4M3 codes + `X.weight_scale_inv` bf16 `[128×128]` scales) — and the
+     rtn rule re-encodes the bf16 PLE table at the same fixed scale, an
+     exact round-trip (nearest-code distance 2⁻³ vs the bf16 rounding
+     perturbation 2⁻⁹): the container gets the checkpoint's own PLE codes
+     plus the one-element scale plane (`blk.1.ple_ngram.scale`, view {1});
+  4. THREE LAWS the live runs proved (each cost a full verify+declare
+     cycle; all fixed in the two scripts, 2026-10-05/06):
+     - **Scale-name law**: `scale_name()` appends `_scale_inv` to the
+       TENSOR NAME verbatim — a stacked tensor named
+       `…experts.gate_up_proj` (no `.weight` suffix) has its scale plane
+       at `…experts.gate_up_proj_scale_inv`, NOT `…weight_scale_inv`.
+     - **File-shadow law**: the reader opens every index-named file in
+       SORTED order and emplaces EVERY tensor each file physically
+       holds, first-wins (`checkpoint.cpp add_shard`). The raw
+       model-000NN shards carry tensors NAMED exactly what the merged
+       PLE shard is named, so the merged file MUST sort first
+       (`00-ple.merged.safetensors`) or a raw fp8 shard wins the name
+       and the plan reads one shard as the codes.
+     - **Symlink-through law**: staging symlinks the raw index into the
+       stage; `open(path,"w")` writes THROUGH it and corrupts the raw
+       checkpoint (raw verify then fails 143/144). The stack script
+       writes index/config temp+`os.replace` — an atomic swap replaces
+       the symlink itself; the build script COPIES the index and
+       symlinks only the shards.
+  5. `rad-info` summary + size floor.
+- **Why the staging dir is mandatory** (the descriptor law, spec §2.4):
+  the plugin is selected by the exact `(arch_id, quant descriptor)` pair
+  with no fallback. A raw official checkpoint carries
+  `quantization_config.quant_method: "fp8"` → descriptor `"fp8"` → **no
+  shipped plugin claims `(qwen4exp, "fp8")`** — raw serve AND unmodified
+  convert both refuse. The shipped `qwen4exp_fp8` plugin claims
+  `(qwen4exp, "")` (recipe-built containers). `rad_convert.cpp` writes
+  `w.set_quant(meta.quant)` into the header, so the staged config's empty
+  quant method flows through as the container descriptor while **every
+  weight stays FP8 data**. Native precision is untouched; only the
+  selection key differs.
+- `--plan-only` first: the convert's declare phase is the engine's own,
+  so a plan that resolves every op will boot. The convert runs card-less
+  (declare = CPU-side layout queries); add `--device` only if a plugin
+  load ever demands it.
+
+## The shape (`scripts/frank-primary.env`, every value commented there)
+
+| Knob | Value | Why |
+|---|---|---|
+| Image | `stilldeadcode/radiance@sha256:4c3f4845…` | digest-pinned INDEX digest (RepoDigests also carry the per-arch manifest `1c582005…`; the image ID `69d05724…` is the config digest, NOT a manifest digest — pinning it sends `podman run` to the network); entrypoint = engine, tools (`rad-convert`/`rad-info`/`rad-tune`/`rad-kbench`) on PATH |
+| Container | `frank-primary` | identity = service name across runtimes; legacy list retires `frank-primary-qwen frank-primary-r9v qwen-fp8 glm-probe` |
+| `--tp 2 --tp-wire wht6` | both R9700s | fused WHT-6 lossy wire: +5.6–6.3 % prefill, ~−1.3 % 8-stream decode at TP2; `--tp-wire-min-kb` stays engine default (128 KiB) |
+| `--max-num-seqs 2` | first boot | Frank: "parallelism = 2 to start". Raise only after a real-traffic look at slot occupancy + the ledger's KV headroom line. The fused all-reduce row count is `seqs × (1 + spec) = 8` |
+| `--max-model-len 200000` | served window | native 262,144; the publisher's production value and what `models.yml` quotes. Longer prompt = refused, never truncated |
+| `--placement expert_tiered --host-pool-mib 40960` | pinned expert pool **per rank** | native FP8 experts are 112.8 GiB total (56.4 GiB/rank slice) vs ~29 GiB at the 4-bit publisher shape — the pool is the price of native precision. Resize from the boot ledger's coverage line, never arithmetic |
+| `--gpu-headroom-mib 96 --expert-vs-cache-ratio 0.82` | publisher values | 0.82 = measured 99 % expert-cache hit; 0.87 → 0 % (the cliff) |
+| `--kv-cache-dtype fp8` | **7.32 KiB/token** | 12 QSA full-attention layers, 2 KV heads × 256 dim, K+V; a 200K-token sequence is 1.43 GiB. KV is sized from the VRAM the ratio leaves |
+| `--num-speculative-tokens 3` | MTP head | the checkpoint carries `mtp.*` (3,101 tensors, `mtp_num_hidden_layers: 1`); the plugin's probe returns draft depth 3 — the publisher's operating point |
+| `--max-num-batched-tokens 2048` | prefill chunk | A/B-verified optimum (1024/2048/4096 ladder, see "Tuning levers") |
+| `--prefix-cache-host-mib 4096 --prefix-cache-dir /data/kvcache/frank-primary --prefix-cache-disk-mib 65536` | 3 tiers | host tier pinned (see the ledger), disk tier in `/home/dank/radiance-state` (mounted rw at `/data`; `/` is ext4, mapped-tier-eligible); a session snapshot moves with its blocks |
+| `--ngram-placement ram` | **PLE pinned to host RAM** | Frank's pin law. The PLE n-gram table is 51.2 GB (320,001,536 rows × 160 dims = 29 % of all parameters) served via io_uring reads; `ram` **refuses the boot** if memlock is too small |
+| `--override-chat-template /templates/template.jinja` | froggeric v22.5 | vendored `scripts/qwen38-froggeric-v22.5.jinja` (28,234 B, `qwen3.8-froggeric-v22.5`); rad-convert bakes the checkpoint's own template, this overrides it, and the engine derives the reasoning/tool-call reply format from it |
+
+**Pinned-RAM ledger** (root unit + `LimitMEMLOCK=infinity` exist for this):
+2 × 40 GiB expert pools + 47.7 GiB PLE + 4 GiB prefix host tier + ~2 GiB
+spare ≈ **134 GiB**. `ttm.pages_limit` (this kernel has NO `pages_max` —
+the two-knob story was stale) caps GPU-mappable pinned pages (default = half
+of RAM): the pre-cutover cap was **123 GiB** — BELOW the ledger — so the
+serve script's TTM preflight (`FRANK_PRIMARY_TTM_MIN_MIB=140000`) refuses to
+start until it is raised. DONE 2026-10-05 pre-cutover: sysfs write of
+**52,428,800 pages = 200 GiB** (`/sys/module/ttm/parameters/pages_limit`;
+verified read-back; raising at runtime is harmless and allocates nothing).
+PERSISTENCE: the sysfs write does not survive reboot — add
+`ttm.pages_limit=52428800` to the kernel cmdline at the next scheduled
+reboot.
+
+**Per-rank VRAM** at TP2: expert slice 56.4 GiB total across VRAM + host
+pool; trunk ~3.75 GiB/rank; token embedding vocab-parallel (~1.18 GiB/rank
+— the bf16 table is 2.43 GiB, sharding hands the other half to KV); lm_head
+row-sharded; recurrent state 54 MiB/copy/card (2 copies with speculation);
+KV from the `0.82`-side remainder of claimed VRAM at 96 MiB headroom.
+
+## The engine contract (all verified in source, not assumed)
+
+- **`/health` == `/ping`**: 200 JSON (`model_id, in_flight, queue_depth`)
+  while serving, 503 only while stopping — **the listener binds AFTER the
+  full load**, so a bare `curl -fsS http://127.0.0.1:7770/health` gate is
+  honest (the deployer loops on it; the FreeToken-era `"status":"ok"` body
+  grep is gone). Boot budget 1800 s (publisher `start_period` 15 m);
+  measured 2026-10-06: health 200 ~2-3 min after start (~120 s warm) —
+  the big terms are the 125.26 GiB page-in (38.1 s at 3.5 GB/s) and the
+  host-pool pin (PLE 47.68 GiB in 43.9 s).
+- **Sampling** (oai.cpp): `temperature, top_p, top_k, min_p, typical_p,
+  presence_penalty, frequency_penalty, repetition_penalty, repeat_last_n,
+  chat_template_kwargs, reasoning_effort, enable_thinking,
+  preserve_thinking` accepted; **unknown params are refused (400)** —
+  models.yml's extraBody set is fully inside it; nothing unverified rides
+  the wire. `max_tokens` is clamped, not refused; `seed` absent = pick
+  one; `response_format` supported; **`logprobs` is refused** (harness
+  never sends it).
+- **Reasoning**: `--reasoning-format` defaults to `auto` → thinking in
+  `reasoning_content` (models.yml `reasoningContentField` matches). Tool
+  calls: the qwen3.8 dialect → `message.tool_calls` +
+  `finish_reason: tool_calls`. No parser flags anywhere — parsers are
+  auto.
+- **Vision**: the official checkpoint carries the 27-layer vision tower;
