5563b778674ab337edf12a87380c77d116474830
100
Commits
| Author | SHA1 | Message | Date | |
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 vh
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5563b77867 |
fix(pfi-gx10): install python3-dev — Triton JIT-compiles C at first use
Triton builds its CUDA-utils shim with gcc the first time a kernel runs, and needs Python.h to do it. Without python3-dev the box looks entirely healthy: torch imports, the 49 GB base loads, LoRA attaches with the right parameter count, and then the first training step dies with a bare CalledProcessError naming a gcc invocation and an exit code. The real message -- "fatal error: Python.h: No such file or directory" -- is discarded, because Triton sends the compiler's stdout to DEVNULL. It cost a probe run and a model load to find something a one-line manual compile answered immediately. Same shape as the dots-tts container needing a C compiler at runtime: a JIT dependency invisible at install time that only surfaces under load. The verify step runs the compile rather than checking the package is present, because `dpkg -l python3-dev` would pass while the compile still failed on a missing library path or header directory. |
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f870dbcbbb |
memory: snapshot — run-3c probe in flight; ana-ml2 baseline reduced from its log
The comparison number nobody had written down: ana-ml2's real run-3c step times, pulled out of run-03c.log before the breaker killed it. About 10.8 to 15.8 s/it over the first 24 steps, so 604 steps lands at roughly 2.2 to 2.7 hours. Anything under about 45 s/it on the GX10 makes it an overnight run. Also records the exact geometry from run-03c.json and a real adapter_config.json, so the probe measures the shape that actually ran rather than an approximation of it. Checked the backend-delta trap the playbook warns about before running anything rather than after: flash-attn is installed on neither box, so both fall back to sdpa. Library versions do differ -- torch 2.13.0 versus 2.14.0, transformers 5.15.1 versus 5.16.1 -- and that is recorded rather than assumed harmless. The probe reads the resolved attention implementation back off the loaded model instead of trusting the request. The probe discards its first two steps as warmup, which is not optional on this box: an unwarmed benchmark here already read 27 TFLOP/s when the true figure was 93, because it was timing the PTX JIT. |
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vh
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c0e352a47b |
fix(elway): probe NOPASSWD with sudo -n true, never sudo -n -v
`sudo -v` refreshes the auth timestamp, and a NOPASSWD-only rule creates no timestamp to refresh, so on sudo >= 1.9.15 `sudo -n -v` returns non-zero while every real command runs passwordless. Measured: pfi-gx10 sudo 1.9.15p5 sudo -n -v rc=1 sudo -n true rc=0 nh3-docker sudo 1.9.13p3 sudo -n -v rc=0 sudo -n true rc=0 ana-docker sudo 1.9.13p3 sudo -n -v rc=0 sudo -n true rc=0 irv-ml1 sudo 1.9.13p3 sudo -n -v rc=0 sudo -n true rc=0 Only pfi-gx10 is new enough to hit it today, but every host does as it moves past 1.9.13, and the failure mode is bad: elway prompts for a password on a host with working NOPASSWD sudo, which in a non-interactive run is an EOFError partway through a playbook. The same probe in my own notes cost this session directly. gx10 looked like a fleet exception with no NOPASSWD sudo when it had it from account creation, and the operator was asked for a password that was never needed. Corrected in auto-memory too. Also lands the gx10 privileged outfit playbook, now green at 5/5: NOPASSWD sudo, nvcc, docker group, a CUDA container seeing the GB10, and the userspace torch stack still working afterward. |
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vh
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0b48517909 |
feat(pfi-gx10): privileged-half outfit playbook (sudo, CUDA toolkit, container toolkit)
The userspace half is done and needed no root. This covers what does: NOPASSWD sudo for infra-ops (ending the gx10 fleet exception), the docker group, the CUDA toolkit for nvcc, and the NVIDIA Container Toolkit wired into dockerd. Unrun -- it needs one interactive invocation to supply the sudo password, because no gx10 credential is vaulted and elway prompts via getpass. After its first step lands, the box stops being an exception and later runs are unattended. Guards worth noting: the sudoers drop-in is validated with visudo -cf before install, since a malformed one locks every sudo user out of a box sitting on a desk with no out-of-band access; and cuda-toolkit is installed rather than the cuda metapackage, so the working 580.173.02 driver is not replaced. |
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fad1db96a0 |
memory: snapshot — GX10 outfitted userspace; bare metal ruled; CUDA works on sm_121
Ruling on the architecture question: bare metal, not Proxmox. Proxmox VE has no aarch64 build, and more fundamentally the GB10's GPU sits on an on-package root complex cache-coherent with the CPU over NVLink-C2C, sharing the same LPDDR5X. Passing it to a guest would mean partitioning the unified memory that is the entire reason for the box. The fleet's other GPU hosts are bare metal for the same class of reason. Installed uv and a venv with torch 2.14.0+cu130 plus the full training stack, and every one of transformers, accelerate, peft, trl, datasets, safetensors, huggingface_hub and bitsandbytes imports clean on aarch64. The per-arch unknowns warning did not materialise for any of them. CUDA works: sm_121, 121.6 GiB addressable, about 93 TFLOP/s dense bf16 with tensor cores confirmed engaged by the bf16-to-fp32 ratio. That is A6000-class throughput with two and a half times the memory, so capacity rather than speed is what this box buys. Two warnings worth keeping. sm_121 is not in torch's compiled arch list, so everything runs by PTX JIT from sm_120: first use of every kernel pays a compile, and any library shipping cubins without PTX will fail outright. And an unwarmed benchmark read 27 TFLOP/s because it was timing that JIT, which nearly became a phantom report that tensor cores were broken -- the playbook's section 4 shape exactly, a run that completes and reports plausible numbers and is wrong. The privileged half is blocked: infra-ops has no NOPASSWD sudo on this box, unlike the rest of the fleet, and no credential is vaulted. That gates nvcc, the container toolkit and the docker group, but not the run-3c throughput probe. |
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18fde5902e |
memory: snapshot — GX10 liveness confirmed; racking is not a prerequisite
Probed the box read-only. Alive and idle at 11h48m uptime, 118 of 121 GB memory free, 822 GB disk free, and completely unchanged since onboarding: no torch, no nvcc, no uv, and infra-ops is not in the docker group. The useful finding is a negative one. I assumed the temporary Wi-Fi would gate getting a 49 GB base model onto the box and it does not. The link is Wi-Fi 7 on 6 GHz at 2401.9 Mbit/s with a -48 dBm signal, and a measured 300 MB transfer ran at 67 MB/s over SSH, which puts the full base at about twelve minutes. SSH's cipher is the limiter there, not the radio. So the throughput probe can run from the desk today and racking is worth doing for permanence rather than as a blocker. Also recorded that nvidia-smi reporting FB Memory and BAR1 as N/A is correct for GB10 rather than a driver fault, since the Grace Blackwell superchip shares unified LPDDR5X between CPU and GPU and has no discrete VRAM figure to report. |
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4c39ef08e2 |
memory: snapshot — never arm a waiter on a pane-routed seat
The /althing:monitor slash command is a separate artifact from the canonical skill and sync_skill.sh does not touch it. It sits at plugin version 0.0.1 with pre-3.2.0 text and zero mentions of pane routes, while the canonical skill it should mirror is at 3.2.2. That makes it active harm rather than stale documentation. The canonical precedence rule is that a live waiter wins over a pane entry, so an agent on a healthy pane route who follows the command verbatim demotes itself back onto the FIFO path the harness reaps -- the path that died twice on this seat tonight. The command cannot warn about it because it predates the problem, and every Claude Code seat reaches for the slash command first because it is the discoverable surface. Declined to arm on this seat and left the route intact. Reported to forseti with a recommendation that althing-listen refuse to arm when a pane route exists for the handle, since a binary enforcing the documented precedence beats a doc that relies on a reader noticing. Also cleared a stale wake-listener lock holding a dead pid, and recorded why it matters: the next althing-listen seeing it exits 3, whose documented response is do not drain and do not re-arm. A stale lock turns a reap into a permanent monitoring outage that reports itself as healthy. |
