The harness clamps max_tokens to min(45056, contextWindow - est - 4096),
its chars/4 est under-counts the rendered prompt by ~3 % (measured:
152,938 -> 157,388 and 158,906 -> 165,301), and compaction fires only at
85 % -- so with window == engine ceiling 200192, every request in the
est ~146k-184k zone was a guaranteed 400. HALOGEN_CTX=262144 (native) +
KV pool 524288; harness window 217088 = ceiling - 45k absorbs the
estimate error (worst-case request ~219k <= 262144). models.yml updated
out-of-repo: contextWindow 217088, maxTokens 45056.
The @data path allowlist on the post-auth listener did not include /img/*,
so docs screenshots fell through to the index.html catch-all and the
browser got 200 text/html for every PNG (docs images broken live
2026-10-08). Pinned by the homepage_caddy seam.
- style.css: green phosphor -> amber CRT-AMBIOS palette; docs styles for
screenshots, code blocks, key warning (full + bare mode)
- index/admin/docs: VT323 from the Google Fonts CDN
- docs.html: rewrite #chat/#models for the live fleet (flash-server v2,
radiance native FP8, Qwen3.5-9B Q6_K); new #api-access with OpenWebUI
key walkthrough + Opencode and Pi configs; three OpenWebUI screenshots
- services.json: AI card details match the current fleet
- deploy-remote.sh: ship shared/homepage/img/ to /srv/homepage/
- docs: Homepage.md theme wave + OpenWebUI.md picker access-control law
rad_arch_declare rejects the split tier (down block-fp8 + gate_up w4): the
qwen4exp_fp8 kernel serves one encoding per expert table (w4nl64a8h) with only
whole bf16 experts beside it. Recipe now sends gate_up AND down through the
GPTQ w4 rule (down calibrated by the Grams, gate_up uncalibrated), drops the
down-fp8 rule, and lowers the size floor 126000->108000 MiB for the ~19 GiB
lighter container. Convert script: checkpoint lives under models/ukisai/, and
the in-container path must keep the subdir (basename stripped it).
OpenWebUI model picker narrowed to exactly frank-secondary (connections
frank-large/frank-primary disabled in openai.api_configs, model table rebuilt,
stale frank-small model_id retargeted to the served alias). Documented the
auth-gated API surface (anonymous probes all 401; keys per account).
Authentik roster gains artem (pk 16) in the human-user template.
kassie (pk 14) and alex (pk 15, deleted pk 7 then recreated) now share the
ridley shape: <user>@franksdank.cloud + Non-Admin Users + jellyfin_users +
openwebui_users. Audit before destroy: the old alex row carried no MFA
device, token, entitlement, or proxy session - checked across the MFA
device tables, authentik_core_token, and providers_proxy_proxysession
(the fleet's column names differ from the ORM names: events.user is
denormalized JSON, certificatekeypair has no user_id). Events survive the
delete, so the audit trail keeps its shape.
Co-Authored-By: omp <omp@franksdank.cloud>
Deploy verified: nvidia-smi 21,436 MiB used / 2,689 free at ubatch 2048 -
the MiMo-priced projection (~21,430/~2,700) landed within 11 MiB, and the
verbosity-4 ledger confirms the KV law to the MiB (CUDA0 KV buffer
12,544.00 MiB = K(f16) 6,272 + V(f16) 6,272 = 32 KiB/token over the
401,408 pool), compute buffers 1,136 MiB CUDA0 + 848 MiB host, mmproj
879 + 248 MiB. Playwright gate: 33 passed.
Two lessons recorded: the 203/EXEC boot loop my shebang-less rewrite
caused (journal signature + the head -1 check), and that llama.cpp's
"n_ctx_seq < n_ctx_train" line is informational when the context is BELOW
the trained window - the Gemma4 drafter warning was the opposite case.
The unit's Description named the Swift-1.5 era (and said 2x131072/q8_0/
draft-mtp); it now names the Q6_K shape. Cosmetic, so it is NOT deployed
as its own restart: the next frank-secondary deploy relinks it.
Co-Authored-By: omp <omp@franksdank.cloud>
Capacity trade: the 9B dense-hybrid (qwen35: 32 layers = 8 full-attention
+ 24 Gated DeltaNet, 4 KV heads x 256 dim, native 262,144, MTP-less,
natively multimodal) costs ~7 GiB of weights where the 26B MoE cost
~16.6 GiB. The freed room buys back the second conversation slot and
exact KV: --ctx-size 401408 --parallel 2 (two 200,704 windows), f16 K +
f16 V, MTP off (the file carries no nextn/mtp tensors - flag-only enable
is impossible), vision via the publisher's BF16 mmproj, ubatch 1024
(the CUDA decode-optimal value), verbosity 4, publisher sampler recipe
(0.6/0.95/20/0) in models.yml.
Hosts with the MiMo-V2.6-Distill Q6_K boots at this geometry: 21,276 MiB
used / 2,849 free at ubatch 1024, 40,960 tokens of fp16 KV at ubatch
2048. Compaction stays routed here; window math is unchanged (maxTokens
16,384 keeps the worst-case harness request inside the slot).