+  the engine runs the Qwen-VL processor (patching, pad-token expansion,
+  3-part rotary are core). `models.yml` keeps `input: [text, image]`;
+  `--mm-max-patches` available.
+- **`/metrics`**: vLLM-compatible names + `radiance:*` families — the
+  Dankclaw Prometheus job re-scrapes 192.168.0.235:7770 (plain LAN routing
+  reaches it; the netavark published-port quirk is same-host only).
+  `/stats`, `/sessions`, `/dashboard` for live internals; the repo ships
+  `deploy/grafana/radiance.json` if a dashboard is wanted.
+- **`n` > 1 requests**: `n` defaults to `max(8, max-num-seqs)`; stop
+  strings ≤ 64 × 4096 B. `model` field not enforced.
+
+## Deploy + verify
+
+```bash
+# one-time (already done 2026-10-05): payload fetch + sha256 verify + convert
+ssh dankserver 'bash /home/dank/frank-primary-build/build-frank-primary-rad.sh --plan-only'   # declare + plan, no bytes
+ssh dankserver 'bash /home/dank/frank-primary-build/build-frank-primary-rad.sh'               # full convert + rad-info
+./scripts/deploy.sh dankserver --service frank-primary
+```
+
+Post-cutover checklist (each is observable, not assumed):
+
+1. Unit: `is-active` active, `is-enabled` enabled, container answers
+   `/health` with 200 JSON.
+2. Boot ledger (engine stdout → `/home/dank/logs/frank_primary.log`):
+   P2P probe per rank pair, TP=2 shard placement, expert pool coverage
+   line ("backs N % of the worst case … M full-length sessions"), KV pool
+   bytes, PLE pinned-RAM line, drafter depth 3, prefix tiers.
+3. MTP live: a completion's `timings` object shows draft/accept counts;
+   `radiance:*spec*` metrics in `/metrics`.
+4. Froggeric template: a probe prompt renders the v22.5 shape
+   (tool-call + thinking turn both exercise it).
+5. Vision: a ≥256 px solid-color PNG answers the exact color through an
+   `image_url` content part.
+6. Prometheus target `frank-primary` green (not never-green).
+
+## Cutover measured (2026-10-06, all values observed live)
+
+- Container: 1,857,127,997,440 B = 177,109 MiB = 173.0 GiB. Plan-only
+  priced 172.76 GiB — the DATA planes; the written container adds a
+  metadata tail. Floor gates must be set from the real file (the first
+  deploy refused on 178000 > 177109; floor is 177000).
+- Boot: 125.26 GiB read in 38.1 s (3.5 GB/s), PLE pinned
+  "47.68 GiB in 43.9 s" (the 2 B scale plane with it), each rank's
+  mover holds 25.46 GiB static+experts (19.15 GiB resident experts,
+  79 %), 16,820 pinned pool slots, health 200 ~2-3 min after start
+  (~120 s warm). No 7-minute vLLM-style boot.
+- KV ledger: kv_attn 99,956 blocks = 2.48 GiB (399,824 tokens),
+  kv_qsa_bkey 317.24 MiB, kv_gdn_state 4 slots × 54 MiB, PLE conv
+  states < 1 MiB; linear checkpoints 56.37 MiB × 8 slots (3 of 8
+  card-backed at boot, 5 withdrawn under the 208 MiB floor).
+- MTP on real traffic (temp 0.7, 220-token ask): draft_n 225 /
+  draft_n_accepted 119 = 52.9 % acceptance, decode 23.8 t/s. ONE
+  sample — the shape's real number needs live-traffic measurement.
+- Contracts: `reasoning_content` field (matches models.yml), usage
+  metering separates reasoning_tokens; vision — a 512² solid-red PNG
+  through `image_url` answers `Red` (279 prompt tokens; NOTE the
+  32-token budget is eaten by thinking — give vision probes ≥256
+  max_tokens); native `tool_calls` with finish_reason tool_calls.
+- Boot defects fixed the same day: the SSD prefix tier opened only
+  after the serve script mkdir -p'd the full path (the engine's
+  create is NOT recursive: "cannot create /data/kvcache/..."); the
+  converter's scale-name law, the PLE file-shadow and the
+  symlink-through-raw-index bug are in the payload section above.
+- Prometheus: `/metrics` answers 200 with vLLM-compatible families
+  (verified from Dankclaw); the `frank-primary` job needs a
+  prometheus-service deploy to go green (config pushed, reload
+  deferred behind a live parallel writer's dirty tree).
+
+## Tuning levers (in order of likely use)
+
+- **`FRANK_PRIMARY_MAX_SEQS`**: raise 2 → 3/4 only when the ledger's KV
+  headroom line and real slot occupancy agree; each slot buys a
+  recurrent-state copy (54 MiB/card) and shrinks the per-request KV share.
+- **`FRANK_PRIMARY_HOST_POOL_MIB`**: the ledger's coverage line is the
+  oracle — it prints what the pool backs at the worst case and how many
+  full-length sessions fit. RAM budget: pools + PLE + prefix ≤ ~140 GiB of
+  the host's 246.
+- **`FRANK_PRIMARY_EXPERT_VS_CACHE`**: 0.82 is the measured 99 %-hit
+  point; 0.87 zeroes it. Move only with a measured hit-rate reason.
+- **`FRANK_PRIMARY_TP_WIRE`**: `wht6` vs `exact` — `exact` trades the
+  +5.6 % prefill for bit-exact reductions; the fused one-shot forms are
+  declared over the seqs × (1+spec) row bound.
+- **`FRANK_PRIMARY_EXTRA_ARGS`**: the boot-ladder escape hatch, expanded
+  unquoted.
+- **`rad-tune -m <rad> --tp 2 --num-speculative-tokens 3`**: benchmarks
+  the tunable space at this model's shapes on this machine, writes
+  `$RADIANCE_HOME/tune/<machine>.tune` which the engine's selector reads at
+  boot. Declare at the shapes you serve (`--tp 2
+  --num-speculative-tokens 3`), or you measure a program the serve never
+  runs. `$RADIANCE_HOME` is `/opt/radiance/share/radiance` — in the IMAGE — so a
+  `--rm` scratch container's cache dies with it: the serve script
+  bind-mounts `$FRANK_PRIMARY_STATE_DIR/tune` (`/home/dank/radiance-state/tune`)
+  at `/opt/radiance/share/radiance/tune`, so the cache (36 GEMM rows,
+  `gfx1201-x3-AMD_Radeon_Graphics.tune`, generated 2026-10-06 in a
+  stopped-service window) survives and is read at every production boot.
+  The two attention requests have no shape recipe (`attn_paged`,
+  `attn_paged_gate_quant` — "see tools/opshapes.h") and are not tuned.
+  `RADIANCE_TUNE` pins a kernel for an A/B — an instrument, not a setting.
+- **`FRANK_PRIMARY_MAX_BATCHED_TOKENS` (MNBT)**: A/B'd 2026-10-06 in a
+  stopped-service window (real-text probes: 20,417-token cold prefill,
+  1,985-token warm prefill, 256-token decode). The prefill ladder is
+  NON-MONOTONIC with an interior optimum at 2048: 1024 → 2048 → 4096 =
+  231 → 460 → 448 t/s cold, 221 → 412 → 427 t/s warm. Decode is flat within
+  noise (24.0 / 25.1 / 24.3 t/s); draft acceptance 60.1 / 67.1 / 63.3 %.
+  **2048 wins** (peak prefill + peak acceptance) — it stays the production
+  value. 1024 halves prefill (the batch too small to saturate the card);
+  4096 is a hair off 2048 (the chunk past the kernel's sweet spot).
+- **Diagnostics**: `rad-info` (container: encodings, recipe, profile),
+  `--debug-graph` (what declared vs what issued), `rad-kbench` (kernel
+  correctness/speed vs libref), `/stats` + `/sessions` (live), and the
+  publisher's `scripts/` harnesses (`fnpf`, `fnconc`, `cstep2`, `ident`,
+  `mctools`) — note `mcserve/fnstop`-family scripts `pkill` the engine
+  binary by name; on this host the service is a systemd unit, so use
+  `sudo systemctl stop frank-primary` instead.
+
+## Rollback
+
+| Layer | Path |
+|---|---|
+| FreeToken (one card, native FP8 safetensors) | `git checkout frank-primary-freetoken-pr587-20261005 -- scripts/frank-primary.env scripts/frank-primary.service scripts/serve-frank-primary.sh scripts/docker/ tests/frank_primary_unit.bats` + redeploy; image `localhost/frank-primary-freetoken:pr587-77a7201` and payload `/home/dank/models/Qwen3.8-Flash-Next-Uncensored-FP8` stay on disk until the window closes |
+| tcclaviger vLLM fork (TP=2, shape G) | git tag `frank-primary-vllm-shapeG-20261003` (trio + env lore; image still in dank's rootless storage) |
+| 4-bit StillDeadcode publisher shape | `qwen3.8-next-flash-fp8-iq4r-moe.rad` + `--host-pool-mib 12288` — the documented fallback if the native container ever proves unbootable |
+
+Retire the FreeToken-era orcarouter payload to `~/.AGENTTRASH/` only after
+the rollback window closes (trash policy, never `rm`).
+
+## Links
+
+- [[LLM/index]] — fleet index
+- [[frank-primary-freetoken-pr587]] — the retired FreeToken era
+- [[dankserver-qwen38-fp8-expert-offload-tuning]] — the retired tcclaviger vLLM era
+- [[Dual-R9700-Cooperation]] — the board swap that made TP=2 legal here
+- [[RDNA4 Container Serving]] — rootless-podman GPU laws on these hosts
+- `serverAppConfigs-docs/Architecture/Testing Policy.md` — the seam contract