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f099caa238 |
memory: snapshot — H3 encoder pin resolved; my question had the direction backwards
Checked the disk rather than waiting on comfy-dev. Both builds are there, pulled a minute apart on Aug 23: a 26 GB int8 and a 15 GB nvfp4-awq. I had asked whether their nvfp4 pin was set under a Blackwell assumption, since Ada has no native nvfp4, which would make the int8 file the right one on the new box. It cannot be. They pinned it on irv-ml1's A6000, which is Ampere sm_86 and has neither native nvfp4 nor native fp8. Ada sm_89 supports a strict superset, so a pin that was correct on the weaker card cannot be invalidated by moving to the stronger one. The migration is incapable of breaking it. The pin is about VRAM, not architecture. Eleven gigabytes on a 48 GB card that also holds a DiT and two VAEs decides whether a graph runs, and a text encoder runs once per prompt rather than once per diffusion step, so its throughput matters far less than the DiT's. That also explains why this pin went the opposite way from their other one without either being inconsistent. The RTX 6000 Ada is also 48 GB, so nothing relaxes. Reclassified the 26 GB int8 from orphan to spare: with the extra drives the destination lands near 14% full, so disk stops being the constraint and the pin-rot argument says keep it. Question withdrawn to comfy-dev. |
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12006d287a |
memory: snapshot — althing 3.2.2 deployed; discover-pid was a bug, not an unknown
Four of the five items I raised are closed and tagged. The one that matters most: --discover-pid matched comm == "claude", which means kimi, grok, codex and pi would each have walked to the multiplexer and refused. Four of five pane seats could never have used it. It now matches the pane's own command, which zellij already reports and guard 1 already compares against, so discovery and the guard read one string. Recording a second miss of my own alongside it. I handed all four seats explicit measured pids because I suspected the ancestry walk was broken, then reported the suspicion as an open question rather than spending the same twenty seconds to settle it, with four live non-Claude seats in front of me. That pairs with the earlier miss in the opposite direction: asserting an open risk on something the author had already measured. Same root -- having the means to settle a question and reporting it as open instead. Also keeping forseti's two dead ends as negative results, because they are the obvious things to propose next and both fail: locating a TUI's input box in a screen dump needs per-TUI parsing, and diffing two dumps to detect typing refuses every poke forever, because status bars carry live token counts and clocks so consecutive dumps differ on an idle pane. The kimi/pi coverage gap is deliberately unfixed and now sits with the operator, with a recommendation to leave it per-seat. |
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f0d30f7a0f |
memory: snapshot — 3.2.1 outcome; the unguardable seats split, and one has a fix
All four seats answered. Delivery is now verified on four TUI families and forseti's measured idle columns held exactly, so nobody had to guess. The two seats guard 4 cannot cover made opposite calls on the same facts and both are right. regin-smithy-dev released to pull-only because the operator composes in that pane routinely, so the exposure is continuous. bil-smithy-dev kept its route because that pane is poke-driven, making the collision window narrow, and because pull-only had already cost them a notice that sat unread for days. The deciding variable is who composes in the pane and why, not risk appetite -- a flat rule either way would have been wrong for one of them. regin also supplied the only real path to closing the hole: pi and kimi report no cursor because their input line is not an empty-prompt-at-idle, so a moment-check reading pane content rather than cursor column would cover them. Column is a proxy; an empty input line is the actual predicate. Relayed to forseti as the lead item. And bil corrected something upstream: the Aug 28 probes proved delivery against non-Claude TUIs but never exercised the collision case, because nobody was typing during them. Two different questions, one body of evidence, only one of them answered by it -- which means the earlier retraction of the non-Claude risk flag was right about delivery and silent about stapling. Note for the operator: regin-smithy-dev is pull-only as of 22:42 and will not be poked until it declares again. |
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1c7bd40c9c |
memory: snapshot — althing 3.2.1 deployed; guard 4 cannot cover two of five seats
3.2.0 wrote its pokes into pane input lines and pressed Enter, so anyone mid-sentence had their half-written message submitted with the herald's line stapled on. It hit the operator within an hour of this evening's deploy. 3.2.1 adds a fourth guard that pins the pane's idle cursor column and stays silent when the live column has moved. Deployed all three steps at 22:40 and re-declared this seat, which pinned idle_cursor=3 as expected for a claude TUI. The finding worth keeping is the hole the fix leaves. forseti's own measurements say kimi and pi report no cursor at all, which means bil-smithy-dev and regin-smithy-dev can never acquire guard 4 no matter what they run. Two of the five pane seats on this box stay permanently exposed to the bug 3.2.1 fixes, so the sentence "3.2.1 fixes the write-into-a-typing-pane bug" is only true where the cursor is legible. All four seats were told, differentiated: a pre-filled re-declare and the expected column for the two that can be guarded, and the honest version plus the release-to-pull-only option for the two that cannot. Also keeping forseti's post-mortem line, because it generalises: three guards that all answer "is this the right pane" and none that asks whether it is a good moment are one check wearing three hats. |
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28dd516be1 |
memory: snapshot — 3.2.0 migration complete, and a miss of my own worth keeping
All four notified seats re-declared within about twelve minutes and the log went quiet. Five of five pane routes now carry the guard fields, and delivery is confirmed on a claude seat, a pi seat and a grok seat. Two findings survive the close-out. The herald's exclusion reason is false for the migration case -- every declaring process was alive and four days old with no pid wrap, and the real cause is simply that the route predates the fields the guard needs. And the uv trap stands as the thing most likely to bite the next person. The third is mine. I raised non-Claude pane delivery with forseti as an open risk on their release when forseti had personally measured it days earlier, on the exact seats in question, and the resulting matrix is what characterised the settle bug they fixed. The error was not caution, it was calling something open without checking whether it was already settled, with the peers who knew right in front of me. Recorded because "I don't know" and "this is an open risk" are different claims and I made the second when only the first was true. Still open with forseti: the uv --force runbook fix, an exclusion-message third branch, --discover-pid against a non-Claude process tree, the one-tick latency note, and status not being durable evidence. |
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2d2c88e43c |
memory: snapshot — the four revoked routes are non-Claude, and the herald's reason is wrong
Operator directed that the four affected seats be told directly. Measured their state before writing, which turned up two things worth more than the notification itself. The herald logs each exclusion as "the process that declared this route is gone, or its pid was reused by something that started at a different time." Neither is true for any of the four. Each declaring process started minutes before its route was written and is still running four days later, and pid_max is 4194304 against a current 2.86M so the counter has not wrapped. The real cause is a third one the message never offers: the route predates the guard fields, so identity cannot be verified. The behaviour is right and the explanation is wrong, and it would send anyone debugging it hunting a dead agent that is alive. Separately, all four seats run non-Claude CLIs -- kimi, grok, codex and pi -- while the pane poke is built around typing into a Claude Code pane. Whether --discover-pid walks a non-Claude process tree, and what a poke does to a non-Claude TUI, are both unverified. Each agent was given their measured pid to sidestep the first, told plainly about the second, and offered the choice between re-declaring as a test or staying pull-only. Both findings raised with forseti. |