Gemma4 payload (Q4_K_M + mmproj + MTP assistant) stays on disk as the
rollback; its runbook is retired in place, keeping the attention_k_eq_v
pair-billing law.
Co-Authored-By: omp <omp@franksdank.cloud>
Verbosity 4 paid for itself: the engine's own llama_kv_cache lines give the
exact accounting — the cache bills the K+V pair TWICE under attention_k_eq_v
(6.125 B/elem at f16 K + q8_0 V), reproducing 3,001.25 MiB (5 full layers at
n_head_kv 2) + 306.25 MiB (25 SWA rings at n_head_kv 8, window 1024) to the
byte. The earlier "bill K-only" note was wrong; full layers have 2 KV heads,
not 8.
Deployed boot: 21,868 used / 2,257 free — the f16-K raise (+2,161 MiB KV) is
funded by the ubatch 2048 -> 1024 cut (~616 MiB of compute buffer) plus
variance, so exact K came essentially free. Measured decode 139.9 t/s temp 0;
vision probe returns Red at prompt_tokens 151.
Payload: HauhauCS/Gemma4-26B-A4B-QAT-Uncensored Q4_K_M (16,796,015,520 B)
+ the publisher's BF16 mmproj, /home/dank/models/Gemma4-26B-A4B/.
Shape: ONE 200,704-token slot (--parallel 1; 2x200,704 does not fit the
24 GiB card), f16 K + q8_0 V under flash attention, batch 4096 / ubatch
1024, --log-verbosity 4, MTP OFF (the gemma4-assistant file is on disk,
not loaded: its draft KV is built against the target's full window,
measured +1.9 GiB -> 23,806/319 free, no margin beside the f16-K raise).
Measured boots (nvidia-smi): q8_0 K + q8_0 V / ubatch 2048 / MTP off =
21,890 used / 2,235 free; with the MTP assistant = 23,806 / 319.
Engine facts recorded: gemma4 hybrid attention (25 sliding-window + 5
full-attention layers) so KV scales with 5 layers only, and
attention_k_eq_v is honoured by build 5fc4f3c (pool bills K-only; the
measured q8_0 pool = 2.05 GiB = the K-only figure).
Serve script gained VERBOSITY (+ --log-verbosity in the argv), the
SPEC_DRAFT_FILE drafter path, and defaults/lore retargeted to Gemma4.
deploy-remote.sh payload gates follow the env. The frank-secondary bats
seam is rewritten for the new contract (argv-based) and fixes a
pre-existing vacuity: serve_argv regenerates the test env, so
mutations made before it were silently undone - tests that mutate now
exec through run_stage. frank-secondary is the fleet compaction hub
(200,704 is the only window that fits a worst-case compaction input).
The KLD engine exited clean with the record complete (kld.json rows:
50235, 24 GiB, 5 files), but the wrapper died rc=127: a mid-run scp of
the calib-shape edit shifted bash's lazy-read offset — the fleet's own
script-swap law, biting on the same class as the autoresearch collision.
Resume path: SKIP_KLD=1 verifies the receipt (rows + size + files) and
goes straight to calib. Never scp a script a running process owns.
Execution facts, blocker trail (plugin-tap probe law, weights-slab
formula, KLD_MOUNT), engine verdict, and the post-chain order. Linked
from the docs index per the completeness seam; the cutover runbook
replaces it.
MNBT 65536 / seqs 8: MOE-W4.md:244 measures 640 tok/s at a 32k step
vs 930 at 55k — the KLD record's own rate confirmed step size drives
disk-tier prefill. 131k resident tokens fits the 3072 pool (~237k
tokens/rank at fp8). Cuts the 10M-token calibration from ~4.3 h to
~3 h.
apply-protected-cast.sh splices derive-protected-experts.py output into
the recipe's PROTECTED-BEGIN/END section (refuses to re-derive, trashes
the pre-edit recipe). kld-gate-swift15.sh boots the candidate on the
serving pin in --kld-ref mode (pool fully resident, no disk tier) and
enforces mean KL<=0.03 / top-1>=95% from the JSON report.
One-GPU serialization: the chain waits for the kld driver log to close
rc=0, sanity-checks kld-ref, boots the calibration server, pushes the
corpus at parallel 16 (4 engine slots; queue absorbs the rest), then
kills the server so its trap restores production.
kld died: 'cannot read --kld-corpus /data/kld.jsonl' — KLD_MOUNT was
referenced but never defined, so the corpus/ref-dir mounts silently
expanded to nothing. Add them (nested order: after the /data mount).
feat(convert-swift15-w4dn8): final recipe convert on 1.0.13
Convert with the SERVING pin so the .rad crosses no version boundary;
only the Gram files cross (RADGRAM2 magic, gram.r%d.%s.bin naming and
calib_gram/calib_resolve/calib_stem are byte-identical between the
1.0.13 and 1.2.0 libquant). Host-backend run: prod can stay up.
