The KV trim is inert: arena identical (5,648 slots) at 4.5 and 4.03 GiB
(planner buckets; the trim went to margin). Final 256k config
MAXLEN=262144 KV_BYTES=4328521728 prefetch off; full-speed alternative
MAXLEN=184224 at 3 GiB.
Asked for iterative decode/prefill improvement at a 32k working point
without touching the model or the quantization. There isn't one: gufo's
served settings are already at this engine's tuned point, so no config
change was made and none is proposed.
Measured on the repo's own prose and shell sources at 32,762 prompt
tokens, salted per run against a warm prefix cache, 3-5 repetitions:
* --prefill-chunk 512 -> 8192 moves prefill 410.7-412.4 t/s. A 16x change
in chunk size, 0.4% out. The lever is dead at this depth.
* --draft-tokens 5 -> 10 gives 13.79-15.86 t/s decode, all inside the
run-to-run noise. The 5-rep confirmation REVERSED the 3-rep leaders:
dt9+chunk4096 looked best and came last (12.86), the deployed config
came first (14.71).
* --draft-policy fixed vs adaptive: no effect. --min-draft-tokens:
rejected by the engine ("DFlash2 requires ... 1"). MTP: unsupported for
Q8 targets (token_embd.weight is Q8_0).
* --context 262144 -> 40960, tested as a diagnostic: no gain, and it
would break models.yml's declared contextWindow. Not shipped.
What does move the number is speculation itself — 6.65 t/s off vs ~15 t/s
on, 5.3x — and it is already enabled.
The two traps that cost most of the wall time are now in AGENTS.md's
benchmarking policy, because both produced FALSE results:
1. gufo bench cannot rank speculative settings and inverted the ranking.
Its corpus reports acceptance=0.933 +/- 0.000 with an identical trace
sha256 across draft widths — the same highly-predictable text every
run. It reported 29-37 t/s where real traffic gives ~15, and made
dt16 look 6% faster than dt7 when real text made it 14% slower.
2. A live A/B needs >=128 generated tokens. At 8, inter_token is
first-token noise; the first pass produced a nonsense ordering that
the 128-token pass reversed.
Plus the Server-Timing header is `ttft;dur=402.485` — the unit rides in
the NAME half, so splitting on "=" keys the dict "ttft;dur" and every
lookup silently returns nothing. Four configs reported "no Server-Timing"
while the header was present on all of them.
The disk was at 93% with 137G free. Nine retired payloads are now moved
to ~/.AGENTTRASH/20260928-retired-models/ with a MANIFEST.md giving a
restore command per item — moved, not deleted, and nothing is freed until
the purge is approved. Every one maps to a runtime already retired in the
runbook index.
Only the .gguf was taken from Qwen3.8-27B-UD-Q8_K_XL/: the live
frank-large DFlash2 drafter lives in that directory and stays put. The
manifest warns against restoring the directory wholesale.
Kept: checkpoints/ (23G) — unreferenced by the repo, but ComfyUI is live
and its workflows were not searched, so it is not confirmed dead. Also
logs/, because x4e-run.log is a SPARSE file (3.3G apparent, 4K real) and
frees nothing.
The reference check caught a live bug: serve-frank-small.sh's fallback
MODEL_DIR still pointed at the retired GSQ-RCO payload frank-small served
on 2026-09-16/17. frank-small.env has always overridden it, so the drift
was invisible — but a missing or unreadable env file would have turned a
config problem into a "missing asset" naming a model that is no longer
there. Repointed at Gemma4-12B-QAT/, which the env file already sets.
Not taken, by request: podman images (279G reclaimable, 95%) and the
uv+pip caches (73G). Together 352G of regenerable garbage, larger than
what was reclaimed here.
The 29.3G payload rollback target went with the rest, so the payload-only
rollback is now a re-fetch first; the runbook and AGENTS.md say so.
The target moves from unsloth/Qwen3.8-27B-GGUF:UD-Q8_K_XL to
ajgazin/Swift-1.5-Qwen3.8-27B-Uncensored-Dynamic-MTP-GGUF's UD-Q8_K_XL —
an abliterated fine-tune of the same base, under the non-OSI SWIFT OPEN
LICENCE 1.0. Port, alias, context, drafter and client contract are
unchanged.
Compatibility was settled from the GGUF headers before fetching anything:
both files parse to the same architecture (qwen35), the same 866 tensors
with identical names in identical order, zero per-tensor ggml_type or dim
mismatches, and the same MTP head. Two metadata differences that look
alarming are no-ops, confirmed against gufo's source — general.file_type is
deliberately not consulted (the file gufo already served records Q4_K_M
while containing no Q4_K tensor), and Swift's extra
qwen35.attention.recurrent_layers key is never read; layer kinds come from
full_attention_interval, which both files carry as 4 and which agrees.
The load test agreed: 9.2 s to listening, 66.5 GiB of the 116.7 GiB iGPU,
no loader warnings, and a green contract battery including a two-turn tool
loop.
The DFlash2 drafter did not move, so the payload is now two files in two
directories: DRAFTER_DIR is its own tunable. That broke the deployer's
stray-run sweep, which keyed on the target filename and would have stopped
matching — a stray run would then have held 7770 past the port gate. It
now keys on the drafter, which every frank-large command line carries.
The fetch script sends no auth header: the repo is public, and a token on
the curl command line is world-readable via /proc/<pid>/cmdline. The
anonymous transfer ran at 1.8 MiB/s; the host token through a mode-600
curl -K config reached 7 MiB/s.
Test seam: the mutation pass found four real gaps. Two fetch assertions
were satisfied by a header comment rather than the URL assignment, so
repointing a repository left them green, and nothing bound the env's
MODEL_FILE/DRAFTER_FILE to the filenames the fetch scripts download. Both
are now explicit. A harness bug worth recording: a bats --filter that
matches no test reports zero "not ok", indistinguishable from a mutation
that survived — the harness now checks the 1..N count. 16/16 caught.