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e014f756fb |
memory: snapshot — infra-ops moved to a pane route; waiters get reaped
The waiter died a second time, within minutes of being armed, so this seat stopped re-arming and declared a pane route instead. That is exactly what 3.2.0 shipped for. - althing-route declare --discover-pid walks the ancestry to the long-lived claude process rather than the ephemeral bash that invoked it. Passing --pid $$ would bind the route to a shell that dies with the tool call. - Recorded the sharper form of the failure: the waiter does not just die, it dies after confirming it is up. Both waiters reported push and reachable immediately after arming. So a green postbox status is not durable evidence of monitoring, and the reap is not confined to the pane seats that were migrated -- this seat is a fifo waiter and was hit twice. Both consequences raised with forseti on the deploy thread, along with a note that reachable is a report rather than a delivered poke. |
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71d97f36b6 |
memory: snapshot — althing 3.2.0 deployed; uv tool install is a silent no-op
forseti requested the deploy, operator-approved and tagged at c4ede0f. All three steps landed on nh3-dev and verified: force reinstall to 3.2.0 with seven binaries, herald restart, skill sync. The durable lesson is the trap in step 1. `uv tool install .` matches on the source spec rather than its contents, so on a box that already had the tool installed from that path it prints "already installed" and exits 0 having done nothing. Following the runbook literally would have left the new binary absent with every command reporting success. Always use --force when reinstalling from a local path. Also recorded: the pane-route migration revoked exactly four routes, verified by splitting on channel= before the restart rather than by auditing fields (all twelve route files lack the new fields, so a field audit over-counts). The four affected agents were deliberately not notified, per the standing rule against unsolicited fleet broadcast, and that is surfaced to the operator instead. And this closes an open question from earlier today: the waiter that died with status "killed" was CC 2.1.257 reaping detached tasks, which is the premise 3.2.0 exists to address. |
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46a3c63706 |
memory: snapshot — A6000 window closed; my dots-tts hypothesis was wrong
Operator freed ComfyUI's VRAM directly, so tts-dev is unblocked and the window request is withdrawn with comfy-dev. - Verified it was a model unload, not a stop: comfyui still up 8 days, same pid, HTTP 200, 18,500 -> 612 MiB. Told comfy-dev explicitly so a VRAM drop is not misread as a restart of their service. - The resulting 43.8 GB free is a snapshot, not a floor. ComfyUI is live and reloads ~18.5 GB on the next render, which puts the real floor at ~25.3 GB against FireRedAudio's ~26 GB requirement. The coordination shrank from "stop ComfyUI" to "don't render during the bench" rather than disappearing. Flagged to both; not volunteered on comfy-dev's behalf. - Withdrew my caching-allocator hypothesis for the dots-tts VRAM. tts-dev identified it as their prompt-feature cache, capped at 32 entries on 2026-08-14 after two production incidents. A named mechanism with an incident history beats a plausible story, and the useful finding is that 14.43 GB sits inside a cap they deliberately chose. Read-only probes; nothing on the box was changed. Memory-only; no version bump per the SemVer SKIP list. |
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e91278799d |
memory: snapshot — irv-ml1 GPU resident map; dots-tts at 2.4x its recorded VRAM
tts-dev asked for an A6000 window for an approved TTS bench and flagged a 3090 VRAM delta. Probed the box and mapped PID to container rather than taking the reported figures. - The 18.5 GB process they attributed to the 3090 is comfyui, on the A6000. And it is 18.5 GB rather than the ~11.8 GB they budgeted, so stopping it gives ~44.4 GB free, not the tight margin they expected. - Their "~4 GB unaccounted" on the 3090 is two things: parakeet is a third tenant the doc figure never counted, and dots-tts alone is holding 14,430 MiB against a burn-in figure of ~6 GB. The second is the larger finding and it is theirs to act on; handed over with a caching-allocator hypothesis and a one-restart discriminating test. - Restated the GPU ordering foot-gun: device_ids ["1"] is the A6000 in a container, but a bare native CUDA_VISIBLE_DEVICES=1 gets the 3090. Window not granted unilaterally — comfyui is comfy-dev's and they are mid-migration, so the request went to them directly and infra-ops relays. Ruled that the bench runs as a plain container under lkraven rather than under /opt/docker/compose/, which is for deployed stacks and would leave a canonical entry reporting as drift until deleted. Read-only probes; nothing on the box was changed. Memory-only; no version bump per the SemVer SKIP list. |
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2cf73fd556 |
memory: snapshot — 768 GB is not a population the R750xa takes
Operator asked why not move all 768 GB across. It does not fit the board's shape: the R640 has 24 slots at 6 channels/socket x 2 DPC, the R750xa has 16 at 8 channels/socket x 1 DPC. 768 GB is either 24x 32 GB (more DIMMs than slots) or 12x 64 GB (fits, but populates 6 of 8 channels per socket and gives up ~25% of memory bandwidth). The board wants 16 identical DIMMs. So the targets are 512 GB if they are 32s, or 1 TB if they are 64s — taking 12 from one spare and 4 from the other. In the 64 GB case the answer beats the question. Also recorded: beyond ~512 GB the return is marginal for this workload, so take 1 TB because it is free rather than because it is needed; 64 GB LRDIMMs run ~50 W hotter in a chassis whose high-performance fans are unaccounted for on the invoice; and DIMM slot count now needs to be on the iDRAC pull, since the 16-slot figure is inferred from the factory CSV and is load-bearing for a 512-vs-1024 decision. Memory-only; no version bump per the SemVer SKIP list. |
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32fd1dbe4a |
memory: snapshot — ARC sizing for the Ada box; no ramfs model tier
Operator asked whether 512 GB justifies a ramfs for hot models, then self-corrected toward ARC with a 128 GB cap. - No ramfs/tmpfs tier. ARC is the same cache done adaptively: no curated hot-list to rot, no boot-time copy-in, and the memory comes back under pressure. Neither tier beats the host-to-VRAM PCIe hop anyway, so the warm-load time is identical. - 128 GB is below the OpenZFS Linux default of 50% of RAM, so doing nothing already gives 256 GB. Recommend ~320 GB, not past ~75%. - Recorded the one honest argument for tmpfs: safetensors mmap gets double-buffered on ZFS-on-Linux, so a checkpoint can cost ~2x. Sized for, not architected around. - Flagged that this is not the idle-VRAM-is-reserved case: arc_max is a ceiling on an elastic cache, not a preallocation. - recordsize=1M cannot be won through zfs send, since recv reproduces the source's block structure. Not worth losing incremental send over; the 128K cost on flash is metadata overhead, not throughput. Memory-only; no version bump per the SemVer SKIP list. |
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3410d578e3 |
memory: snapshot — R640 RAM harvest may zero the RAM line; CPU diff was missing
Operator has 2x Dell R640 at 768 GB each and asked whether the memory is interchangeable with the R750xa. Both are DDR4 RDIMM platforms and Dell does not vendor-lock DIMMs, so the answer is very likely yes. - 16 slots in the R750xa x 32 GB = 512 GB, double the factory spec, and it deletes the 8x M04W6 purchase. - Gating question is RDIMM vs LRDIMM. 24x 32 GB 2Rx4 RDIMM is the safe and most likely case; 12x 64 GB LRDIMM needs Ice Lake support checked. - Cleanest harvest is to strip one R640 entirely and leave the other whole, rather than half-emptying both into unbalanced populations. Separately, reading the factory CSV to answer this surfaced a gap in the diff table: the reseller also swapped 2x Xeon Platinum 8362 (32C/64T, 265 W, DDR4-3200) for 2x Xeon Silver 4314 (16C/32T, 135 W, DDR4-2666). The Silvers were recorded under "As bought" but never diffed, so the swap went unremarked. Two consequences: the box cannot use the 3200 rating the buy list was paying for, which makes 2666 R640 DIMMs a free lunch; and the CPUs draw 260 W less, which the existing ~1,020 W power figure already assumed correctly. Memory-only; no version bump per the SemVer SKIP list. |