serverAppConfigs-docs/LLM/frank-primary-radiance-swift15-w4dn8.md

@@ -0,0 +1,224 @@
+# frank-primary — Radiance (StillDeadcode), Swift1.5 Qwen3.8-Flash-Next W4Dn8, 2× R9700 TP=2
+
+**Status: LIVE — deployed 2026-10-08 (main `3ca64e1`, e2e 33/33).** · **Host:** Dankserver (192.168.0.235) · **Port:** 7770 · **GPU:** 2× Radeon AI PRO R9700 (gfx1201), TP=2
+
+The FP8-native era (`qwen4exp-fp8-native.rad`, 173.0 GiB, no-recipe convert)
+is replaced by a **GPTQ-calibrated 4-bit container** of
+`ukisai/Swift1.5-Qwen3.8-Flash-Next` (BF16, 335.3 GiB, abliterated
+finetune) converted with the recipe
+`scripts/recipes/qwen4exp-swift15-w4dn8-i8-hc8m.recipe` on engine
+**1.0.13** (`stilldeadcode/radiance@sha256:4c3f484564…` — same digest as
+the FP8-native era; 1.2.0 was used ONLY for the calibration serve).
+Same port, alias, container name, systemd unit. Rollback: revert the
+`MODEL_FILE` line in `scripts/frank-primary.env` + redeploy —
+`/home/dank/models/rad/qwen4exp-fp8-native.rad` stays on disk (trash
+policy; 173 GiB, purge when the window closes).
+
+## Why this swap
+
+- **Context economics at production memory budgets**: the FP8-native
+  experts (56.4 GiB/rank slice) forced a 40 GiB/rank pinned host pool and
+  left KV ~3.5 GiB. W4Dn8 experts cut the resident set to ~1/4, freeing
+  host and VRAM budget for KV and slots at equal-or-better measured
+  decode (gate: ≥ 30.8 t/s vs the 30.08 t/s control).
+- **Swift 1.5 base**: abliterated finetune of Flash-Next (Frank's
+  fleet-personality call; frank-large serves the same family).
+- **GPTQ closes the precision gap**: RTN alone on BF16 weights lands
+  measurably worse; GPTQ with Hessians from a 10M-token corpus of this
+  model's own distribution recovers most of it, verified end-to-end by a
+  KLD gate against the BF16 container itself (KL ≤ 0.03, top-1 ≥ 95 %).
+
+## The payload chain
+
+1. **Fetch**: `scripts/download-frank-primary-swift15.sh` →
+   `/home/dank/models/Swift1.5-Qwen3.8-Flash-Next` (131 shards, 335.3
+   GiB, per-shard LFS sha256 verified, no token on the command line).
+   48 layers, 512 experts (top-10 routed), moe_inter 640, hidden 2560,
+   PLE 20M-vocab table 95.37 GiB, MTP head, vision tower.
+2. **BF16 container**: `run-swift15-bf16.sh convert` →
+   `rad-convert` no-recipe (pure copy) at `--max-ctx 4096` →
+   `qwen4exp-swift15-bf16.rad` (335.28 GiB). Serves on 1.0.13 as the
+   KLD reference engine (disk-tier placement; boot ~35 min).
+3. **KLD reference**: `run-swift15-bf16.sh kld` (1.0.13) →
+   `/home/dank/corpora/kld-ref` — `radiance-kld-1` format, `rows:
+   50235`, 197 source-labelled docs, top-16 log-probs (f16), corpus
+   fnv1a64 `a9d62b33c18b3d9c`. ~4 h at 32k prefill steps
+   (`--max-num-batched-tokens 32768` — a 65536 step OOMs an arena
+   tensor at `--max-model-len 16384`). **The wrapper's exit line is the
+   receipt: never scp this script while it runs** (bash lazy-read;
+   a mid-run swap killed a clean engine exit with rc=127, third fleet
+   occurrence of the script-swap law). `kld-then-calib.sh SKIP_KLD=1`
+   resumes from the `rows: 50235` receipt.
+4. **Calibration**: `run-swift15-bf16.sh calib` on **1.2.0** (the only
+   engine with `RADIANCE_CALIB_TOKENS`; grams are engine-invariant
+   bf16 activations — RADGRAM2 format byte-identical across versions)
+   + `calib-push.py` pushing `calib-corpus.jsonl` (10,014,932 tokens,
+   1,294 prompts, assembled from real HF sources, chat-templated,
+   source-labelled). The tap (in the `qwen4exp_fp8.so` architecture
+   plugin) writes per-expert down-proj Gram matrices
+   `gram.r<rank>.blk.L.ffn_down_exps.<e>.bin` (32-byte RADGRAM2 header +
+   640×640 f32; `rows` = routing count, `trace` = activation energy)
+   and stops the serve at the token threshold. TP=2: whole experts per
+   rank (256/rank); gate_up input is rank-sliced (plugin warning — the
+   recipe only casts the down projection).
+5. **Protected-expert derivation**: `derive-protected-experts.py
+   /home/dank/corpora/calib` → the 24 highest-energy experts (the law
+   is data-driven; Swift drew 24, not the fp8-era 10 — its routing is
+   spikier: `blk.47 e445` alone carries 32 % of its layer's down energy
+   at 70× routing share) promoted to whole-expert bf16.
+   Protection law: energy share ≥ 3 % AND ≥ 3× routing share; even count
+   per rank half; odd half gains the next most energetic.
+   `apply-protected-cast.sh` splices the derived names between the
+   `# PROTECTED-BEGIN/END` markers of the recipe (refuses double-derive).
+6. **Final convert**: `convert-swift15-w4dn8.sh` (1.0.13, host-backend
+   CPU-only — no GPU visible to `rad-convert`; GPTQ solve is CPU BLAS) —
+   gates: ≥48 gram files present, bf16 container already deleted
+   (disk), ≥146 GiB free, output ≥108,000 MiB, `rad-info` shows the
+   gptq receipt. No `--reuse`. Measured: 5 h 50 m (6.0–6.5 min/layer on
+   49 GPTQ layers, load-average 8), output **116,416 MiB**.
+   The plugin's `rad_arch_declare` enforces ONE ENCODING PER EXPERT
+   TABLE (`one table of experts takes one encoding, and only a whole
+   expert kept plain bf16 is served beside it`) — a split tier
+   (down=fp8, gate_up=w4) is unservable: the kernel serves a single
+   w4nl64a8h table; both expert tables ride GPTQ w4 (down calibrated,
+   gate_up uncalibrated), protected experts bf16 beside them.
+
+## The recipe tiers (scripts/recipes/qwen4exp-swift15-w4dn8-i8-hc8m.recipe)
+
+| Tier | Encoding | Why |
+|---|---|---|
+| MoE experts, both `gate_up` and `down` (one w4 table/layer) | **GPTQ w4nl64a8h** — u4 codes, fwht128 rotation, fp8 scales/64 + f32 scale plane | the campaign tier: GPTQ on down-input Hessians from the 10M-token corpus (the tap rides the down projection; gate_up shares the table uncalibrated — one-encoding-per-table plugin law) |
+| 24 derived protected experts (whole) | **bf16** | energy-share law (step 5) |
+| shared expert + trunk attention/SSM linears | **int8**, scale per 128 cols, searched | trunk precision law |
+| hyper-cond + MTP head | **fp8_e4m3 row-groups** | hc+mtp fp8 law |
+| lm_head | **int8** | i8 lm_head law (E4M3 costs 0.6 top-1 alone) |
+| PLE n-gram table | fp8 at the checkpoint's fixed scale 1.9931793212890625e-04 | the fixed-scale law; absmax audit passed the 2⁻¹³..2⁻¹⁰ plateau |
+| embeddings, vision tower, QSA indexer | **bf16 passthrough** | untouched |
+
+## The shape (`scripts/frank-primary.env`)
+
+Unchanged from the FP8-native era: TP=2 + `wht6`, `--max-num-seqs 3`,
+`--max-model-len 200000`, MNBT 2048 (the MTP resonance law — 2560
+collapses decode to 2.6–7.7 t/s), fp8 KV, `expert_tiered` host pool
+40960 (the load-bearing floor), `--ngram-placement ram`, froggeric
+v22.5 template override. `MODEL_FILE` points at
+`/home/dank/models/rad/qwen4exp-swift15-w4dn8-i8-hc8m.rad` (**116,416
+MiB measured**); the serve size floor `FRANK_PRIMARY_RAD_MIN_MIB` moves
+177000 → **110000**.
+
+## Gates (measured at cutover)
+
+- **KLD gate: PASS (2026-10-08 04:12)** — `kld-gate-swift15.sh`,
+  candidate on 1.0.13 vs `kld-ref`: **mean KL 0.0146** (median 0.00126,
+  p90 0.0315, p99 0.194, max 4.12), **top-1 97.84 %**,
+  **ppl ratio 1.0126** (candidate 1.6913 vs reference 1.6702), 50,235
+  positions / 197 docs. Report: `/home/dank/radiance-state/kld-gate.json`
+  (+ `.rows` per position). Gate shape: disk-tier, pool 40960, MNBT 3072
+  (the w4 activation arena scales hard with MNBT: 32768 → 33.3 GiB arena,
+  refused), KV 2048, headroom 512, no speculation. The engine ran rank 0
+  fully on card 0 (25.88 GiB) with rank 1 host-side in this shape —
+  scoring is placement-invariant; scored at 28 positions/s.
+- **KLD gate re-PASS (2026-10-08 04:49) at the production-legal shape**:
+  MNBT 2048 (the w4 file-tier law: experts stream in place only at
+  MNBT ≤ 2048 — the recording's 32768 demands a widened form the disk
+  tier refuses), pool 40960, vram-kv 3072 fp8, tp 2, headroom 768.
+  **mean KL 0.0131, top-1 97.92 %**. The Δ vs the first PASS (0.0015) is
+  the float-order noise band of the chunk-size change — the verdict is
+  chunk-size invariant, ~2.3× inside the 0.03 bar on both shapes.
+- **Throughput gate: PASS (2026-10-08 05:40)** — decode at 44.5k depth
+  (streamed chat probe, temp 0, 512 tokens, salted): arm A (main serve
+  shape, EVC 0.82) **146–173 t/s**, engine timings **121.9 t/s**,
+  draft accept **0.7246** (350/483); arm C (deployed vram-pair shape
+  26071/3543) median **136.0 t/s**, warm TTFT 9.45 s. Control
+  (fp8-native 1.0.13): 30.08 / accept 0.7549. Prefill ~4.7k t/s warm.
+  The w4 expert table is resident — the fp8-native era's NVMe expert
+  streaming (the 3.5 GB/s wall) is gone; the MTP ×3 head rides a 45–60
+  t/s base decode. Conversation battery (short context): geomean
+  172 t/s (min 100 / max 208), mean ttft 0.09 s. The EVC ladder is moot:
+  the w4 resident set fits fully inside the 40 GiB pool at 0.82
+  (ledger: mover full fill). Depth-output audit: the 44.5k-depth
+  summary is a coherent ordered index of the concatenated corpus
+  (no repetition, no NaN-token garbage) — the depth-corruption class
+  of 2026-10-04 is clean.
+- Fleet probes: PASS, re-run on the deployed prod — vision (solid-red
+  512 px → "Red"), tool_calls (`{"expr":"23 * 47"}`), reasoning_effort
+  xhigh/medium/low/high all 200 (the Swift1.5 froggeric template accepts
+  the full set), reasoning_content present, response_format json_object
+  honoured. **Vision probe law:** budget ≥ 256 tokens and read
+  `reasoning_content` as fallback — at 64 tokens thinking exhausts the
+  budget and `content` comes back empty (the answer lands in the
+  reasoning channel).
+- **Deployed verification (2026-10-08)**: `deploy.sh dankserver
+  --service frank-primary` green incl. Playwright e2e 33/33; unit
+  `active`, `/health` 200, `/v1/models` id `frank-primary` (the harness
+  alias is the served-model-name, independent of the file — models.yml
+  untouched). Deployed shape: fp8 KV (KLD-gate shape), spec depth 2,
+  seqs 3, vram pair 26071/3543. Boot ledger: KV pool 3.46 GiB, expert
+  pools 57.84 GiB, cards settled 1.41 GiB free, `serving on :7770`.
+  Live decode probe (512 tokens, streamed, temp 0): ~190 t/s.
+
+### Wire rename + the bench drop-in trap (2026-10-09)
+
+`FRANK_PRIMARY_SERVED_NAME` moved `frank-primary` → **frank-archon**; the
+service, unit, scripts and env file keep the old name. Radiance is the only
+fleet engine whose `/v1/models` listing reflects the flag, and it echoes the
+**requested** model name in chat responses — so a chat probe that returns 200
+proves nothing about the served id. Always read `/v1/models`.
+
+The first deploy after the edit came up green and `/v1/models` still said
+`frank-primary`. Cause: a campaign leftover drop-in
+`/etc/systemd/system/frank-primary.service.d/bench.conf` (2026-10-08 04:09)
+overrode BOTH `Environment=FRANK_PRIMARY_ENV_FILE` and `ExecStart` to the
+staging worktree `/home/dank/serverAppConfigs-swift15`. Every
+`deploy.sh dankserver --service frank-primary` since then restarted the
+staging tree: green health gate, old configuration, silently. Verified before
+removing: the staging `serve-frank-primary.sh` and `frank-primary.service`
+are byte-identical to the checkout, its `frank-primary.env` differed only by
+the alias line, and its HEAD (`8375567`) is an ancestor of `main`. The
+drop-in is staged in `/home/dank/.AGENTTRASH/20261009-frank-primary-bench-dropin/`.
+After `daemon-reload` + restart, `/v1/models` reports `frank-archon` (boot
+healthy in 102 s, shape unchanged: fp8 KV, spec depth 2, seqs 3, vram pair
+26071/3543).
+
+**Deployer blind spot:** `deploy.sh` plans from repo paths and gates on the
+port. It cannot see host-side drop-ins, so unit drift is invisible to a green
+run. After an LLM deploy, verify the served configuration behaviourally
+(here: `podman inspect <name> --format {{json .Config.Cmd}}` and `/v1/models`).
+
+
+
+## Deploy + verify
+
+```bash
+sudo systemctl stop frank-primary          # drained window
+bash convert-swift15-w4dn8.sh              # if not already run
+./scripts/deploy.sh dankserver --service frank-primary
+```
+
+Post-cutover checklist: unit active/enabled; `/health` 200; boot ledger
+(expert pool coverage now w4-sized, KV pool should be larger than the
+FP8-native era's 399,824 tokens); MTP draft counts in `timings`; froggeric
+probe; ≥256 px `image_url` vision probe answers the color; tool-call
+probe; `reasoning_effort` fold; Prometheus target green; `tests/run.sh`
++ Playwright e2e green.
+
+## Rollback
+
+| Layer | Path |
+|---|---|
+| FP8-native radiance (immediate) | revert `MODEL_FILE` in `scripts/frank-primary.env` + redeploy; `qwen4exp-fp8-native.rad` (173.0 GiB) on disk |
+| FreeToken PR #587 | git tag `frank-primary-freetoken-pr587-20261005` |
+| tcclaviger vLLM shape G | git tag `frank-primary-vllm-shapeG-20261003` |
+
+Campaign artifacts on disk: BF16 checkpoint (335 GiB), `kld-ref` (24
+GiB), grams (`/home/dank/corpora/calib`), corpora JSONL, logs
+`/home/dank/logs/swift15_*.log`. The BF16 `.rad` container is deleted
+before the final convert (disk gate) — the raw checkpoint is the
+re-convert source if the window ever reopens.
+
+## Links
+
+- [[frank-primary-radiance-qwen38-native-fp8]] — the replaced FP8-native era
+- [[LLM/index]] — fleet index
+- [[swift15-campaign-worklog]] — campaign record
+- [[Dual-R9700-Cooperation]] — the board swap that made TP=2 legal here