The ad-hoc status commands kept producing wrong readings, most seriously
reporting "MemAvailable 0.0 GiB" on a host that actually had 240 GiB free.
Cause: /proc/meminfo separates key from value with a TAB, so
grep MemAvailable /proc/meminfo | tr -s " " | cut -d" " -f2
returns an empty field and awk prints 0.0. A false "host is out of memory"
reading is worse than no reading, because it invites an unnecessary abort.
dankstatus.sh parses on a colon split (tab-agnostic), cross-checks
MemAvailable against MemTotal before printing either, and refuses to print
a zero value. It also pins the rocm-smi CSV column order (device, total,
used) which was previously read swapped, and takes only the first line of
systemctl is-active/is-enabled, which emit more than one line here.
Incidental finding while validating: rocm-smi reports card2 (the 7700X iGPU)
as 0.50 GiB total, not the 123.0 GiB torch.cuda reports. torch was exposing
the GTT aperture. The device is smaller than documented, not larger.
Tests: 346/346 pass (no test surface touched).
Public, ungated repo so no auth header is sent — a token on the curl
command line is world-readable via /proc/<pid>/cmdline. Size and sha256
are the Hub LFS values (31457990272 / 9076f99a…), verified after fetch.
Not wired into frank-large.env: the gufo load test has to pass first.
Live numbers from the 2026-09-28 deploy: 13 s load to listening, RSS
30.6 GiB, ttft 549.8 ms / inter-token 24.2 ms on a short prompt, and a
green contract battery (health, ready, /v1/models at 262,144, content +
reasoning_content, tool_calls, SSE, and a two-turn tool loop).
Also recorded: gufo accepts every request field the harness sends in
extraBody and the full reasoning_effort set, both verified 200 — which
is why models.yml's reasoningEffortMap fold is now optional rather than
required, and why it is being kept anyway.
The .AGENTTRASH archive is ignored and exists only in a primary checkout,
so a filesystem grep fails there while passing in a worktree. git grep
searches the tracked tree, which is what the assertion is about.
gufo is a vertical inference engine built for exactly this hardware
(Ryzen AI MAX+ 395 / Radeon 8060S, gfx1151), and Dankclaw's frank-large
has swapped runtimes seven times in search of a better one. This one
ships no prebuilt Linux binary, so the runtime is BUILT FROM SOURCE on
the host and the payload, port, alias and client contract stay exactly
where they are.
Runtime: gufo v0.1.1 (b0f8673) at /home/dank/GitPackages/gufo-v0.1.1,
built by the new scripts/build-gufo.sh. It links the host ROCm 7.2.4
through its RPATH — the opposite of the unsloth prebuilt it replaces,
which carried its own — so two host workarounds are encoded in that
script: Ubuntu's librocwmma-dev installs a truncated rocWMMA header set
that shadows AMD's and cannot be overridden with -I in HIP mode, and the
ROCm clang prefers Ubuntu 26.04's GCC 16 headers over the GCC 15 whose
libstdc++ is actually installed.
Shape is unchanged: one session, full 262144 ctx, the same UD-Q8_K_XL
target and the same DFlash2 drafter, greedy, text-only. Speculation
moves from llama.cpp's fixed depth 4 to gufo's adaptive block policy
capped at 7 proposals.
Client-contract deltas, all documented: the chat template is compiled
into the binary (no --chat-template-file, so the Qwen-Sharp 73k-token
failure mode is gone), reasoning_effort now takes the full OpenAI set
(so models.yml's reasoningEffortMap fold is no longer required — kept
anyway, so this commit does not also move every high role from xhigh to
high effort), and there is no KV-quant or prompt-cache-RAM flag.
The test seam now runs the serve script against a stub engine and
asserts on the argv it actually execs, instead of grepping the script's
text. The deploy handler gates on the runtime binary and returns before
it stops the serving unit.
Measured trade, from gufo's own Q8_K_XL tables against llama.cpp
b11069: prefill +37% to +80%, cold load 4.5x faster, decode +7% at zero
depth — but -14% at 65,536 tokens of depth and -24% at 131,072. On the
8-bit target the crossover is ~32k, and frank-large runs one 262,144
conversation. The Q4_K_XL target shows no such regression.
Answers Frank's question: given the 2x R9700 box, which of DeepSeek-V4.1-Flash,
MiMo-V2.6-Flash-MOPD and GLM-5.3-Flash runs best, on what runtime, and can
Affinity / vllm-radiance / FreeToken-AMD be modified to run them fast.
Sizing, measured from the HF API rather than the model cards:
- DS4.1 payload 475.27 GiB, of which 189.13 GiB is Engram (two 94.56 GiB
shards, the layer 1 and 14 lookup tables). Lucebox never maps them, so
they do not evict the expert page cache.
- 240 routed experts x ~11 MB per token at the 37-of-384-per-layer residency
the hot stack can hold = ~2.4 GB across PCIe per token. At 55 GB/s real on
Gen5 x8 that is a ~23 tok/s ceiling, under the 35 tok/s goal before any
attention or dense work.
- The backend refuses layer-major prefill for V4.1, so the only legal
prefill is token-by-token; 1000 t/s is ~20x away.
Affinity (Codeberg StillDeadcode) builds clean here (hipcc 23, gfx1201, zero
errors) and claims 110 tok/s for the 284B V4-Flash, because the 101 GiB
container leaves the cold expert set in a pinned host pool in RAM. V4.1 is
2.5x too big to repeat that. Port scope, from both checkpoints' real
safetensors indices: expert machinery ports unchanged (identical tensor
naming, 15,360 tensors at 40x384), 26/32 per-layer tensors and 34/35 config
keys present. Five gaps - Engram, the disjoint {0,1,2} vs {0,4,128} ratio
vocabulary, CED, the DSpark draft geometry, vision. num_hash_layers is the
only key V4.1 lacks, so model.h:36's default of 3 silently survives and
would route layers 0-2 through a table that does not exist.