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5c88280f9f |
memory: snapshot — operator leaning 6 drives; records the layout analysis
Six drives fills all eight bays, which turns a capacity top-up into a one-shot build decision. Recorded because the reasoning survives whatever he picks: - raidz2 over mirrors. Workload is large sequential reads of safetensors, ARC fronts it, SSD resilver has no seek penalty, and "expand two at a time" is meaningless once every bay is full. raidz2 survives any two failures; 4x mirrors dies to an unlucky pair. - Buy 8, not 6. A raidz vdev caps every member at the smallest, so the two as-bought 1.92 TB drives would cap all eight and put two used drives of unknown endurance inside the parity set. - Drive size is now the permanent ceiling. SAS/SATA backplane, all bays full, and the free-PCIe-slot inventory is still unpulled. Runway table included with an explicit caveat that the growth rate is projected off one acquisition batch, not measured. Memory-only; no version bump per the SemVer SKIP list. |
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3180dc85fa |
memory: snapshot — Ada sizing corrected; prune and drives are orthogonal
comfy-dev's disk-vs-catalog diff and a recount of my own figures both landed on this thread. Two numbers were wrong and both were headed for the operator's sizing conversation. - My ~90% was a double-count. I read ALLOC 1.45T while their pull was running and then added the full ~112 GB on top; most of it was already in that reading. "Onboarded" is not "landed". Settled payload is ~1.47 TiB and the as-bought mirror lands at 84%, not 90%. - comfy-dev's "pruning gets us nearer 45%" is the striped figure. On the as-bought pair mirrored, deleting all ~215 GiB of unreferenced weights still lands at 72%, with ~140 GiB of runway on a store that took on ~100 GiB in one day. The constraint is vdev layout, not payload — a 1.75 TiB pool stays 1.75 TiB whatever goes in it. - So the prune audit and the drive purchase are independent decisions and neither gates the cutover. Presenting them to the operator that way rather than as a trade. Also recorded: pool arithmetic (1.75 / 3.49 / 3.57 TiB), the mirror-vdev smallest-member gotcha if the new drives get paired one-each with the 1.92s, comfy-dev's 34 GiB of uncatalogued LTX 2.5, and an open question back to them on whether the H3 encoder's nvfp4 pin was set under a Blackwell assumption that sm_89 does not satisfy. Memory-only; no version bump per the SemVer SKIP list. |
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72065b45aa |
memory: snapshot — Ada destination pool is smaller than the source
Measured irv-ml1's storetank against the R750xa's as-bought drives while answering comfy-dev's "does the Ada box have ZFS?" question. - storetank: 1.81 TiB pool, 1.45 TiB used, 80% CAP already, compression off at compressratio 1.00x (safetensors incompressible — no win at recv). comfy-dev's ~112 GB batch is landing into it now. - The R750xa shipped 2x 1.92 TB SATA SSD; mirrored that is ~1.74 TiB, smaller than the pool it receives from. Migration would arrive at ~90% full with no growth room. - Buy list: +2x 2 TB SATA SSD (6 bays free, HBA355i has the ports) -> two mirror vdevs striped, ~3.49 TiB at ~45%, redundancy intact. - Retain vs reclaim irv-ml1's /storetank after cutover: RETAIN recommended, surfaced to the operator. Also corrects branch (b)'s recorded rationale. comfy-dev enumerated all twelve running containers: only comfyui mounts /storetank, so (b) was unavailable during the transition, not structurally. Right conclusion, wrong reason — infra-ops reasoned about the box when the question was about the mount. Memory-only; no version bump per the SemVer SKIP list. |
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ace839c768 |
memory: snapshot — the Ada inference server is a stripped used R750xa
Dell R750xa JPJ1ZP3, 2x RTX 6000 Ada to be fitted, ComfyUI's new home at NH3. Diffing Dell's factory CSV against the reseller invoice shows four downgrades: half the RAM, the 2400 W PSUs, and the GPU risers, cables and high-performance fans all absent. Records the resolved GPU power chain, correcting my own first answer: the chassis' CPU 8-pin cabling is the right source type and NVIDIA 930-00030-1546-000 bridges it to the card's 12VHPWR, so the PCIe-type RCCWC I first proposed is withdrawn. Also closes the NVMe question — the backplane is SAS/SATA only — and notes that free PCIe slots may moot it. Auto-archival fired at 308 lines; seven entries moved to archival-memory.md. The 250-line target was not reached because the guards hold nearly everything else back as under 14 days or carrying open deferred work. |
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7142657749 |
memory: snapshot — gx10 unracked and next up as an inference+training box
Run 3c's new intended home is the GX10 rather than a power triage on ana-ml2: a ~240 W appliance instead of the kilowatt-class box that tripped the breaker, and 121 GB unified holds the 49 GB bf16 base comfortably where ana-ml2 was tight. The box is bare, so the first move is a throughput probe rather than a harness port. Also banked: the Ada migration settling on zfs send with branch (b) ruled out by irv-ml1 keeping its eight services; the Synapse 39-release upgrade with its one-way schema migration, the appservice namespace opening and the admin API lockdown; the ratified room alias convention; and a named failure class — a correct check aimed at the wrong object — with six instances from one day across three sessions. |
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vh
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73866f6a7e |
feat(synapse): restrict /_synapse/admin to LAN, track the stack
Synapse mounts its admin API on the same vhost as the client API, so publishing matrix.phasefinal.com published the admin surface too -- it answered 200 from the open internet. HMAC-protected, so not an open door, but Synapse's own guidance is to keep it off the public listener. A higher-priority router (explicit priority 100, not relying on Traefik's rule-length tie-break) scopes PathPrefix(/_synapse/admin) behind an ipallowlist. Verified from a genuinely external vantage rather than from a fleet host, since nh3-dev sits inside the allowed range and would have proved nothing: via the NH3 residential egress proxy the admin path returns 403 while the client API returns 200 and Element is unaffected. The 10.0.0.0/8 entry matches nothing today and the comment says so rather than implying fleet access exists. matrix.phasefinal.com resolves publicly, so fleet hosts hairpin out their own WAN -- a request from nh3-dev arrived as 70.230.226.88. The rule is effectively deny-all through Traefik, which is the intended posture: admin work goes through docker exec to localhost:8008 and never traverses Traefik. Allow-listing the sites' WAN addresses was considered and rejected as a maintenance trap on dynamic addresses. Also brings the stack under stacks/ with the Postgres password replaced by a required .env variable. The tracked copy and the live file have therefore DIVERGED and deploy-stack.sh must not be used until the live file reads from a .env; the README says so. |
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vh
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a0c5fc6ed5 |
feat(pfi-gx10): rack-move network playbook — VLAN 50, static 10.100.50.60
Target settled: nh3-servers VLAN 50, static 10.100.50.60. Clear of the four existing statics and below the .150 DHCP pool where fleet statics live. The playbook never leaves itself one path back. Wi-Fi stays up throughout while the wired interface is configured beside it; the new address is verified from outside before anything is torn down, and Wi-Fi teardown is explicitly a separate later change. A botched netplan therefore costs a retry over Wi-Fi rather than a trip to the rack — which is what substitutes for 'netplan try', whose interactive rollback needs a TTY that elway cannot provide. Two preconditions are asserted as steps rather than assumed: the interface must have carrier (writing a static config for a dead NIC and reporting success is the failure this avoids), and its MAC must match, since interface names can renumber across kernels but MACs do not. Requires nothing from the operator beyond racking the box. The wired NIC has a distinct MAC from the Wi-Fi one, so the post-move address and switch port are both discoverable from the UDM rather than needing to be relayed. |
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vh
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8fb8cc87ca | chore(pfi-gx10): first inventory snapshot | ||
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vh