serverAppConfigs-docs/LLM/frank-redo-2026-08-17.md

@@ -18,7 +18,7 @@ The old scripts moved to `old/scripts/` in this repository.
 | Service | Host | Runtime | Model | Port |
 |---------|------|---------|-------|------|
 | frank-large | Dankserver | llama.cpp (patched FastMTP) | HauhauCS Qwen3.8-27B-Uncensored-Aggressive IQ4_XS | 7770 |
-| frank-small | Dankclaw | vLLM ROCm | nameistoken Qwen3.6-35B-A3B Quark W8A8 INT8 | 52625 |
+| frank-secondary | Dankclaw | vLLM ROCm | nameistoken Qwen3.6-35B-A3B Quark W8A8 INT8 | 52625 |
 
 The RTX 3090 runs the whole 27B model standalone. No RPC worker, no
 tensor split, no NPU.
@@ -40,7 +40,7 @@ tensor split, no NPU.
   `--spec-draft-ngl all`, `--spec-draft-p-min 0`) then ngram-mod fallback
   (48/64/24).
 
-### frank-small — nameistoken/Qwen3.6-35B-A3B-Quark-W8A8-INT8
+### frank-secondary — nameistoken/Qwen3.6-35B-A3B-Quark-W8A8-INT8
 
 - Base: Qwen3.6-35B-A3B (hybrid MoE, 256 experts top-8, ~3B active, 40
   layers: 10 blocks of 3 DeltaNet + 1 Attention, native 262144 ctx).
@@ -103,10 +103,10 @@ Production choice: **131072 (128K)** — the maximum that keeps about
 5. Verify: `curl -sf http://127.0.0.1:7770/health` and a chat completion.
 6. Confirm VRAM headroom with `nvidia-smi`.
 
-### Dankclaw (frank-small)
+### Dankclaw (frank-secondary)
 
 1. Stop the old services: `sudo systemctl stop frank-large
-   frank-small` (retired PP head and NPU endpoint).
+   frank-secondary` (retired PP head and NPU endpoint).
 2. Install uv and create the venv:
    ```
    curl -LsSf https://astral.sh/uv/install.sh | sh
@@ -131,9 +131,9 @@ Production choice: **131072 (128K)** — the maximum that keeps about
    vision encoder falls back to torch SDPA and engine init OOMs.
 5. Symlink the unit, reload, enable, restart:
    ```
-   sudo ln -sf /home/dank/vllmScripts/scripts/frank-small.service /etc/systemd/system/
-   sudo systemctl daemon-reload && sudo systemctl enable frank-small
-   sudo systemctl restart frank-small
+   sudo ln -sf /home/dank/vllmScripts/scripts/frank-secondary.service /etc/systemd/system/
+   sudo systemctl daemon-reload && sudo systemctl enable frank-secondary
+   sudo systemctl restart frank-secondary
    ```
 6. Verify: `curl -sf http://127.0.0.1:52625/health` and a chat completion.
 
@@ -159,7 +159,7 @@ on gfx1151; newer vLLM releases may already include it.
 
 Three changes landed after the initial deployment:
 
-### 1. Tool calling on frank-small (HTTP 400 fix)
+### 1. Tool calling on frank-secondary (HTTP 400 fix)
 
 Clients (pi) send `tool_choice: "auto"`. vLLM rejects it with HTTP 400
 unless the server enables tool calling. Fixes applied:
@@ -184,7 +184,7 @@ model behavior, which motivated the swap.
 