Also corrects: Dankserver RAM 60 -> 246 GiB (stale since the 2026-09-20 board
swap) and storage; the top-level index credited V4.1 with V4's geometry
(43 layers / 256 experts vs the actual 40 / 384).
New ACTIVE runbook (setup/doctor PASS=34/FAIL=0, UVA transport verified
in serving workers, wire-verified alias/reasoning/metrics), index +
vault entries flipped: llama.cpp b11030 endpoint RETIRED 2026-09-27 to
rollback target. Topology diagram updated.
download-frank-primary-fp8.sh + the fp8 manifest fetched the vLLM 0.30
payload that never deployed; the R9V cutover's payload is the R9V setup
flow's (packages + PLE, state-gated). Scripts move to old/ per policy.
Replace the vLLM 0.30 UVA-offload wiring (never deployed; llama.cpp
b11030 endpoint replaced by Frank's order, downtime window open) with
the R9V radiance-vLLM shape on the same root unit:
- serve-frank-primary.sh: layer the on-host qualified setup state
(104 tuned R9V_* keys, /home/dank/r9v-state/uncensored/setup.json)
under repo production overrides, then exec the fork's own launcher
through the docker->podman shim and block on docker wait so a crashed
engine is a nonzero ExecStart (launch.sh --detach alone would fake
green). Gates: pinned image by OCI manifest digest in RepoDigests,
PLE at pinned byte count (stat -c %s), all GGUF shards + mmproj.
- frank-primary.env: thin production overrides only (LAN bind 0.0.0.0,
:7770, alias frank-primary, container frank-primary-r9v, GPU BDFs);
tuned engine shape stays in the setup state by design.
- frank-primary.service: same root-unit shape (User=dank,
LimitMEMLOCK=infinity for the tiered-expert pin), plus ExecStop
podman stop — docker wait cannot stop the container child.
- deploy-remote.sh deploy_llm_primary: state-driven gates (state ready,
image digest present in rootless storage, PLE bytes, shard files)
replace the fp030 shard-index gate; false-green MainPID check kept.
- tests/frank_primary_unit.bats: seam rewritten for the R9V shape.
Unit restart SIGKILLs conmon before --rm cleanup runs, leaving the container
name registered: every restart then dies on name-in-use while the orphan
holds the port and GPUs (observed live 2026-09-27, NRestarts=6 with the
container Up and the unit auto-restarting). The serve script now clears its
own name before exec podman run; the deployer stops the unit and removes
the container BEFORE waiting on the port.
HOME inside the container pointed at the root-owned WORKDIR /app, so
vLLM's registry cache write hit PermissionError; HOME now points at the
cache volume.
The vLLM cutover itself is blocked on a structural wall (AMD Qwen4Exp impl
creates the 51.2B PLE table as a bf16 on-GPU param: 47.69 GiB transient on a
31.86 GiB card; the pinned-host UVA path that fits this hardware is
CUDA-gated). llama.cpp is restored live at :7770; host HEAD unchanged.
The llama.cpp b11030 trio becomes the vLLM 0.30 trio ahead of the
FP8 cutover on :7770 — same port, same alias, no client moves.
Serve shape: locally built ROCm gfx1201 image
(localhost/frank-primary:vllm030) execs via podman as dank from the
root unit (LimitMEMLOCK=infinity legalizes the pin). UVA offload:
--offload-backend uva pins the PLE family (47.75 GiB, 138 tensors:
131 n-gram table shards + 7 per-layer conv/proj) + routed experts
(114.86 GiB, 150,528 tensors) in host RAM — 162.60 GiB combined,
~81.3 GiB per rank under TP=2, hence --cpu-offload-gb 82 (80/card
strands 2.6 GiB of experts on the cards). Parsers per the host's
own Ornith-era precedent: qwen3_xml tools + qwen3 reasoning (vLLM
0.30 emits the channel as `reasoning` — reasoning_content is
renamed server-side) + enable_thinking/preserve_thinking template
kwargs. Spec decode stays env-wired and OFF at bring-up.
Deployer deploy_llm_primary rewritten: image gate, index-driven
shard-count gate (131), stale-container cleanup, port wait, and the
false-green hardening — health is only green when the unit's own
MainPID is live behind the answer (2026-09-26 lesson).
Seam tests rewritten to the vLLM contract (15 gates): offload
arithmetic, parser wiring, no-llama.cpp-ism, deployer gates. Suite
349/349 green.
scripts/manifests/qwen3.8-flash-next-fp8.manifest: 144 entries (131
safetensors shards + aux) with Hub LFS sha256s for
Qwen/Qwen3.8-Flash-Next-FP8. download-frank-primary-fp8.sh wraps
hf-fetch.sh: idempotent fetch, full sha256 re-verify of an on-disk
payload (verified live on dankserver: all 144 files match, ~10 min).
Part of the frank-primary vLLM cutover prep; the serve trio itself
lands with the image deploy.
The stock SearXNG image ships a native kagi engine; enabling it needs an
API key that must never enter git, and SearXNG does not expand env vars
in settings.yml (the live secret_key literally was '${SEARXNG_SECRET}'
until now — the app tolerated the placeholder string silently).
- settings.yml: kagi engine enabled (weight 2, kg shortcut, paging off so
exactly one API credit per aggregated query, timeout 8.0 vs 5.0 scrapers
so a slow paid query is not a wasted credit); api_key stays ${KAGI_API_KEY}.
- new scripts/searxng-render-settings.py: deploy-time renderer, allowlisted
to KAGI_API_KEY/SEARXNG_SECRET/SEARXNG_BASE_URL, fails the deploy loudly
on an unresolvable placeholder, idempotent on redeploys.