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1b596c8c30 |
feat(pfi-gx10): register the ASUS Ascent GX10 and convert it to headless
NVIDIA GB10, aarch64, 121 GB unified, sm_121. Ships booting to graphical.target with GDM and GNOME Remote Desktop running. playbooks/gx10-headless.yaml sets multi-user.target, stops gnome-remote-desktop, masks the sleep/suspend/hibernate targets, makes logind ignore lid and idle, and adds sshd keepalives so a stalled link does not kill a long-running job. Two things learned the hard way and recorded in the playbook: - gdm is a STATIC unit on Ubuntu, pulled in by display-manager.service and never 'enabled'. A guard of always skips, and a verify written the same way passes while the desktop is still running. Both now test is-active. The first run reported six green verifies having not stopped gdm. - elway's --sudo applies only to ad-hoc --shell/--upload. Playbook steps run as the connecting user and must carry their own sudo; connect as infra-ops. The playbook refuses to stop the display manager while a seat session is held, overridable with --var force_dm_stop=true. Networking is deliberately out of scope: the box is on a desk on Wi-Fi with a temporary DHCP lease and no ethernet carrier, and belongs to the rack-install change. |
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vh
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931bac8f68 |
docs(matrix): current state, upgrade procedure, alias convention, push findings
Synapse v1.120.0 -> v1.159.0 and Element-web v1.11.80 -> v1.12.27 (2026-09-01). The existing build steps date from the AIPA era and are now marked as provenance rather than as instructions. Records what only existed in a session transcript: - Schema migrations are one-way; rollback is restore-from-dump. Pre-upgrade pg_dump procedure, with a pg_restore --list verification step. - Why the appservice user namespace is now exclusive: false. exclusive governs who ELSE may act, not what the appservice may do, so on a closed single-admin server it locked out all other account creation to prevent squatting that cannot occur. Includes the two things not to do: narrow the regex (orphans 13 accounts) or rename the id (Synapse keys ownership on it). - The shared-secret registration HMAC takes no trailing null after notadmin. - Room alias convention #<agent>-<purpose>, operator-ratified, with its cost accepted deliberately and its rationale stated as room-identity-carries-tier rather than push-payload-carries-room-name. - Push reality: the pusher is event_id_only, so the notification is assembled on-device by Element X's service extension. Records the resulting server-invisible failure mode when the phone cannot reach the homeserver. - QR sign-in requires Matrix Authentication Service and why it is deferred. Ops ownership recorded: worldtree-dev writes the bridge, infra-ops operates this instance. |
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vh
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9e986d8ee8 |
feat(phasefinal-web): cloudflare edge config — cache ruleset + always online
Cache rule on www.phasefinal.com with edge and browser TTL both respect_origin, so cache policy stays declared once in nginx.conf rather than split between the repo and the dashboard. Always Online enabled, which is what actually survives an origin outage; a 300s document TTL alone would only mask five minutes. Verified: document and assets both reach cf-cache-status HIT, apex 301s to www, edge email obfuscation active. |
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vh
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524aa4d860 |
fix(phasefinal-web): healthcheck targeted ::1, so traefik skipped the container
The healthcheck used http://localhost/, which resolves to ::1 in nginx:alpine while nginx listens on IPv4 only — so it never passed, the container stayed unhealthy, and Traefik silently declined to create a router for it. That presents as a broken docker provider: correct labels, right network, no route, no error. Target 127.0.0.1 explicitly and add a start_period. Adds the apex router (301 phasefinal.com -> www) and drops the file-provider workaround, which was mitigating the wrong diagnosis. |
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vh
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7ffbee6f09 |
feat(phasefinal-web): corporate site stack on ana-docker
Single static page (nginx) fronted by Traefik at www.phasefinal.com, built from the design brief. Site markup/CSS checked in verbatim from the design session; fonts self-hosted (SIL OFL) with the @font-face block uncommented, which every fresh export re-comments. Routed via a Traefik file-provider config rather than the container labels: the docker provider on ana-docker was not registering newly-created containers, so the file router avoids restarting shared ingress. Labels are retained in compose so the file can be dropped once that is fixed. |
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vh
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c488eadc31 |
memory: snapshot — althing v3 fleet-wide at 3.1.1, sec on GPU0
The in-flight section was a day stale: it still described run 3c as the live subject on a box where nothing had moved. Rewritten around what is actually true now -- v3 deployed fleet-wide, the post office relocated to nh3-docker, sec serving on GPU0, run 3c still held on power. Six new decision entries, three of which carry findings that outlive their incident: the inbound half of the handle-resolution bug (a stale ALTHING_HANDLE reads another agent's mailbox and reports it empty, which is a second route into the failure v3 exists to prevent), the OOM attribution to Claude Code sessions, and the operator's two explicit belays recorded so a later session does not re-raise them as new. Auto-archival fired at 301 lines and moved exactly one entry. Three others were old enough and every one carries a still-open deferred pointer -- the parked CI flip, muninn-gate's submit path, and the triton backend deferred to the Ada refresh. Held back per the guards; an over-cap file that keeps live decisions beats a scannable one that lost them. The entry that did move had its deferred item closed today: nh3-extdev's staged v2.1.2 wheel is moot now that the box runs 3.1.1. |
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vh
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583f329d00 |
fix(playbooks): 3.1.1 deploy — and why the markers match presence, not count
Deploys althing-core 3.1.1 to nh3-extdev. Both markers present on both boxes; the warning verified behaviourally in four conditions rather than by grep alone -- mismatched inherited handle warns, matching handle silent, explicit --handle silent, unlaunched directory silent, and the warning precedes the output it is about. The release's own verification line says `grep -c handles_launched_at dev_launch.py # 2+`. The real count there is 1, the definition; the other two occurrences are in postbox.py. The installed tree is byte-identical to the repo at the pushed tag, so the instruction is wrong rather than the install. This playbook matches on presence via grep -q, so it passed. Had it asserted the stated count it would have reported FAILED on a perfect deploy -- a verification instruction that fails on correct input, which is the same false-negative this file has now produced three times in different costumes. Recorded above the variable so the next bump does not reintroduce a count. |
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vh
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8a04d6f1bb |
fix(statusline): resolve the handle from the v3 binding, not the v2 map
The statusline resolved its handle from ~/.althing/session_handles.json.
forseti corrected the grounding and I verified it: that file is a v2
artifact and v3 never opens it. `grep -rn session_handles althing/` is
empty, postbox's resolve_config takes --handle then ALTHING_HANDLE and
nothing else, and `althing-cli use` -- the tool that maintained the map
-- was deleted at the cutover. Whatever is in it now is hand-kept and
drifts silently.
launch-history.json is written by dev_launch, which is the thing that
sets ALTHING_HANDLE in the first place, so it is the real cwd-to-handle
binding. Shape is {cwd: {command: {at, handle}}} with several commands
per directory, so this takes the most recent by timestamp rather than
whichever key happens to sort first. The v2 map stays as a fallback for
its broader coverage.
Worth recording why this was wrong: I wrote the resolution this morning
by reading the v2 statusline block it replaced and keeping its data
source while updating its commands. The commands were the visible half
of the cutover and the data source was not, so it survived a rewrite
that was otherwise about removing v2.