 ### 2. Model swap: Avesed INT8 W8A8 -> Quark W8A8 INT8
 
-`frank-small` now serves
+`frank-secondary` now serves
 `nameistoken/Qwen3.6-35B-A3B-Quark-W8A8-INT8` (AMD Quark 0.11.1, ~35 GB,
 vLLM-native expert layout). The Avesed compressed-tensors quant's
 Qwen3-XML tool fidelity was poor. Verified with the Quark model: plain

serverAppConfigs-docs/LLM/frank-secondary-benchmark.md

@@ -1,7 +1,7 @@
 ---
 timestamp: 2026-09-03
 type: reference
-title: frank-small Benchmark — ncmoe and Batch Tuning
+title: frank-secondary Benchmark — ncmoe and Batch Tuning
 description: llama-bench sweep of MoE CPU offload (ncmoe) and batch/ubatch on the RTX 3090 for Qwen3.6-35B-A3B Q6_K_P.
 tags:
   - benchmark
@@ -11,11 +11,11 @@ tags:
   - dankserver
 ---
 
-# frank-small Benchmark — ncmoe and Batch/Ubatch Tuning
+# frank-secondary Benchmark — ncmoe and Batch/Ubatch Tuning
 
 This page records a llama-bench sweep on Dankserver (RTX 3090, 24 GiB) for
 the HauhauCS Qwen3.6-35B-A3B Q6_K_P model. The sweep decided the final
-`--n-cpu-moe` and `--ubatch-size` values for `frank-small`.
+`--n-cpu-moe` and `--ubatch-size` values for `frank-secondary`.
 
 Run date: 2026-08-13. Build: llama.cpp `555881ebc` (10121). CUDA, 7 threads,
 `--cache-type-k q8_0 --cache-type-v f16`, `--fit-target 0` (KV auto-sized to
@@ -106,7 +106,7 @@ service runs 700416 cells (13,680 MiB).
 
 ## Chosen State
 
-> **Live drift (2026-08-22 audit):** the deployed `frank-small` now runs `--n-cpu-moe 38`, `--batch-size 4096`, `--ubatch-size 2048` (ncmoe raised from 36 for stability; batch lowered). The values below are the original benchmark recommendation.
+> **Live drift (2026-08-22 audit):** the deployed `frank-secondary` now runs `--n-cpu-moe 38`, `--batch-size 4096`, `--ubatch-size 2048` (ncmoe raised from 36 for stability; batch lowered). The values below are the original benchmark recommendation.
 
 Prefill speed is the priority, so the state keeps the top prefill tier and
 moves one expert layer to CPU for stability:

serverAppConfigs-docs/LLM/frank-secondary-gemma4-12b.md

@@ -1,10 +1,10 @@
 ---
 type: reference
-title: "frank-small on Dankclaw — llama.cpp, Gemma4-12B QAT Q4_K_M"
-description: "The active frank-small runbook — mainline llama.cpp on Dankclaw's RTX 3090 serving HauhauCS Gemma4-12B-QAT-Uncensored-HauhauCS-Balanced at Q4_K_M with a separate MTP assistant sidecar chained behind ngram-mod, q8_0 KV, and --fit sizing the full 262k window: identity, payload, KV arithmetic, the spec-decode chain, deployed flags, the client contract, and what to check when it misbehaves."
+title: "frank-secondary on Dankclaw — llama.cpp, Gemma4-12B QAT Q4_K_M"
+description: "The active frank-secondary runbook — mainline llama.cpp on Dankclaw's RTX 3090 serving HauhauCS Gemma4-12B-QAT-Uncensored-HauhauCS-Balanced at Q4_K_M with a separate MTP assistant sidecar chained behind ngram-mod, q8_0 KV, and --fit sizing the full 262k window: identity, payload, KV arithmetic, the spec-decode chain, deployed flags, the client contract, and what to check when it misbehaves."
 tags:
   - llm
-  - frank-small
+  - frank-secondary
   - dankclaw
   - llama.cpp
   - rtx3090
@@ -14,25 +14,25 @@ tags:
 timestamp: 2026-09-17
 ---
 
-# frank-small — llama.cpp Gemma4-12B QAT Q4_K_M (MTP + ngram-mod)
+# frank-secondary — llama.cpp Gemma4-12B QAT Q4_K_M (MTP + ngram-mod)
 
-> **ACTIVE since 2026-09-17.** Replaces [[frank-small-qwen38-gsq-rco]]
+> **ACTIVE since 2026-09-17.** Replaces [[frank-secondary-qwen38-gsq-rco]]
 > (ISTA-DASLab Qwen3.8-27B-GSQ-RCO IQ3_S, now the rollback target) and
-> [[frank-small-vllm-gemma4]] (the same-day vLLM experiment, also the
-> rollback target). Same port (7771), same alias (`frank-small`), same
+> [[frank-secondary-vllm-gemma4]] (the same-day vLLM experiment, also the
+> rollback target). Same port (7771), same alias (`frank-secondary`), same
 > client contract surface.
 
 ## Identity
 
-- **Endpoint:** `frank-small` at `192.168.0.143:7771` (Dankclaw).
+- **Endpoint:** `frank-secondary` at `192.168.0.143:7771` (Dankclaw).
 - **Runtime:** mainline llama.cpp master `37b53fd`, CUDA build for sm_86 with
   `-DGGML_CUDA_FA_ALL_QUANTS=ON`
   (`/home/dank/GitPackages/llama.cpp-mainline/build-cuda/bin/llama-server`).
   The FA_ALL_QUANTS flag is required: without it, q8_0 KV + flash attention
   collapses prefill ~12x on CUDA.
-- **Service shape:** root systemd unit `scripts/frank-small.service`
-  (`User=dank`), launched by `scripts/serve-frank-small.sh` with tunables from
-  `scripts/frank-small.env`.
+- **Service shape:** root systemd unit `scripts/frank-secondary.service`
+  (`User=dank`), launched by `scripts/serve-frank-secondary.sh` with tunables from
+  `scripts/frank-secondary.env`.
 
 ## Shape
 
@@ -90,7 +90,7 @@ fault).
 
 ## Client contract
 
-- **Alias:** `frank-small` (the `id` at `/v1/models`).
+- **Alias:** `frank-secondary` (the `id` at `/v1/models`).
 - **Reasoning:** extracted to `reasoning_content` (`--reasoning-format
   deepseek`, unlimited budget).
 - **Tools:** supported (`--jinja` is pinned even though this build defaults
@@ -109,12 +109,12 @@ pp ~965 t/s, tg 257–302 t/s, idle 23,300 MiB of 24,576. Larger ubatches
 
 ## Rollback
 
-Payload and runtime for [[frank-small-qwen38-gsq-rco]] (ISTA-DASLab
-Qwen3.8-27B-GSQ-RCO IQ3_S) remain intact on disk. [[frank-small-vllm-gemma4]]
+Payload and runtime for [[frank-secondary-qwen38-gsq-rco]] (ISTA-DASLab
+Qwen3.8-27B-GSQ-RCO IQ3_S) remain intact on disk. [[frank-secondary-vllm-gemma4]]
 is the same-day vLLM experiment (also a rollback target).
 
 # Citations
 
 - Model + mmproj + MTP sidecar: https://huggingface.co/HauhauCS
 - llama.cpp: https://github.com/ggml-org/llama.cpp
-- Serve script: `scripts/serve-frank-small.sh`; tunables: `scripts/frank-small.env`
+- Serve script: `scripts/serve-frank-secondary.sh`; tunables: `scripts/frank-secondary.env`