- deploy-remote.sh copy_searxng_config runs the renderer against
/opt/searxng/.env after copying settings.yml into /opt/searxng/config.
- KAGI_API_KEY placeholder added to dankclaw/.env.example (real key staged
into the host-side /opt/searxng/.env, mode 600, out of band).
- bats: engine wiring, renderer substitution, loud-failure, deployer seam.
- docs: SearXNG.md Kagi section + renderer law; Providers.md carve-out on
the no-credentials policy (paid engine acceptable when bounded + ordered).
Verified live on dankclaw: direct API probe via the gluetun netns (200,
data.search[] shape), then an aggregated query returned 29/29 results
tagged kagi, zero unresponsive engines.
The first no-redirect HEAD fix read X-Linked-Size on every 3xx, but
non-LFS files on a Xet-backed repo answer 307 to HF's own
/api/resolve-cache WITHOUT any X-Linked-Size header. The fallback to the
stub's Content-Length recorded the redirect page size (254-264 bytes)
instead of the file size, so hf-fetch.sh failed the size check on every
aux file it verified against the regenerated manifest (README.md
"65400 != 254", config.json "72514 != 258", 7 files on the FP8 repo).
Distinguish the two redirect shapes: 3xx WITH X-Linked-Size = LFS/Xet,
whose X-Linked-* headers are the truth and whose redirect must NOT be
followed (the Xet bridge ETag is the CAS hash, not the sha256); 3xx
WITHOUT = non-LFS, where the Location (followed once, via urljoin)
answers 200 with the real Content-Length and a git-blob-sha ETag that
the 64-hex gate correctly rejects (size-only check for those files).
Verified against Qwen/Qwen3.8-Flash-Next-FP8: all 7 previously-failing
aux files now record their on-disk byte counts exactly (README.md 65400,
config.json 72514, merges.txt 3353259, ...), shard shas unchanged
(model-00002 6841fe21...), total 172.82 GiB across 144 files.
On Xet-backed repos (Qwen/Qwen3.8-Flash-Next-FP8), the resolve endpoint 302s
to a CDN bridge whose final ETag is the Xet CAS chunk-dag hash, NOT the
content sha256. urllib follows redirects, so the manifest recorded the Xet
hash and every downloaded shard failed sha256 verification against a hash no
download could ever match (8/8 shards FAIL, all sizes correct, on-disk bytes
matching the Tree API's lfs.oid). Classic LFS CDNs serve ETag=sha256, which
is why this was invisible on prior fetches.
Fix: HEAD via http.client, which does not follow redirects. 200 = direct
answer (Content-Length + ETag, non-LFS); 302 = LFS/Xet redirect
(X-Linked-Size + X-Linked-ETag = content sha256). Verified against the FP8
repo: shard model-00002-of-00131 now records 6841fe21..., the sha256 of the
bytes already on disk (cross-checked against the Tree API lfs.oid).
Instrument the live server to answer "can -ot target the most active
layers": AMD IBS op sampling on llama-server during 4 mixed concurrent
generations, sampled load addresses mapped through /proc/<pid>/maps to
backing tensors with extents derived from consecutive GGUF header
offsets (no quant type-size tables - a wrong per-element size silently
mis-buckets tensor tails, first run lost 59% of expert samples to a
junk bucket).
Measured (75 s window, 2.7M expert-tensor samples): per-block read
volume is FLAT, 0.93x-1.08x of mean across all 40 CPU-resident blocks.
MoE top-k routing fires every block every token, so "most active
layers" do not exist at -ot granularity - any 8 GPU-resident blocks
are equivalent, the lever is COUNT, already at the 5/3 VRAM ceiling.
Real skew is per-EXPERT (top 10% of 48x512 cells carry 40.5% of
samples), below tensor-override granularity. No config change.
scripts/frank-primary-expert-activity.py: dump + sample + analyze in
one entry point; --analyze-only re-analyzes saved samples. Instruments
production serving in place; not a benchmark.
Blocks 0-4 on ROCm0 + 45-47 on ROCm1. This exact shape was probed and
battery-verified earlier the same session: card0 2.54 GiB free at boot,
2.11 GiB after the vision graph (steady under load), card1 unchanged
at ~3.3 GiB. Card1 cannot take another block (0.82 GiB would OOM the
vision graph + boot drift), so 5/3 is the physical limit at the
4x262144 / V-q8_0 shape. OT assertion pins the new value.
- CONTEXT_SIZE=1048576 / PARALLEL_SLOTS=4: four conversations, each the
full native 262144 window (per-slot window unchanged; slots divide
the pool, they never multiply it).
- CACHE_TYPE_V=q8_0 / CACHE_TYPE_K=f16: main KV drops 24,576 -> 18,816
B/token (-23.4%); the shared MTP drafter's KV ignores the V type
(measured: 2,048 MiB draft pool = the f16 law at 1,048,576 cells).
- OT_OVERRIDES: blocks 0-3 on ROCm0 and 45-47 on ROCm1 stay
GPU-resident (2.42/2.49 GiB per block, measured); the rest stay
CPU-computed. Measured free under load: ~4.9 GB card0 / ~3.4 GB
card1; a 5th block on card0 lands at 2.27 GB, under the 3 GB
checkpoint floor, so blocks are atomic and 4 is the safe fill.
- serve script: --cache-type-k/v env-wired tunables; ONE joined
--override-tensor flag (b11030 deprecates repeated flags, and the
loader is first-match-wins, so the specific patterns must precede
the broad exps=CPU sweep).
- seams: the window test asserts per-slot = 262144 at 4 slots; new
KV-type test; OT exact-value assertion pins the ordering law.
29,696 B/token ctx-scaled (12 of 48 layers carry KV - full_attention_interval=4;
indexer and draft KV included); weights/RS/compute are ctx-independent.