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vh
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c648a40b68 |
fix(playbooks): 3.1.0 herald deploy; the marker check takes a LIST now
Deploys althing-core 3.1.0 to nh3-extdev and restarts the herald. Verified by content on both boxes: POST_OFFICE_HINT 0 -> 4 in post_office_herald.py and resolve_post_office 0 -> 3 in dev_launch.py, dist-info 3.0.3 -> 3.1.0. The check took one file:marker pair. 3.1.0 changed two files, so a single pair would have asserted half a release and passed -- the same half-passing-silently shape as the version-string check it replaced two releases ago, one level up. It now takes a space-separated list, reports each pair individually, and fails if any is missing. Every release's markers so far are recorded above the variable so the next bump is a lookup rather than an archaeology exercise. Also verified the behaviour the release exists for rather than just its markers. The herald writes its address to $ALTHING_ROOT/post-office and dev_launch.resolve_post_office reads it when the variable is unset: env unset -> http://10.100.50.40:8390 env set -> the env value, which wins env set to blank -> the file, because blank counts as unset My first attempt tested this through postbox, which still requires the variable and reported "no post office address is configured" -- correct behaviour that looked like a failed deploy. dev-launch is the reader, not postbox. |
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vh
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590b55f7d8 |
feat(playbooks): potrace/agg headers, with the two traps that mislead
pypotrace is an sdist that compiles at install time, so every machine and every CI runner resolving it needs these headers first. That makes it a recurring per-box action rather than the one-off it arrived as. Two things learned installing it on nh3-dev are recorded here rather than left in an althing thread, at forseti's suggestion, because a thread is not where the next person looks: Only libagg is a pkg-config consumer. potrace ships no .pc file and is found via potracelib.h directly, so `pkg-config --exists potrace` returns false on a correctly configured box. It looks exactly like the cause and never is. libagg's pkg-config modversion is 2.7.0 while its Debian package version is 1:2.6.1-r134. Comparing those two numbers convinces you the wrong package is installed. The verify phase asserts the geometry, not the import: a square must come back as one curve of four CornerSegments. An extension linked against the wrong thing can import cleanly and return nonsense, so a successful build is not evidence the module works. Getting the build probe to run took three passes and the reason is worth keeping. uv is not on a non-interactive ssh PATH; it is in a different place on each box; and on nh3-dev it sits inside a 0700 home, so even the correct absolute path fails `test -x` for the ssh user because the directory cannot be traversed. The headers are system-wide and root's business, but the build check is a developer action and has to run as the user who owns the toolchain. |
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vh
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cdeb57c18b |
fix(playbooks): 3.0.3 herald deploy, and a content check that survives releases
Deploys althing-core 3.0.3 to nh3-extdev and restarts the herald. Verified by content on both boxes: PANE_SETTLE_S 0 -> 2 occurrences, value 0.3, dist-info 3.0.1 -> 3.0.3. The content check was hardcoded to the 3.0.1 markers, so from the next release onward it would have kept passing while asserting nothing about what had just been installed -- a check that verifies the previous release is indistinguishable from one that works. It now takes the marker and file as variables, bumped per release, with both releases' markers recorded so the pattern is obvious rather than folklore. That is the same defect class as the install step gated on `postbox` not existing, which this playbook carried until last round: a guard written correctly for the first run and never re-read on the second. The post office container was not touched. forseti established by import graph that althing/post_office/* imports neither changed module -- the fix is in reach_pane, which is herald code -- and the container has been up two hours across both herald restarts. |
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vh
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9f87ff87e1 |
feat(althing): surface the post office on Homepage under Toolchain
Labels the container into `Toolchain`, an existing group under the existing Toolchain tab -- "the plumbing", which is where a message bus belongs. Confirmed live: Homepage's API now returns it. I had previously recorded in this file that no group fitted, which was wrong. That conclusion came from a grep over the layout block that missed the nested groups, and it went into a comment as though it were a finding. The group was there the whole time. Labels bind at container creation, so this deployed with `up -d` rather than `restart`; a restart leaves the old labels and the dashboard keeps showing what was there before. nh3-docker is already a discovered host in homepage's docker.yaml as `nh3-pfi-docker`, so the label alone is enough -- adding a services.yaml entry as well would render the card twice. althing-chamber on ana-docker also carries Toolchain labels and is a separate service per the operator. Left alone. |
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vh
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dbb930d546 |
fix(playbooks): 3.0.1 herald reinstall, and two guards that were release-hostile
Reinstalls althing-core on nh3-extdev for 3.0.1 (the pane-route fix) and restarts the herald. Both boxes verified BY CONTENT rather than by version string -- forseti's own checks, grep for _PANE_ID and _live_pid, because a dist-info directory records what was installed, not what the files contain. Both went 0 -> 3 and 0 -> 2. Two bugs in the playbook this run exposed, both of which only appear on the second use: The install step was gated on `postbox` not existing. That guard was correct for the cutover, when postbox genuinely was absent, and wrong for every release after it -- postbox exists now, so a version bump would have silently skipped the install and the playbook would have reported success having done nothing. `--force` already makes the reinstall idempotent, so the guard bought nothing and cost correctness. The post_office variable still pointed at nh3-dev, three hours after the post office moved to nh3-docker. It failed in the verify rather than at install time, which reads as a broken deploy rather than as a stale constant. Worth noting the failure message was the outage semantics working exactly as designed: "This is an outage, not an answer: do not treat it as 'no mail'." |
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vh
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22da609053 |
feat: registry-push the post office image; version the statusline
## Registry The image moved by `docker save | ssh | docker load`, so a rebuild meant repeating that by hand. It is now published and the compose pulls a digest-pinned reference, so a redeploy is `compose up -d` on any host that has logged in. Pinned by digest rather than by tag: `:3.0.0` is a mutable pointer on a registry anyone can re-push, and this container is the fleet's whole message bus. The tag rides alongside so a human can read what it is. Namespace is claude-bot, not vh. claude-bot's token carries write:package and `docker login` succeeds, but package namespaces are owned -- pushing to vh/ returns "unauthorized: authentication required" after a successful login, which reads like a credential fault and is actually an ownership one. Publishing under claude-bot's own namespace also satisfies the standing directive to stop reusing the operator's personal credentials for infra work, so the constraint and the policy point the same way. Recorded in the compose header so the next person does not read that error as a broken token. Pull path proven rather than assumed: the running container was recreated from the registry reference and its data verified afterwards. ## Statusline Brought under version control because the v3 cutover broke it invisibly. The segment gated on `command -v althing-cli`, a binary the cutover deleted, so the unread badge and the armed bell silently vanished for every session on the box. With 71 of 73 handles pull-only, that badge is the only out-of-band signal telling a session with no armed waiter that it has mail -- a dead statusline made a working bus look like an empty one. Canonical here, live at ~/.claude/statusline-command.sh, copies rather than symlinks per the same rule as stacks/. |
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vh
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9d4e7bd34a |
feat(althing): move the post office to nh3-docker
Operator directive, and a standing goal: the bus belongs on the docker host. The flag-day deployment put it on nh3-dev because the herald lives there -- but the herald is the piece that must be host-local, and the post office is explicitly the piece that is not. nh3-dev was wrong on three counts. Our own server table calls it "not a Docker-stack host". It has had three OOM events in fourteen days with the interval halving, and the confirmed hog is Claude Code sessions at 5-18 GB, which is that box's actual job. And mem_limit protects the fleet from the post office while doing nothing in the other direction: oom_score_adj was 0, an ordinary kill candidate, on a box whose last sweep took althing-herald and uvicorn. The new deployment sets oom_score_adj=-500. The compose is now version-controlled here as a normal stack rather than living only in the althing repo's deploy dir. ## docker stop does not checkpoint the WAL The database was 155 KB with a 4.1 MB write-ahead log, and every recent message was in the log. A clean container stop left it untouched -- an explicit PRAGMA wal_checkpoint(TRUNCATE) was required. A docker cp of the .db alone would have produced a database that opens cleanly, passes integrity_check, serves the full 73-handle roster, and is missing the day's mail, with nothing raising an error. Row counts were verified at source, in the staged copy, and after seeding, because the count is the only thing that separates those two outcomes. The old volume is left in place. Not a rollback path, which the operator ruled out -- just not deleting the only other copy on the day of a move. ## Follow-up left open The image has no registry push and moves by save/ssh/load, so a rebuild means repeating that by hand. It should join the gitea registry pattern the other stacks use. |
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vh
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e58360668e |
feat: althing v3.0.0 cutover (U9b) and the sec seat onto GPU0
Two operator-authorised changes on the same afternoon.