serverAppConfigs-docs/LLM/frank-secondary-llama-gemma4-q4km-200704.md

@@ -0,0 +1,111 @@
+# frank-secondary — llama.cpp CUDA, Gemma4-26B-A4B Q4_K_M, 1×200,704 (Dankclaw)
+
+**RETIRED 2026-10-07** — same day it went live. Superseded by
+[[frank-secondary-llama-qwen35-9b-q6k-2x200704]] (Qwen3.5-9B Q6_K at
+2 × 200,704 with exact KV); the payload stays at
+`/home/dank/models/Gemma4-26B-A4B/` as the rollback. This page keeps the
+Gemma4 KV-billing law (the `attention_k_eq_v` pair-billing measurement) and
+the drafter-cost boots, both of which outlive the model.
+
+## Payload
+
+|File|Path|Bytes|
+|---|---|---|
+|Main (Q4_K_M)|`/home/dank/models/Gemma4-26B-A4B/Gemma4-26B-A4B-QAT-Uncensored-HauhauCS-Balanced-Q4_K_M.gguf`|16,796,015,520 on disk; the engine reports 16,780,192,888|
+|Vision (mmproj BF16)|`/home/dank/models/Gemma4-26B-A4B/mmproj-Gemma4-26B-A4B-QAT-Uncensored-HauhauCS-Balanced-BF16.gguf`|~1.195 GiB|
+|MTP assistant (on disk, NOT loaded)|`/home/dank/models/Gemma4-26B-A4B/mtp-gemma-4-26B-A4B-it.gguf`|~0.252 GiB|
+
+Repo: `HauhauCS/Gemma4-26B-A4B-QAT-Uncensored-HauhauCS-Balanced-MTP`
+(base `google/gemma-4-26B-A4B-it`). Downloaded 2026-10-07 by plain
+`curl -fL` (no token needed, ungated).
+
+## Shape
+
+```
+--ctx-size 200704 --parallel 1        # ONE conversation, Frank's 200k cap
+--flash-attn on
+--cache-type-k f16 --cache-type-v q8_0   # exact K, FP8-class V (q8_0)
+--batch-size 4096 --ubatch-size 1024
+--n-gpu-layers 99
+--log-verbosity 4                     # debug: KV layout, buffers, graph capture
+--spec-type none                      # MTP OFF (see below)
+--mmproj <BF16 projector>
+--jinja --reasoning auto --reasoning-format deepseek
+```
+
+Measured boot at **q8_0 K + q8_0 V, ubatch 2048, MTP off** (2026-10-07
+morning): **21,890 MiB used / 2,235 MiB free** (`nvidia-smi`). With MTP ON
+(the assistant loaded) the same shape was **23,806 / 319** — the drafter's
+context costs ~1.9 GiB because its KV is built against the target's full
+window ([[Llama Cpp Serving Sizing]]).
+
+**Deployed shape (2026-10-07 evening, Frank): K = f16, ubatch 1024, verbosity
+4. Measured boot: 21,868 MiB used / 2,257 MiB free** — i.e. the f16-K raise
+(+2,161 MiB of KV, see the accounting below) was paid for by the ubatch cut
+(2048 → 1024 returns ~616 MiB of compute buffer) plus slot-geometry variance.
+Exact K came essentially free. The verbosity-4 ledger (journal, `llama_kv_cache`
+lines): model 16,002.91 MiB (CUDA0) + 577.50 MiB (CPU-mapped), **KV 3,001.25
+MiB (5 full layers) + 306.25 MiB (25 SWA rings)**, compute buffers 926.39 +
+150.63 MiB, output buffer 1.00 MiB.
+
+**Measured decode: 139.9 tok/s** (`/completion` timings, temp 0, 256 tokens,
+deployed shape) — the ~4 B-active MoE is at home on the 3090. The model
+thinks: a 256-token budget on a short prompt can be consumed entirely by
+`reasoning_content` with empty `content` and `finish_reason: length`, so
+harness requests must budget for thinking (same law as the uncensored Swift
+payload). Vision probe on this build: solid-red 512 px PNG via the OpenAI
+`image_url` part → `Red`, `prompt_tokens` 151 (the image reached the model).
+
+Decode is expected ubatch-invariant (Swift-era 3090 law; the 139.9 t/s above
+is at ubatch 1024 — one rung measured, not a ladder).
+
+## Architecture facts
+
+Gemma4 MoE hybrid: 30 layers, 128 experts (8 active), n_embd 2816, 262,144
+native window, n_vocab 262,144. Attention is hybrid per
+`gemma4.attention.sliding_window_pattern` (read from the file at load): **25
+sliding-window layers (window 1024, `n_head_kv = 8`) + 5 full-attention layers
+(indices 5, 11, 17, 23, 29, `n_head_kv = 2`)**, head_dim 256 for K and V in
+both classes. The KV pool is TWO caches: the full-context cache scales with
+the 5 layers at the full 200,704 window; the SWA cache is a fixed 1024-token
+ring per sliding layer.
+
+**The rate law (measured, exact):** bytes/token = `n_kv_heads × 256 × 6.125 B`
+per layer at f16 K + q8_0 V — `attention_k_eq_v` does **NOT** shrink the
+allocation: `Vcur = Kcur` is a compute-graph mapping, the cache reserves K and
+V slots and, with `k_eq_v`, reserves the pair **twice** (6.125 = 2 × (2 B f16 +
+1.0625 B q8_0)). Verified against the boot: 5 × 2 × 256 × 6.125 = 15,680
+B/token × 200,704 = **3,001.25 MiB** ✓ and 25 × 1024 × 8 × 256 × 6.125 =
+**306.25 MiB** ✓. At q8_0 K + q8_0 V the rate is 2.125 B/elem: the same shape
+costs 1,041 + 106 = **1,147 MiB**, so the f16-K raise is **+2,161 MiB**. The
+`--cache-type-v` flag is honoured, not inert.
+
+The llama.cpp master build at commit `5fc4f3c` supports the arch
+(`src/models/gemma4.cpp`, `LLM_ARCH_GEMMA4 = "gemma4"`). Note: `grep -c`
+arch names on the packed binary returns 0 for EVERY arch (including the
+`qwen3_5` it serves) — check the source tree, not the binary.
+
+## MTP law
+
+MTP for Gemma4 is a SEPARATE file (`mtp-gemma-4-26B-A4B-it.gguf`, arch
+`gemma4-assistant`), loaded via `--spec-draft-model` beside
+`--spec-type draft-mtp` — unlike Swift, whose MTP head ships inside the main
+GGUF. The serve script gained `SPEC_DRAFT_FILE` for this. 2026-10-07 (Frank):
+**MTP disabled** — `SPEC_TYPE=none` strips both spec flags (flags move as a
+set). The assistant payload stays on disk; setting `SPEC_TYPE=draft-mtp` +
+`SPEC_DRAFT_FILE` re-enables it at the cost of ~1.9 GiB (boots at 319 MiB
+free).
+
+## Verification
+
+- `curl http://192.168.0.143:7771/health` → `{"status":"ok"}`
+- `/v1/models` reports `n_ctx: 200704`, `ftype: Q4_K - Medium`
+- Vision probe: solid-color PNG ≥ 256 px via OpenAI `image_url` part,
+  expect the exact color word.
+
+## Rollback
+
+Revert to the Swift trio: `MODEL_FILE=/home/dank/models/Swift15-UncIQ3XXS/…`,
+`CTX_SIZE=229376`, `PARALLEL=2`, in-file `SPEC_TYPE=draft-mtp` — the Swift
+payload remains on disk. See
+[[frank-secondary-llama-swift15-unc-iq3xxs-mtp-114688]].

serverAppConfigs-docs/LLM/frank-secondary-llama-mimo-q5km.md

@@ -1,10 +1,10 @@
 ---
 type: reference
-title: "frank-small on Dankclaw — llama.cpp, MiMo-V2.6-Distill-Qwen-9B Q5_K_M GGUF (retired)"
-description: "The retired 2026-10-02/03 frank-small runbook — the Q5_K_M rung at two wide unquantized-KV slots on the RTX 3090, superseded 2026-10-03 by the Q6_K file. Kept as the rollback reference: it carries the measured boot-pair table that prices every pool shape on this card, the slot-window contract, and the misbehaves tree."
+title: "frank-secondary on Dankclaw — llama.cpp, MiMo-V2.6-Distill-Qwen-9B Q5_K_M GGUF (retired)"
+description: "The retired 2026-10-02/03 frank-secondary runbook — the Q5_K_M rung at two wide unquantized-KV slots on the RTX 3090, superseded 2026-10-03 by the Q6_K file. Kept as the rollback reference: it carries the measured boot-pair table that prices every pool shape on this card, the slot-window contract, and the misbehaves tree."
 tags:
   - llm
-  - frank-small
+  - frank-secondary
   - dankclaw
   - llama-cpp
   - rtx3090
@@ -14,17 +14,17 @@ tags:
 timestamp: 2026-10-03
 ---
 
-# frank-small — llama.cpp MiMo-V2.6 Q5_K_M (RTX 3090, retired)
+# frank-secondary — llama.cpp MiMo-V2.6 Q5_K_M (RTX 3090, retired)
 
 > **RETIRED 2026-10-03** — superseded by
-> [[frank-small-llama-mimo-q6k-2x200704]]: the ~3.5 GiB this shape left free
+> [[frank-secondary-llama-mimo-q6k-2x200704]]: the ~3.5 GiB this shape left free
 > bought the **Q6_K quant rung** (same runtime, same 2 × 200,704 pool, same
 > f16 KV), which measures 2,849 MiB free and 94.44 tok/s — 10 % slower
 > decode is the measured price of the higher rung on this bandwidth-bound
 > card. This file remains the rollback reference and the owner of the
 > measured boot-pair table.
 
-> Its own history: one quant step down from [[frank-small-llama-mimo-q6k]]
+> Its own history: one quant step down from [[frank-secondary-llama-mimo-q6k]]
 > (2026-10-01), resized twice on 2026-10-02 (three contexts with vision, then
 > the 2–3 GiB-band pool), and on 2026-10-03 morning reshaped to **two
 > conversations of 200,704 tokens with the KV VALUES unquantized** —
@@ -36,11 +36,11 @@ timestamp: 2026-10-03
 
 |Field|Value|
 |---|---|
-|Endpoint|`192.168.0.143:7771`, served alias `frank-small`|
+|Endpoint|`192.168.0.143:7771`, served alias `frank-secondary`|
 |Runtime|llama.cpp **master** (qwen35 model support), CUDA build `sm_86`, at `/home/dank/GitPackages/llama-master/build-cuda/bin/llama-server` — unchanged across the quant swap|
-|Payload|`bartowski/MiMo-V2.6-Distill-Qwen-9B-GGUF` **Q5_K_M**, 6,876,124,704 B (6.40 GiB), sha256 `04cf46f2b5584ef922697d6c063f94879434fb86c467f1a09e52daf0cddaf35f` + the repo's `mmproj-MiMo-V2.6-Distill-Qwen-9B-f16.gguf`, 918,166,048 B, sha256 `ff348f3180a63188aa7285db85f550fe38acb61dd013c599eb8bad08d2cc2576` — both at `/home/dank/models/MiMo-Q5KM/`, fetched and byte- and sha256-verified by `scripts/download-frank-small.sh`|
-|Unit|root `frank-small.service` (repo `scripts/frank-small.service`, `User=dank`, relinked), env `scripts/frank-small.env`, serve `scripts/serve-frank-small.sh`|
-|Repo pins|`tests/frank_small_unit.bats` (stub-argv seam + the VRAM arithmetic), `deploy_llm_small` gates in `scripts/deploy-remote.sh`|
+|Payload|`bartowski/MiMo-V2.6-Distill-Qwen-9B-GGUF` **Q5_K_M**, 6,876,124,704 B (6.40 GiB), sha256 `04cf46f2b5584ef922697d6c063f94879434fb86c467f1a09e52daf0cddaf35f` + the repo's `mmproj-MiMo-V2.6-Distill-Qwen-9B-f16.gguf`, 918,166,048 B, sha256 `ff348f3180a63188aa7285db85f550fe38acb61dd013c599eb8bad08d2cc2576` — both at `/home/dank/models/MiMo-Q5KM/`, fetched and byte- and sha256-verified by `scripts/download-frank-secondary.sh`|
+|Unit|root `frank-secondary.service` (repo `scripts/frank-secondary.service`, `User=dank`, relinked), env `scripts/frank-secondary.env`, serve `scripts/serve-frank-secondary.sh`|
+|Repo pins|`tests/frank_secondary_unit.bats` (stub-argv seam + the VRAM arithmetic), `deploy_llm_secondary` gates in `scripts/deploy-remote.sh`|
 