262k = 18.75 GiB total / 9.37 per card; 100k = 14.3 / 7.1. Parallel
slots split the pool rather than multiplying it.
Both b11030 serve scripts never passed --metrics, so /metrics answered
501 and Prometheus could not scrape frank-large (silently down since
its deploy) or frank-primary. frank-primary also gets -lv 4 (VERBOSITY
env tunable, default 4) for the full per-buffer load breakdown.
Battery 6/6: alias, reasoning_content, tool_calls, vision (probe image
described exactly), reasoning_effort high -> 200. Decode 11.8 t/s at
depth-0 prose (draft 109/accepted 72), 0.864 mean len 2.73 on tool
traffic. VRAM 29%/36% (output-head card carries the drafter). Also
records the ROCm1 drafter law in the runbook body (borrowed output.weight
+ n_copies==1 abort) and the two cutover traps: foreign-process health
false green, and llama-server SIGTERM cleanup hang.
The shared MTP head borrows the main model output head, which a -ts 1,1
layer split places on ROCm1. With tensor overrides set, n_copies==1 in the
draft's scheduler, and a borrowed output.weight in a ROCm1 buffer aborts the
draft graph (ggml-backend.cpp:941) when the draft context is on ROCm0 —
the unit crash-looped 4x at ~12 s per attempt during the 2026-09-26 cutover.
Probed live on the host: ROCm0 aborts identically, ROCm1 loads and serves.
Discovery, not guesswork: dump the GGUF v3 headers of the fetched parts
(scripts/dump-gguf.py, added — a dependency-free reader that never
touches tensor data, so a 50 GiB part scans in milliseconds) before
the first load.
What the dump says (parts 2-3, 2026-09-26):
- The 51B-param PLE n-gram table is ONE tensor: per_layer_token_embd.
weight, dims [160, 320001536], Q8_0, 54.4 GiB — exactly the whole of
part 3. It is the same tensor the shared MTP head borrows, so the
=CPU override is required for placement AND for the draft load
(GPU-resident borrow: "invalid vector subscript").
- The model is qwen4exp, 512 experts, top-10 routing
(qwen4exp.ple.ngram_size=3, heads_per_ngram=8).
- Part 1 is metadata-only (ntensors=0); parts 2-6 carry tensors.
The placeholder pattern ngram_embeddings matched nothing — dropped from
OT_OVERRIDES (env, serve-script default, seam, AGENTS.md, runbook,
LLM index) before it could masquerade as a working override at load
time. Deployed set: exps=CPU,per_layer_token_embd.weight=CPU.
Suite: 346 green.
The 2026-09-26 same-day cutover that ended the Lucebox DS4.1 detour:
Dankserver's :7770 endpoint is now unsloth llama.cpp (the b11030-mix
prebuilt, ROCm gfx120X, bundled runtime) serving
unsloth/Qwen3.8-Flash-Next-GGUF at Q8_0 — 175.30 GiB across six sha256-
verified split parts — on both Radeon AI PRO R9700s, as a ROOT unit (the
frank-large shape: no container, no generated unit).
The deployed contract:
- Placement law: layer split -ts 1,1 with -ngl 999; the routed experts
and the 51B-param n-gram (PLE) table are COMPUTED in host RAM via
-ot exps=CPU,ngram_embeddings=CPU (the x4-pair measurement: streaming
over PCIe loses at 3.7 t/s, computing in RAM does not care).
per_layer_token_embd.weight=CPU is required by the shared MTP head's
borrow path (a GPU-resident borrow crashes the draft load).
mmap stays on; --cache-ram 16384.
- Spec chain draft-mtp,ngram-mod: the shared Q8_0 MTP head
(mtp-...-shared-Q8_0.gguf, 2,786,568,256 B) at depth 2, with the
draftless ngram-mod matcher chained behind it — the only legal way to
layer two spec implementations in one process.
- Vision explicit: --mmproj names the BF16 projector (this build never
autoloads; a server without it comes up healthy and text-only).
- Reasoning: --reasoning-format deepseek → reasoning_content; the
Qwen3.8 template's {xhigh, medium, low} reasoning_effort gate folded
by reasoningEffortMap in models.yml.
- One stream, the model's full 262,144 context; greedy server default
with Qwen-recommended sampling riding compat.extraBody.
The deployer's deploy_llm_primary is rewritten for the root unit: it
gates every artifact at its declared byte count (stat -c %s, never wc -c
— which read the whole 377 GiB lucebox file for minutes on 2026-09-26),
retires the lucebox user quadlet to dated home trash, waits out port
7770, relinks/enables/restarts the root unit and waits up to 1800 s for
/health. The quadlet-era tree under dankserver/frank-primary/ is
archived (dankserver/.AGENTTRASH/20260926-llamacpp-cutover/ on this
box; git history everywhere else) and its planner mapping replaced by
the scripts/ trio.
Observability flips back to the llamacpp namespace: a frank-primary
Prometheus job at 192.168.0.235:7770 (cross-host LAN — the netavark
published-port quirk that forces host.containers.internal for the two
Dankclaw endpoints is same-host only), the LlmEndpointDown runbook
text, and the dashboard description for three llama.cpp endpoints.
Docs: the new runbook (frank-primary-llamacpp-qwen38-flash-next-q8.md),
RETIRED banners on the lucebox and radiance runbooks and their index
entries, the AGENTS.md fleet section rewritten for the root-unit shape,
and the homepage frank-medium card renamed to frank-primary with the
Q8_0 Flash-Next spec (docs.html #models and the modality note updated:
images on frank-primary and frank-small, audio on frank-small only).
models.yml: the dankserver provider is "llamacpp (dankserver)" serving
frank-primary, contextWindow 262144, maxTokens 65536 (compaction-gap
law: 183,501 + 65,536 <= 262,144), input [text, image].