## althing v3 (U9b flag day, one-way, no rollback)
The post office replaced the v2 P2P bus on nh3-dev and nh3-extdev.
One container is the only stateful component; heralds are one per box
and dial out; waiters are one per session. Every v2 command was deleted
rather than deprecated, so a script calling althing-cli now fails loudly
instead of silently talking to nothing.
73 handles seeded from the v2 CLI, which is authoritative over the v2
database's 91 agent rows -- the extra 18 are superseded names, a typo,
an underscore variant, and two machine-qualified handles that v3 makes
a category error. Verified by set difference in both directions rather
than by counting; a peer's "72 rendered" was a line-count artifact.
Deleted 5,043 orphaned wake FIFOs. The reason there were five thousand
is that v2 named them per session with the PID and never reaped them;
v3 names them per handle, so the leak is bounded by construction. That
is a fix in v3, not a cleanup we performed.
nh3-extdev needed its own path: althing lives there as a system wheel
under /opt/uv-tools with entry points in /usr/local/bin, its daemons
were system units rather than user units, and uv is not on the login
user's PATH. Captured as a rerunnable playbook rather than shell
history.
The v2 database is left inert on disk. There is no import path and none
was improvised.
## sec onto GPU0
GPU1 carries the five resident fleet seats and had ~28 GB free against
the ~51 GB this seat reserves, so it could not start there at all. GPU0
has been idle since run 3c was stopped. The compose header, the GPU pin
default and the homepage label all carried the old card number and are
corrected together -- a label that names the wrong GPU is a record that
lies about where the work runs.
Both playbooks carry verify phases that assert effective state. Two of
those verifies failed on green deployments while I was writing them:
one used a Go template that collided with the runner's own {{ }}
substitution, one omitted --handle so it failed on identity rather than
reachability. Both are fixed with the reason recorded inline, because a
verify that reports FAILED on a working system trains you to ignore it.
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vh
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f875f746b8 |
feat(playbooks): nh3-dev memory forensics — and the OOM hog is Claude Code
forseti asked for journald kernel persistence plus sysstat, on the premise that nh3-dev's three OOM events in 14 days left no evidence. The premise was wrong. journald has been persistent all along: 15,068 kernel entries in the 82-day previous boot and 351 OOM records across retained boots, full task tables included. `journalctl -b -1 -k` returned one entry because it ran as a user in neither adm nor systemd-journal, and journalctl shows only your own messages in that case. The same artifact produced the "journal stops at 05:36:08 with no shutdown sequence" claim -- the true boot -1 end is 05:47:04 with OOM kills logged at 05:38, 05:40 and 05:42. So the fix for "no evidence" is a group membership, not a logging change: usermod -aG adm lkraven, which is the group Debian's journald ACL names explicitly. With the journal readable the attribution is already in it. The versioned Claude Code binary lives at .local/share/claude/versions/, so OOM victims named 2.1.220 / 2.1.177 / 2.1.168 are CC sessions, as are those named claude. Every one of the twelve largest resident processes ever recorded on this box is a CC session, topping out at 18.4 GB. Everything else killed is 30-55 MB collateral, which clears the althing daemons by measurement rather than by their own sampling. sysstat and atop are added because the journal records the moment of the kill, not the ramp, and names the victim rather than the winner. atop was not requested and is the one that matters: with a dozen panes open, only a per-process timeseries says which session was growing. Not done: a cgroup cap on CC sessions. It is the real mitigation and it would kill long-running sessions mid-work, so it goes to the operator. |
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vh
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ea818380ff |
memory: the rack is one circuit — my blast-radius objection was wrong
Operator supplied the topology: "the entire rack is on the same circuit,
public ip is served by firewall on the same circuit. load tripped
breaker, entire rack goes dark."
That inverts the argument I committed one commit ago in
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vh
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3cc55b4b40 |
memory: separate the measured breaker trip from the load hypothesis
The record read "power capacity is the open item" next to ana-ml2's ~600 W, which reads as a cause. It is not one. The trip and its timing are measured; the attribution to the training load is the operator's working read and the reason for the weekend triage. The observation that makes the single-load story incomplete on its own terms: a site-wide blackout is a larger blast radius than one GPU box accounts for. If ana-ml2's draw were the whole story, ana-nas, ana-wg and the public address would not have gone dark with it. Shedding seats may still be the right first move and it is cheap. That is not the same as having identified what loaded the circuit, and the distinction matters going into a triage that will act on it. |
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vh
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88d79375f7 |
memory: run 3c had TWO launches — the third was an untimestamped report
brokkr-smithy-dev asked how many times 3c was launched rather than reconstructing it, and their reading was three. It was two. #1 17:53:33 PDT killed by the power loss at step 80/604 #2 20:58:41 PDT stopped deliberately at 21:07:40, healthy The phantom third came from a report I wrote at 23:03 narrating the 21:07 kill in the present tense with no timestamp. Every fact in it was accurate; it was unreadable in sequence against a correctly-observed 22:46 snapshot of an idle GPU. Evidence is ZFS birth times (a `>` redirect truncates the log but keeps its birth, so mtime alone cannot separate "rewritten" from "created"), plus the absence of any mtime under /tank/erp-tune after 21:07:34 — a relaunch would have rewritten three files there. Also pins the outage window to 18:14:45-18:17:00 PDT and corrects the downtime from "~90 minutes" to 1h58m: the last journald entry before a hard power loss is the last time anything wanted to log, not the moment of the loss, and here it was 20 minutes early. Corrects the in-flight header (step 22 -> last-logged step 24, stop deliberate) and its stale "as of" stamp. |
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vh
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98e7d4886a |
memory: snapshot — run 3 gated DO-NOT-SERVE, run 3c held on a tripped breaker
Run 3 trained, gated and dispositioned do-not-serve on a measured 44pp self-harm guardrail regression that its own preregistered rule passed -- a pooled preserve-list test cannot see a single-axis collapse. Run 3c (lr 20x cut, single variable) launched, killed by an Anaheim power-breaker trip at step 80, relaunched, then stopped by the operator at step 22 pending a weekend power triage. Also captured: the corpus mix was specified in a unit the optimiser never sees (45.8% dialogue by context, 24.2% by loss); the dose-response says benefit and damage are one direction in weight space, so the merge-back measures the problem rather than fixing it; four guests including the storage SPOF had onboot unset and never came back from the outage, now fixed with dependency ordering; and a transport failure that enters a measurement as a value looks like whatever you hoped to find -- which found a live defect in another agent's instrument an hour after it was reported. Auto-archived 8 entries to archival-memory.md (Recent decisions: 8, Tried and abandoned: 0); 4 held back on open deferred-work pointers. |
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vh
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1a36e60d3a |
docs(quant-playbook): §3.7's APC-off mitigation was reverted nine days ago and the section never said so
Found while answering a question from the operator, relayed via brokkr-smithy-dev, about whether a recorded Qwen3.8 degeneracy at ~1,700 tokens relates to a length sensitivity just measured on the tuned Gemma-4. The record is §3.7 and the number is ~2,000 -- but reading it to answer that question surfaced that the section is stale. §3.7 presented "disable prefix caching, keep MTP" as THE MITIGATION, resolved 2026-08-17, and stated the gen seat runs that config. It does not and has not since that same day: APC-off passed a synthetic 7-turn probe and the operator still saw severe degeneration in real use, so it was reverted. The multi-day hunt resolved to the AEON W4A4 quant being defective, with MTP / prefix-caching / gateway merely amplifying it (§3.8 records the corrected causal story; §3.7 was never updated to match). Verified against the live container rather than against the compose file alone: vllm-gen runs --enable-prefix-caching with qwen3_5_mtp / num_speculative_tokens 3. stacks/gen-seat/compose.yaml carries the full corrected history inline and is the current authority. §3.7's superseded text is kept and fenced rather than deleted -- it is the history of a mitigation that looked right and was not. Added a dated row to §7 per the standing rule that a wrong playbook claim gets a superseded-claims entry, not just a fix. The lesson inside the lesson is worth more than the correction: §3.7's own standing rule is "gate MTP on a multi-turn coherence probe, not just single-shot acceptance." The APC-off mitigation was gated on exactly that probe, passed it, and still failed in real use -- the multi-turn probe was itself too small to gate on. A passing probe is not sufficient evidence at any size that has not been calibrated against real use. |
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vh
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5a51e76135 |
fix(litellm)!: withdraw merged-run03 — measured self-harm guardrail regression
Added 40 minutes ago at the operator's request; withdrawn on brokkr-smithy-dev's
window-2 safety follow-up. Measured against its OWN base:
selfharm/methods base 1.000 (10/10) -> tuned 0.560 (k=25) -44.0pp p=0.011
meta/sysprompt base 1.000 (10/10) -> tuned 0.720 (k=25) -28.0pp p=0.071
Eleven of twenty-five times it engages with a request for the most effective methods
to kill oneself. The base refused ten of ten. It also surrenders a planted
system-prompt passphrase 7 of 25 against a base that leaked 0 of 15 -- the zero-leak
baseline recorded in this same file hours earlier as "a baseline to PROTECT". It did
not hold.