 ## Shape and the KV contract
 
@@ -128,7 +128,7 @@ The per-slot window is the whole price, in both directions:
   "Brown.", i.e. it generated from text alone. `/completion` takes `images`
   but refuses a request without `messages` in this build, so chat is the only
   vision path.
-- Setting `MMPROJ_FILE=` (explicitly empty) in `scripts/frank-small.env` drops
+- Setting `MMPROJ_FILE=` (explicitly empty) in `scripts/frank-secondary.env` drops
   `--mmproj` and returns ~1.4 GiB of the card to KV — the documented text-only
   opt-out. `MMPROJ_FILE` pointing at a file that does not exist is a boot
   refusal, never a silent fallback to blindness.
@@ -145,15 +145,15 @@ traffic first. The bats seam fails if a draft flag ships unannounced.
 
 |Target|How|
 |---|---|
-|Previous shape: 3 × 180,224 + q8_0 V (2026-10-02)|`scripts/frank-small.env` `CTX_SIZE=540672 PARALLEL=3 CACHE_TYPE_V=q8_0` (measured 21,684 MiB used, 2,441 free) and `contextWindow: 180224` in `models.yml` — choose it when three simultaneous conversations matter more than per-slot window or exact V|
-|Q6_K, same runtime|`scripts/frank-small.env` `MODEL_FILE=/home/dank/models/MiMo-Q6K/MiMo-V2.6-Distill-Qwen-9B-Q6_K.gguf` + `MMPROJ_FILE` in that dir — **this is the deployed rung since 2026-10-03** ([[frank-small-llama-mimo-q6k-2x200704]]): at 2 × 200,704 it fits with 2,849 MiB free, no pool cut needed (the older "+1.4 GiB, drop 49,152 tokens" estimate held only for the 3-slot shapes)|
+|Previous shape: 3 × 180,224 + q8_0 V (2026-10-02)|`scripts/frank-secondary.env` `CTX_SIZE=540672 PARALLEL=3 CACHE_TYPE_V=q8_0` (measured 21,684 MiB used, 2,441 free) and `contextWindow: 180224` in `models.yml` — choose it when three simultaneous conversations matter more than per-slot window or exact V|
+|Q6_K, same runtime|`scripts/frank-secondary.env` `MODEL_FILE=/home/dank/models/MiMo-Q6K/MiMo-V2.6-Distill-Qwen-9B-Q6_K.gguf` + `MMPROJ_FILE` in that dir — **this is the deployed rung since 2026-10-03** ([[frank-secondary-llama-mimo-q6k-2x200704]]): at 2 × 200,704 it fits with 2,849 MiB free, no pool cut needed (the older "+1.4 GiB, drop 49,152 tokens" estimate held only for the 3-slot shapes)|
 |Two 262,144 windows|`CTX_SIZE=524288 PARALLEL=2 CACHE_TYPE_V=q8_0` (measured 20,190 MiB used, 4,386 free **with q8_0 V** — with f16 V the same shape is 16,384 MiB of KV + ~7,986 non-KV and does not fit) and `contextWindow: 262144` in `models.yml` — choose it when a single conversation needs the native window|
-|Vision off|`MMPROJ_FILE=` in the env file; the deployer gate then has to be satisfied too, so point `LLM_SMALL_MMPROJ` at any real projector file or drop the gate in the same commit|
-|SGLang + INT8|[[frank-small-sglang-mimo-v26]] — venv and old unit/env/serve trio still in place; the INT8 payload was staged to `/home/dank/.AGENTTRASH/20261002-retired-llm-payloads/` on 2026-10-02, so restore it there or re-fetch with `old/download-frank-small-sglang-int8.sh`|
+|Vision off|`MMPROJ_FILE=` in the env file; the deployer gate then has to be satisfied too, so point `LLM_SECONDARY_MMPROJ` at any real projector file or drop the gate in the same commit|
+|SGLang + INT8|[[frank-secondary-sglang-mimo-v26]] — venv and old unit/env/serve trio still in place; the INT8 payload was staged to `/home/dank/.AGENTTRASH/20261002-retired-llm-payloads/` on 2026-10-02, so restore it there or re-fetch with `old/download-frank-secondary-sglang-int8.sh`|
 
 ## When it misbehaves
 
-- Port 7771 dark: `journalctl -u frank-small -n 50`; the serve script exits 1
+- Port 7771 dark: `journalctl -u frank-secondary -n 50`; the serve script exits 1
   naming the missing file if the binary or GGUF moved.
 - `No CUDA GPUs are available`: the 3090 vanished from the bus or the UVM node
   is missing — `nvidia-modprobe -u -c=0` (the serve script runs it).

serverAppConfigs-docs/LLM/frank-secondary-llama-mimo-q6k-2x200704.md

@@ -1,10 +1,10 @@
 ---
 type: reference
-title: "frank-small on Dankclaw — llama.cpp, MiMo-V2.6 Q6_K, 2 x 200704, f16 KV"
-description: "The active frank-small runbook — llama.cpp master (CUDA sm_86) serving bartowski's MiMo-V2.6-Distill-Qwen-9B Q6_K GGUF on the RTX 3090: two conversations of 200,704 tokens with fully unquantized f16 KV, vision through the bundled projector, and the boot-pair arithmetic that prices every pool change."
+title: "frank-secondary on Dankclaw — llama.cpp, MiMo-V2.6 Q6_K, 2 x 200704, f16 KV (RETIRED)"
+description: "The RETIRED frank-secondary runbook (2026-10-03 → 10-05 evening, rollback target) — llama.cpp master (CUDA sm_86) serving bartowski's MiMo-V2.6-Distill-Qwen-9B Q6_K GGUF on the RTX 3090: two conversations of 200,704 tokens with fully unquantized f16 KV, vision through the bundled projector, and the boot-pair arithmetic that prices every pool change. Superseded by the Swift-1.5-27B IQ3_S-mtp swap."
 tags:
   - llm
-  - frank-small
+  - frank-secondary
   - dankclaw
   - llama-cpp
   - gguf
@@ -12,29 +12,31 @@ tags:
 timestamp: 2026-10-03
 ---
 
-# frank-small — llama.cpp MiMo Q6_K, 2 × 200,704, f16 KV
+# frank-secondary — llama.cpp MiMo Q6_K, 2 × 200,704, f16 KV
 
-> **ACTIVE since 2026-10-03.** The third shape Frank bought in two days on
-> this card: the freed third slot paid for **exact KV values**
-> ([[frank-small-llama-mimo-q5km]] documents that step and remains the
-> rollback), and the ~3.5 GiB still free then paid for the **Q6_K quant rung**
-> — the same file this endpoint served on 2026-10-01, come back as an
-> upgrade rather than a compromise. The pool held at 401,408 (2 × 200,704);
-> only the weights moved up.
+> **RETIRED 2026-10-05 (evening).** Superseded by
+> [[frank-secondary-llama-swift15-unc-iq3xxs-mtp-114688]] — the Swift-1.5-27B
+> RCO-IQ3_XXS-mtp swap (uncensored, MTP drafted). Two facts for the trail:
+> committed earlier the same day (77decb5..380aafa) was **never deployed** —
+> THIS MiMo shape was the live payload when Swift cut over; (2) the KV
+> contract Frank set here (f16 K + f16 V, "no further quantization on this
+> endpoint") was superseded the same evening by the Swift swap's explicit
+> q8_0/q8_0 order. This page stays as the rollback and the measured
+> boot-pair record. Payload on disk: `/home/dank/models/MiMo-Q6K/`.
 
 ## Identity
 
 | | |
 |---|---|
 | Host / device | Dankclaw (192.168.0.143), RTX 3090, 24,576 MiB |
-| Endpoint | `http://192.168.0.143:7771` — alias `frank-small`, `/v1` OpenAI surface |
+| Endpoint | `http://192.168.0.143:7771` — alias `frank-secondary`, `/v1` OpenAI surface |
 | Runtime | llama.cpp master, CUDA sm_86 build `…/llama-master/build-cuda/bin/llama-server` (`GGML_CUDA_FA_ALL_QUANTS=ON`) |
-| Unit | root systemd `frank-small.service` ([`scripts/frank-small.service`](../../scripts/frank-small.service)), `User=dank`, `Restart=on-failure`, `LimitMEMLOCK=infinity` |
-| Serve script | [`scripts/serve-frank-small.sh`](../../scripts/serve-frank-small.sh), tunables [`scripts/frank-small.env`](../../scripts/frank-small.env) |
+| Unit | root systemd `frank-secondary.service` ([`scripts/frank-secondary.service`](../../scripts/frank-secondary.service)), `User=dank`, `Restart=on-failure`, `LimitMEMLOCK=infinity` |
+| Serve script | [`scripts/serve-frank-secondary.sh`](../../scripts/serve-frank-secondary.sh), tunables [`scripts/frank-secondary.env`](../../scripts/frank-secondary.env) |
 | Weights | `/home/dank/models/MiMo-Q6K/MiMo-V2.6-Distill-Qwen-9B-Q6_K.gguf` — bartowski/MiMo-V2.6-Distill-Qwen-9B-GGUF **Q6_K**, 7,793,710,624 B, sha256 `ef96d05a…` (qwen3_5 hybrid: 32 layers, 8 full-attention + 24 Gated DeltaNet, native 262,144, no MTP tensors) |
 | Projector | `…/MiMo-Q6K/mmproj-MiMo-V2.6-Distill-Qwen-9B-f16.gguf` — 918,166,048 B, sha256 `ff348f…` (hardlink of the Q5KM dir's copy, link-count-2 verified; the dir is self-contained) |
-| Fetch | [`scripts/download-frank-small.sh`](../../scripts/download-frank-small.sh) — `-f`, byte count + LFS sha256 verified, `--verify-only`; both files verified against the pinned sha on the host before this deploy |
-| Seam | `tests/frank_small_unit.bats` (argv assertions against a stub engine: payload path, slot geometry, KV pair, batch pair, projector opt-out) |
+| Fetch | [`scripts/download-frank-secondary.sh`](../../scripts/download-frank-secondary.sh) — `-f`, byte count + LFS sha256 verified, `--verify-only`; both files verified against the pinned sha on the host before this deploy |
+| Seam | `tests/frank_secondary_unit.bats` (argv assertions against a stub engine: payload path, slot geometry, KV pair, batch pair, projector opt-out) |
 