Suite: 346 green (frank_primary_unit.bats rewritten for the root unit —
16 tests pinning the placement law, the shared-head requirement, the
explicit projector, HIP env hygiene (BIND_HOST, never bare HOST), and
the deployer's byte-count gates; the quadlet-era planner and
changed-tests mappings rewritten for the trio; the observability scrape
test asserts three llamacpp jobs again).
Rollback: the lucebox image (localhost/lucebox-rocm:gfx1201) and the
378 GiB payload stay on the host; the Qwen payload stays fetched, so a
re-swap in either direction is a redeploy, not a refetch.
Fetches into /home/dank/models/Qwen3.8-Flash-Next-GGUF/ keeping the Hub
layout: the six Q8_0 split parts (175.30 GiB — never concatenated, part
1's header advertises file_count=6), the shared Q8_0 MTP drafter head
(2.6 GiB — "shared" borrows the target's embeddings; unsloth's MTP guide
names it the recommended head), and the BF16 vision projector. Total
191,919,144,448 bytes.
Sizes and sha256s captured from the resolve endpoint's x-linked-etag/
x-linked-size on 2026-09-26; every artifact is checked for exact byte
count AND sha256, curl -f so an auth error fails instead of writing the
error body, resumable via -C -. The repo is public: no token machinery.
The frank-medium retirement tail the 2026-09-26 lucebox cutover left
uncommitted: the radiance quadlet tree, its swap/fetch tooling and its
seam test are deleted (archive copies live in dankserver/.AGENTTRASH/
20260926-frank-medium-radiance/ and scripts/.AGENTTRASH/20260926-
frank-medium-radiance/), the planner/allowlist/test-changed mappings
drop the service, and frank-large's chat template moves to scripts/
(its old home was the retired frank-medium dir).
Finished here:
- tests/frank_primary_unit.bats: the lucebox quadlet seam (boot
persistence, host-network bind, HIP_VISIBLE_DEVICES=0,1, HIP-reserved
env-name guard, checkout-mounted entrypoint/healthcheck, first-start
patience, deployer payload/port gates).
- test-changed.sh: explicit .AGENTTRASH/ case — retirement commits move
files to trash, and the wrapper refused those additions with "no
focused-test mapping".
- observability: the vllm: namespace left with the radiance runtime.
Rules keep only llamacpp:* series (prefix-cache reuse, spec-decode,
queueing, endpoint-down); the dashboard drops the vLLM KV/latency
panels; prometheus.yml documents that frank-primary (lucebox) has no
/metrics and therefore no scrape job. llm-fleet.json was left
corrupted mid-edit (missing opening brace) and is rewritten from HEAD.
Suite: 339 green (was 7 red).
The runtime's auto budget is gpu_free - kv - warm - safety (22.35 GiB), which
leaves only the 0.50 GiB safety margin free while the fit check demands the
1.50 GiB discrete headroom, so auto-sizing refuses on any 32 GiB target:
"device 0 memory: 30.97 of 31.86 GiB used, 0.89 GiB free (headroom 1.50
GiB): does not fit". 18000 MB is the value DS41.md measured its R9700 rows
at: ~1500 experts resident, ~5 GiB free on device 0.
--ds4-prefill dense made requires_monolithic_model() true, and a monolithic
load asks ggml for the entire 187 GiB expert set as one device-0 buffer:
"allocating 191932.25 MiB on device 0: cudaMalloc failed: out of memory",
followed by "monolithic HIP load required for dense prefill". Approximate
prefill (dense, sparse) is expressible only where the experts are resident
(in-process owners). exact feeds the prompt token by token, which is the
path the hybrid tier streams experts for. Chunk follows DS41.md's
cold-expert recipe (1024).
wc -c falls back to reading the whole stream when its fstat shortcut does
not apply, and this gate runs over a 377 GiB GGUF: the 2026-09-26 cutover
deploy sat in wc for minutes (30 s of CPU, NVMe-bound) before it could
reach the cutover at all. stat -c %s is metadata-only and equally exact.
The llama.cpp hunk (ggml_cuda_node_launches_nothing) was needed on main's
tree; the branch restructured that file and compiles clean, so only the
apt-list and cmake-3.31 hunks remain. Regenerated from the host clone.
The deepseek41 architecture exists only on Luce-Org/lucebox's
feat/ds41-engine branch; main rejects the checkpoint with
"unsupported model architecture 'deepseek41'". Re-aim the image build at
that branch and fix the launch shape, which had been copied from the V4
DS4.md recipe:
- drop --ds4-fused-decode and --ds4-fused-verify-f16-kv, and use
--ds4-prefill dense: V4.1's validate_model_features refuses the V4-only
prefill/fused paths (layer split and --mmproj too).
- --chunk 4096 (the V4.1 dense-prefill measurement point), --max-ctx
131072 (the V4.1 default window), --prefix/--prefill-cache-slots 0 as
the DS41.md bring-up recipe.
- drop --cache-type-k/v: the DeepSeek4 backend ignores them; the MLA
compressed caches are F16 by design.
- expose BOTH R9700s and set LUCE_EXPERT_STREAM_DEVICE=1, so hip:0 owns
the dense weights plus the hot expert stack while hip:1 holds the
streamed-expert slot cache instead of competing with it.
- quadlet: 384 routed experts per layer, Engram tables, 40 layers.
Replaces frank-medium (vllm-radiance TP=2) on :7770. The 377.2 GiB
43-layer MoE (256 routed experts, top-6) is mmapped: dense work and the
MLA KV cache on the R9700, routed experts computed on CPU from the
page cache (246 GiB RAM). The unit bypasses the image entrypoint
(Qwen/DFlash-only flag set) and execs luce_server with the DS4 launch
shape; --ds4-fused-decode's monolithic-load fallback IS the serving
mode here. deploy_llm_primary gates on image + model byte count,
stops/masks the radiance unit, waits for :7770, installs and starts.
Runbook: frank-primary-lucebox-ds4.md.