Gates: grams present (>=48), bf16 container deleted, disk floor,
rad-info gptq receipt, size floor 126000 MiB.
The weights slab derives as card - headroom - kv, and the disk tier
refuses under 9.57 GiB (4.21 layer buffers + 5.36 fixed). 4608 starved
it (8.68 budget, DID NOT FIT). 3072 leaves 10.2 for weights; 4x16k fp8
slots fit the pool, recording is prefill-bound so slot count only
queues pushes.
kld stays on 1.0.13 (record on the scoring engine) with --vram-kv-mib
4608 + 16 slots: the ratio-derived pool came up SHORT by 217 MiB at
32x16k. calib moves to 1.2.0: the RADIANCE_CALIB_TOKENS tap is a 1.2.0
qwen4exp plugin feature (1.0.13 plugin has the DIR half only), and
Grams are bf16 activations - engine-invariant. --vram-kv-mib 9984
covers 64x16k fp8 slots.
Engine v1.2.0 (Codeberg e95d397, Docker Hub tag 1.2.0, index digest
665775ed...) removes --expert-vs-cache-ratio. The explicit
--vram-weights-mib 26071 / --vram-kv-mib 3543 pair transcribes the live
1.0.13 boot ledger at ratio 0.82 (weights 25.46 GiB, KV 3.46 GiB per
card); bracket arms scale KV linearly in (1-r): 0.90 -> 1968, 0.95 -> 984.
Serve script, autoresearch harness, and bats gates follow; the bats
shape asserts are realigned to the deployed seqs=3/spec=2 sweep result
(95923d3, df6b5bb) they failed to track.
- download-frank-primary-swift15.sh: ukisai/Swift1.5-Qwen3.8-Flash-Next
(BF16, 131 shards + index + configs, 360037355038 B = 335.3 GiB, ungated)
via the vendored hfget.sh; verify = listed sizes + 131-shard weight-map
count + every shard's sha256 against the Hub LFS oid. Pattern cloned
from scripts/download-frank-primary.sh.
- qwen4exp-swift15-w4dn8-i8-hc8m.recipe: routed experts with DOWN at
block-fp8 (rtn codes=fp8_e4m3 block=128x128 scale=bf16, the served
FP8-native tile form; moe_gemm_fp8a8 reads it; minicpm5-2b is the
shipped bf16->fp8a8 precedent) and gate_up at w4nl64 GPTQ cd=3;
hc/MTP-fc fp8, trunk i8, lm_head i8, PLE fixed scale, draft_head u2
(inert). Protected-expert cast lines carry the BASE checkpoint's names
as a warm start; the derivation step rewrites them from Swift's own
calibration recording before the recipe is ever run (law documented
in the file).
23,120 used / 1,005 free at boot, 763 settled after a vision probe
(encoder buffers resident). Rung cost 746 MiB, not the transferable
616 prediction. Prefill baseline 1,209 t/s @ 3,760 recorded; decode
unchanged as the law says. 2048 = ceiling for this shape.
Frank's order the same night. The 1024->2048 rung measured +4.6 %
prefill for 616 MiB on the 3090 (2026-10-05 boot pair); the 112k cut
left 1,751 MiB free, so it rides with ~1.1 GiB predicted margin.
Decode is ubatch-invariant; batch 4096 >= 2048 keeps the seam happy.
Cross-cutting rename of the current llama.cpp endpoint on the RTX 3090
(port 7771) from frank-small to frank-secondary:
- Service trio: scripts/{frank-small.env,frank-small.service,
serve-frank-small.sh,download-frank-small.sh} → frank-secondary.*
- Test: tests/frank_small_unit.bats → tests/frank_secondary_unit.bats
- Runbooks: serverAppConfigs-docs/LLM/frank-small-*.md →
frank-secondary-*.md (9 files); the FreeToken runbook stays
freetoken-frank-small.md (it documents the retired CUDA-era service)
- Identifiers: FRANK_SMALL→FRANK_SECONDARY, llm_small→llm_secondary,
LLM_SMALL_*→LLM_SECONDARY_*, deploy_llm_small→deploy_llm_secondary
- Historical references to the OLD frank-small (FreeToken/CUDA on the
3090, and the Dankserver service that became frank-medium) are
preserved verbatim — they describe what the service was called
at the time.
The 2026-10-06 cutover crash-looped frank-large 8 boots (03:09:39-03:11:42):
the engine's pin gate refused to start while host MemAvailable was ~74.7 GiB,
3.34 GiB short of the 78.11 GiB it needs (62.11 GiB checkpoint + 16 GiB floor).
It self-resolved in ~2 min when the dying 27B engine freed RAM, but left the
host with only ~1.8 GiB MemAvailable - a restart would crash-loop again.
serve-frank-large.sh now checks MemAvailable against the pin gate 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 overall-budget knob).
frank_large_unit.bats: FRANK_LARGE_MEMINFO seam + a refuse test. Runbook +
log: the pin-gate law, the incident, and the fix.
Frank