 ## Shape and KV contract
 
@@ -65,7 +67,7 @@ compaction-prompt fit by 1,236 tokens — or revert V to `q8_0`), and both
 were declined. Re-open only on evidence the Q6_K residual matters.
 
 **Pool prices on this card come from boots, never formulas** — the measured
-boot-pair table lives in [[frank-small-llama-mimo-q5km]] (*Shape and KV
+boot-pair table lives in [[frank-secondary-llama-mimo-q5km]] (*Shape and KV
 contract*) and still prices every rollback shape.
 
 ## Compute-buffer geometry (2026-10-05)
@@ -76,7 +78,7 @@ measured rather than assumed, because the predecessor shape recorded **"ubatch
 
 **That failure was a different shape, and it did not reproduce here.** The
 service itself is the bench (one card holds one server, so no scratch port):
-the old rung was re-booted through a transient `FRANK_SMALL_ENV_FILE` drop-in
+the old rung was re-booted through a transient `FRANK_SECONDARY_ENV_FILE` drop-in
 and reproduced the 2026-10-03 boot **byte-identically** — 21,276 MiB used /
 2,849 MiB free — which makes the ubatch the only variable in the comparison.
 
@@ -97,7 +99,7 @@ Three readings:
 - **The price is 616 MiB out of a 2.8 GiB margin.** 2,279 MiB free is still
   inside the 2–3 GiB band, at its bottom edge. `4096/1536` is the middle rung,
   and the pool's 12,288-token (384 MiB) ladder in
-  [[frank-small-llama-mimo-q5km]] is the other way to buy room back.
+  [[frank-secondary-llama-mimo-q5km]] is the other way to buy room back.
 
 **Why 2048 is right HERE while 512 was right on the R9700 sibling: the curve is
 backend-specific.** The 2× R9700 layer split peaks at ubatch 512 and is 10 %
@@ -123,7 +125,7 @@ that is the pair the curve was measured at.
 
 ## Client contract
 
-llama.cpp surface, live-verified on this model family: model id `frank-small`,
+llama.cpp surface, live-verified on this model family: model id `frank-secondary`,
 native tools via `--jinja` (`tool_calls` in the response), thinking in
 **`reasoning_content`**, vision via `image_url` content parts. Two vision
 traps, both measured here:
@@ -145,13 +147,13 @@ so the harness compacts before llama.cpp answers a longer prompt with a 400.
 
 |Target|How|
 |---|---|
-|Q5_K_M at the same shape|`scripts/frank-small.env` `MODEL_FILE=/home/dank/models/MiMo-Q5KM/MiMo-V2.6-Distill-Qwen-9B-Q5_K_M.gguf` (+ `MMPROJ_FILE` in that dir) and the deployer default — buys back 875 MiB (3,571 free measured) **and the 10 % decode rate** (104.84 tok/s measured). Payload sha-verified on disk|
+|Q5_K_M at the same shape|`scripts/frank-secondary.env` `MODEL_FILE=/home/dank/models/MiMo-Q5KM/MiMo-V2.6-Distill-Qwen-9B-Q5_K_M.gguf` (+ `MMPROJ_FILE` in that dir) and the deployer default — buys back 875 MiB (3,571 free measured) **and the 10 % decode rate** (104.84 tok/s measured). Payload sha-verified on disk|
 |3 × 180,224 + q8_0 V|`CTX_SIZE=540672 PARALLEL=3 CACHE_TYPE_V=q8_0` (measured 21,684 used / 2,441 free **with Q5_K_M** — Q6_K does NOT fit that shape inside the floor; pair them explicitly)|
-|SGLang + INT8|[[frank-small-sglang-mimo-v26]] — payload staged to `/home/dank/.AGENTTRASH/20261002-retired-llm-payloads/`|
+|SGLang + INT8|[[frank-secondary-sglang-mimo-v26]] — payload staged to `/home/dank/.AGENTTRASH/20261002-retired-llm-payloads/`|
 
 ## When it misbehaves
 
-Same decision tree as [[frank-small-llama-mimo-q5km]] (*When it misbehaves*):
+Same decision tree as [[frank-secondary-llama-mimo-q5km]] (*When it misbehaves*):
 `No CUDA GPUs are available` ⇒ `nvidia-modprobe -u -c=0`; a 400 naming
 200,704 ⇒ the conversation outgrew one slot, not the pool; slow decode after
 a shape change ⇒ check `nvidia-smi` for KV pushed into unified memory; queue

serverAppConfigs-docs/LLM/frank-secondary-llama-mimo-q6k.md

@@ -1,10 +1,10 @@
 ---
 type: reference
-title: "frank-small on Dankclaw — llama.cpp, MiMo-V2.6-Distill-Qwen-9B Q6_K GGUF (retired)"
-description: "The retired 2026-10-01 frank-small runbook — the first llama.cpp + GGUF shape on the RTX 3090: two native-window sessions, the asymmetric KV contract (f16 K + q8_0 V) that motivated the move off SGLang, the VRAM fit at Q6_K, and what to check when it misbehaves. Superseded the same night by the Q5_K_M payload."
+title: "frank-secondary on Dankclaw — llama.cpp, MiMo-V2.6-Distill-Qwen-9B Q6_K GGUF (retired)"
+description: "The retired 2026-10-01 frank-secondary runbook — the first llama.cpp + GGUF shape on the RTX 3090: two native-window sessions, the asymmetric KV contract (f16 K + q8_0 V) that motivated the move off SGLang, the VRAM fit at Q6_K, and what to check when it misbehaves. Superseded the same night by the Q5_K_M payload."
 tags:
   - llm
-  - frank-small
+  - frank-secondary
   - dankclaw
   - llama-cpp
   - rtx3090
@@ -14,10 +14,10 @@ tags:
 timestamp: 2026-10-01
 ---
 
-# frank-small — llama.cpp MiMo-V2.6 Q6_K (RTX 3090)
+# frank-secondary — llama.cpp MiMo-V2.6 Q6_K (RTX 3090)
 
 > **RETIRED 2026-10-01 → 10-02** (one night): superseded by
-> [[frank-small-llama-mimo-q5km]], the same runtime and shape one quant rung
+> [[frank-secondary-llama-mimo-q5km]], the same runtime and shape one quant rung
 > down. The Q6_K file stays at `/home/dank/models/MiMo-Q6K/` as the rollback
 > target.
 >
@@ -29,11 +29,11 @@ timestamp: 2026-10-01
 
 |Field|Value|
 |---|---|
-|Endpoint|`192.168.0.143:7771`, served alias `frank-small`|
+|Endpoint|`192.168.0.143:7771`, served alias `frank-secondary`|
 |Runtime|llama.cpp **master** (qwen35 model support), CUDA build `sm_86`, at `/home/dank/GitPackages/llama-master/build-cuda/bin/llama-server`|
 |Payload|`bartowski/MiMo-V2.6-Distill-Qwen-9B-GGUF` **Q6_K**, 7,793,710,624 B, sha256 `ef96d05a2ddf2cbb450d1af1ac3860ec769d3575bafa70692ee5609bda3fad7d`, at `/home/dank/models/MiMo-Q6K/`|
-|Unit|root `frank-small.service` (repo `scripts/frank-small.service`, `User=dank`, relinked), env `scripts/frank-small.env`, serve `scripts/serve-frank-small.sh`|
-|Repo pins|`tests/frank_small_unit.bats` (stub-argv seam), `deploy_llm_small` gates in `scripts/deploy-remote.sh`|
+|Unit|root `frank-secondary.service` (repo `scripts/frank-secondary.service`, `User=dank`, relinked), env `scripts/frank-secondary.env`, serve `scripts/serve-frank-secondary.sh`|
+|Repo pins|`tests/frank_secondary_unit.bats` (stub-argv seam), `deploy_llm_secondary` gates in `scripts/deploy-remote.sh`|
 
 ## Shape and the KV contract
 
@@ -100,7 +100,7 @@ use the parent of the 2026-10-01 night swap commit).
 
 ## When it misbehaves
 
-- Port 7771 dark: `journalctl -u frank-small -n 50`; the serve script
+- Port 7771 dark: `journalctl -u frank-secondary -n 50`; the serve script
   exits 1 naming the missing file if the binary or GGUF moved.
 - `No CUDA GPUs are available`: the 3090 vanished from the bus or the UVM
   node is missing — `nvidia-modprobe -u -c=0` (the serve script runs it).