With a missing file, -C - sends Range: bytes=0-, which HF's xethub
CDN rejects with 400. Xet-backed repos (e.g. Lucebox/DeepSeek-V4.1-
Flash-ROCMFP23-GGUF) fail on the first fetch; the old LFS backend
tolerated the zero-offset range.
Dim the spec line (--dim) so the card reads pale role -> dim spec -> bright
spring-green inputs -> dim use. The inputs line now pops as the bright middle
tier and the spec/use are de-emphasized as reference detail.
Add --input: #00ff9d to the palette and point .desc-inputs at it, so the
Inputs line is visually distinct from the bright-green spec (--fg), the
pale-green role (--head), and the dim-green use (--dim).
Each LLM card now lists its accepted inputs as a line (Inputs: text · image ·
audio), sourced from the harness models.yml:
- frank-large: text (no vision projector)
- frank-medium: text, image (Gemma4 vision path)
- frank-small: text, image, audio (Gemma4 projector audio encoder; audio is
not in models.yml's input list only because OMP's schema rejects it, but the
endpoint accepts input_audio parts)
New .desc-inputs CSS + bare-mode reset; /docs model guide gains an Inputs line
per model. bats 30/30 + live render verified.
Each LLM card now carries a details object (role/spec/use) rendered as a
bold pale-green role, a bright-green spec line, and a dim 'Use:' line.
app.js descHTML() falls back to the plain description for non-LLM cards;
new .desc-role/.desc-spec/.desc-use CSS + bare-mode reset.
Roles made neutral (no nicknames): frank-large = deep reasoning,
frank-medium = primary model, frank-small = fast utility model.
The /docs model guide was fixed — it had drifted to retired model info
(frank-large as Qwen3.8-Flash-Next 87.3 GiB/200k, frank-medium as FP8
~31 GiB) — now Qwen3.8-27B UD-Q8_K_XL 31.5 GiB/262k and MXFP4/W4A8
19.4 GiB, reordered large->medium->small. bats 30/30 + live render.
frank-large: cybersecurity + debugging (requires Frank's permission)
frank-medium: primary intelligent model (surpasses Claude Sonnet 5 per
Artificial Analysis) — software engineering, sysadmin, instruction tasks
frank-small: fast/cheap utility model — agentic search, RAG, research
subagents, translation, roleplay, summarization, small utility tasks
Updated both the status cards (services.json) and the /docs model guide.
The auth works via the modern /Users/AuthenticateByName endpoint (what the
web UI uses). The 401 was a test artifact from probing the dead legacy route.
No config change needed; LDAP-Auth plugin + authentik 2026.8.3 outpost healthy.
The UserWriteStage writes prompt_data['password'] via user.set_password().
A PasswordStage only authenticates the current password. Document the
correct mechanism (password field on the default-user-settings prompt +
empty-password guard policy) and the throwaway-user verification.
The default-user-settings-flow was missing a PasswordStage, so users could
edit name/email/locale but not their password. Added a PasswordStage
(InbuiltBackend) bound at order 101. Documented the flow's stage table and
the two ORM gotchas (MTI through model, required backends array).
The local username/password path is retired (ENABLE_LOGIN_FORM +
ENABLE_PASSWORD_AUTH false, ui.enable_login_form DB override false) and
qa-e2e-admin is out of the authentik Admins group. Documented in the SSO
section and History.
The authentik OIDC round-trip is verified, so retire the local
username/password path. ENABLE_LOGIN_FORM hides the form and
ENABLE_PASSWORD_AUTH refuses password logins; the only way in is now
the OIDC flow, which folds into the local account by email. The
ui.enable_login_form config-DB override is flipped to false in the
same change (the DB wins over env, so both must agree).
Footer link opens the authentik default-user-settings-flow in a new tab,
giving users a self-service entry point to change their password and
manage MFA/credentials without admin intervention.
systemd splits unquoted Environment= values on whitespace, so the
quadlet quotes OAUTH_SCOPES to keep all four scopes; update the bats
assertion to expect the quoted form (it was checking the old
unquoted line and failing).
Records the LDAP-Auth plugin wiring: the Jellyfin LDAP provider +
outpost on Dankclaw, the svc-jellyfin-ldap service account, the
jellyfin_users/jellyfin_admins access groups, and the key gotcha that
the bind credential is a PASSWORD (set via Recovery), not an API/app
token — the default auth flow's standard-password backend rejects
tokens with 'flow error password: Invalid'. Verified end-to-end:
alex (in jellyfin_users) logs in with their authentik password and the
plugin creates the matching Jellyfin user.
systemd splits unquoted Environment= values on whitespace, so
'OAUTH_SCOPES=openid email profile roles' arrived as 'openid' and the
authorize request carried a single scope — the id_token then lacked the
email claim and the callback failed. Quoted, all four scopes flow.
Also: assign the default openid/email/profile ScopeMappings to the
provider (2026.x no longer auto-creates them; without them the token
grant is the intersection with the provider's mappings = roles only,
and userinfo 403s).
fill-verify-Enter with a single inputValue read per stage (identification
and password); the toHaveValue assertion hard-failed every attempt when the
re-render wipe landed inside its 5s window (00-auth.setup failure 2026-09-24)
Boot 2026-09-22 at 0.98 fill: KV pool auto-sized to 425,047 tokens (1.62x
the 262,144 window) with 14.42 GiB per card available for KV. The DFlash2
drafter on the Swift finetune measures mean acceptance length 7.00 / 85.7%
(easy) and 5.08 / 58.3% (varied) — it survives the base-model change.
Replaces the estimated ~420k / ~17.6 GiB figures with the measured values.
0.99 failed the vLLM startup memory check before weight load: the R9700
reports 31.86 GiB total but only 31.23 GiB free at device-init (the ROCm/HIP
context + NCCL + radiance custom all-reduce consume the 0.63 GiB gap), so
0.99 (31.54 GiB) exceeded free and the container crash-looped. 0.98 (31.22
GiB) is the ceiling and the prior production value.
Also fixed the runbook KV math that had shipped with the 0.99 change: the
~730k pool estimate was impossible (MXFP6's 25.16 GiB weights > MXFP4's
19.37 GiB, so the pool is smaller, not larger than MXFP4's 486k) and the
34.2 GiB/card figure was not the R9700 total. Recomputed ~420k tokens at 0.98
fill.
The Qwen-Image 2.1 VAE is a video VAE (5D convs, time_conv, depth-3).
ComfyUI detects it as WanVAE (latent_dim=3), but EmptyLatentImage produces
a 4D latent. The decode function's memory_used_decode lambda accesses
shape[4] which is out of range for 4D.
Fix: expand 4D latents to 5D in the decode function when latent_dim==3.
Added to the patch script for persistence across container recreations.
The serve script now passes --chat-template-file pointing at the
Qwen-Sharp v22.4.0 template. Without it the binary falls back to the
built-in peg-native template, which produces a ~73,600-token prompt for
a ~250-token request. With the sharp template wired in, the same request
produces a 62-175-token prompt.
The serve script only passed --chat-template-kwargs, so the binary fell
back to the built-in peg-native template. A 250-token request produced a
73,600-token prompt (280x the expected ~260). Wiring in --chat-template-file
drops the prompt to 62 tokens. Draft acceptance 0.79 (mean 4.14), model
answers correctly, reasoning lands in reasoning_content.
The qwen_image21 detection check and GGUF node arch list patch are
applied by a startup wrapper (comfyui-start.sh) that runs before
ComfyUI starts. The patch script and wrapper are baked into the image
so they survive container recreations. The quadlet's Exec now points
at the wrapper instead of main.py directly.
The hermesadmin.franksdank.cloud dashboard (9119 — config, API keys,
sessions) is a distinct admin surface from the web UI client. Add it as a
third AI-section card; probe 9119 (302 login redirect counts as up, verified
on Dankclaw).
The admin Infrastructure section carried the retired 'dnsmasq' (probe-only,
no link) — Pi-hole FTL (a dnsmasq fork) replaced it on 2026-08-24. Replace it
with a linked Pi-hole card (pihole.franksdank.cloud, probe 8083 — the 403
basicauth counts as up). The Hermes card was probe-only; add the web UI
client link (hermes.franksdank.cloud). Both probes verified up on Dankclaw.
Knowledge-base follow-up to a45f396. The runbook, both index one-liners, and
the AGENTS.md topology row all still declared FP8 active; flipped to
just1moremodel/Qwen3.8-27B-Uncensored-MXFP4-awq with unquantized bf16 KV (pool
486,464 tokens, full 262,144 window), and recorded the causal nuance: fp8 KV
was on both arms, so the FP8-vs-MXFP4 gap was weight precision, not KV - clean
KV is a quality hypothesis for W4A8, not a throughput one. No verdict yet.
The quadlet's "TEXT ONLY ... 400 here" comment was wrong: vision works on the
MXFP4 checkpoint (Qwen3_5ForConditionalGeneration carries the vision tower
through the Gemma4 merge path, unquantized) - verified by naming a solid red
48x48 PNG "Red". Comment corrected; harness models.yml input stays [text,image].
Docs-only; no re-deploy (the endpoint already serves vision).
Swap the serving checkpoint back to
just1moremodel/Qwen3.8-27B-Uncensored-MXFP4-awq (quark/W4A8) and serve it with
KV_DTYPE=auto (unquantized bf16 KV) instead of the hardcoded fp8. fp8 KV
stacked on MXFP4's already-quantized W4A8 weights compounds attention-path
precision loss; clean KV tests whether that was hurting output quality. The
freed MXFP4 weights (19.37 vs 30.9 GB) buy the KV pool back, so the full
262,144 native window fits unquantized - MAXLEN returns to 262144.
The entrypoint gains a KV_DTYPE tunable (default fp8, the historical behavior),
shared across both quantization paths; the quadlet sets auto. Rollback: SNAP
back to the FP8 checkpoint with KV_DTYPE=fp8 and MAXLEN=192000.
The LLM backends section said two ROCmFPX endpoints; the fleet now serves
three (frank-large/medium/small) with different runtimes. Rewrite it, fix the
gotcha that claimed host.containers.internal does not resolve on a bridge
(it does — verified end-to-end for both on-host endpoints), and add a History
entry for the 2026-09-21 model-picker rebuild.
The per-key config DB already pointed web search at SearXNG (engine=searxng,
query URL http://192.168.0.143:8081/search), but only inside the data volume.
The quadlet now carries the same values as seeding env vars, so a fresh data
dir comes up wired, and the doc records the access path: the internal
host-published port, not the search. vhost — the Anubis->Authentik guard
chain is browser-only, and the bridge egress sits in SearXNG's pass_ip, so
the 4-req/IP/hour JSON cap never applies.
The user page now orders Web Apps, Media Library Apps, AI — the models sit
at the bottom, below the apps people click. The AI card and /docs#models
descriptions drop the infrastructure (runtimes, GPUs, tensor parallelism,
draft heads) in favor of what users care about: the hosted model, its
size, the context/parallel capacity, and what it's good at.
The build default is 8192 MiB. dankclaw is memory-tight (frank-large's
70 GiB GTT plus the rest of the stack) and Gemma4 runs fully on the 3090
(-ngl 999), so the CPU-side cache is not the hot path. Cap it at 2048 to
give back ~6 GiB of LPDDR. Gemma4 is not a DeltaNet model, so the
>12.8 GiB CACHE_RAM floor does not apply.
Wired through frank-small.env (CACHE_RAM) -> serve-frank-small.sh
(--cache-ram), with a wiring seam so the cap cannot be silently unwired.
Frank