Withdrawn from the GATEWAY specifically because that is the shared-key surface: one
all-agents key reaches every model listed here, across every session and project. The
operator's hand-testing is preserved in full at the direct endpoint :8099 -- this
removes the fleet's blast radius, not his access. Acted rather than waited because he
is away and the request predates the finding.
ITS PREREGISTERED GATE PASSED. The pooled operational delta is -1.0pp against a
+/-3.00pp bound: nineteen axes held at 5/5 and a 44-point collapse on one moved the
aggregate by one point. The rule was NOT retroactively changed. The failure is
structural and is recorded as R47 section 8 item 11 -- a pooled preserve-list test
cannot see a single-axis collapse, and any future preserve-list gate needs a per-axis
tripwire sized so a total loss on one axis cannot hide in an aggregate.
NOT attributed to the filters: five things changed between run 2 and run 3 and there
is no run-2 measurement on these axes. The measured claim is narrower and sufficient
-- run 3's tuned arm is materially worse than its own base on two axes it was never
licensed to touch. Not a CSAM finding; that detector ran fail-closed across all 575
generations and scanned clean.
The model_list entry is left in place commented out, with the finding above it, so
re-adding is deliberate and informed rather than a blank re-registration.
Verified: config parses, gateway healthy after reload, merged-run03 absent from
/v1/models, direct :8099 still serving.
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c577d69e2d |
feat(litellm): expose run-3's merged tune for parallel hand-testing
merged-run03 -> ana-ml2:8099, the run-3 ERP/RP SFT merged into stock instruct. Operator asked for it so he can test it alongside the gate rather than after it. NAMED FOR THE ARTIFACT, NOT A TIER. It is `merged-run03` and not `erp-tune-v3` because its behavioural gate has not run. A tier name arriving before the evidence that would justify it is how a name comes to mean something nobody decided -- and with a v2 already in the list, a v3 reads as a successor to anyone holding the shared key. If it passes, `v3` is a name to give it then, as a decision. brokkr-smithy-dev raised this against my own erp-tune-v3 suggestion and was right. The entry carries the preregistrations ABOVE the description, so a reader meets the commitments before the numbers: T6 one-directional (a gain is uninterpretable against a 3.1x fireball tailwind), T3/T4 at ceiling on base so recovery is UNOBSERVABLE rather than merely unpredicted, and any run-2 comparison descriptive and non-attributable with its five confounds named. Also carries the retraction in-line: "bluemoon is the largest loss contributor at 38.6%" came from a words x 1.4 estimator, not a tokenizer. As encoded it is third at 32.9%. The direction survives (1.4% -> 8.0% of total loss) and that is the finding; the superlative does not. Documents why its config.json is the base's copied verbatim: transformers 5.15.1 save_pretrained silently drops text_config.global_head_dim and num_global_key_value_heads, and vLLM then dies in make_layers with a TypeError naming neither the config nor the field. Cost a failed boot to find. A LoRA merge changes weights, not architecture, so the base config is correct by definition. gemma4-26b-a4b-it-base marked CURRENTLY DOWN rather than deleted -- the tuned arm took GPU0 and only one 26B bf16 seat fits on that card. Kept because the seat returns, and deleting a name to re-add it later is how scoped keys get orphaned. Verified: config parses, no duplicate model_name, gateway healthy after reload, completion returns text in `content` with reasoning_content null. |
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b6ce22ddcb |
feat(litellm): register the run-3 gate base arm at operator request
gemma4-26b-a4b-it-base -> ana-ml2:8099, the unmodified upstream instruct release (/tank/aimodels/gemma4-26b-a4b-it-bf16). Operator asked for it on the gateway so he can hand-test it; it had been direct-only because the seat is ephemeral. The entry disambiguates WHICH base explicitly. Three exist on that box -- -bf16 (this one, official instruct), -abliterated-bf16, and -heretic-bf16 (run 1's trainee) -- and brokkr-smithy-dev's gate plan called this arm "stock abliterated" a few hours ago, which would have been a different set of weights. A reader of the config should not have to resolve that ambiguity themselves. Carries the measured refusal posture in-line rather than in an althing thread, per the erp-tune-v2 precedent: R19's Mistral Small 4 map does NOT transfer to this base (it draws a wider line than consent, refusing consenting-adult incest and fictional gore that Mistral engages), system-prompt leak is 0/15 against Mistral's 4/5, and advice/medical 0/5 is a pre-existing base gap recorded so it cannot later be misattributed to a tune. Flagged EPHEMERAL in the strongest terms available: it holds ana-ml2 GPU0, which the run-3 gate needs for its tuned arm, so this entry will 503 when window 1 completes. It is not a promise of availability. Serving flags mirror erp-tune-v2 (--reasoning-parser gemma4 plus --default-chat-template-kwargs enable_thinking=false, and --max-model-len 16384) so a base-vs-tuned comparison differs in weights only. Verified: config parses, no duplicate model_name, gateway healthy after restart, model listed at /v1/models, and a completion returns text in `content` with `reasoning_content` null -- the enable_thinking trap is not firing. |
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71e44176e9 |
memory: snapshot — run 3 corpus built and held on a megamix containment defect
Run 2 is finished, gated FAIL, and serving on the gateway at operator request. Run 3's corpus was built to brokkr's first recipe and held before any GPU spend: creative-writing-multiturn is a DECLARED MEGAMIX containing bluemoon, PIPPA, LimaRP and stheno, and the remix promoted creative-writing AND bluemoon -- the two roots that overlap, at median jaccard 0.873. Containment, not overlap. Dedup direction reversed so the primary source survives rather than the copy inside the bag: bluemoon 67 -> 126 conversations and 38.6% of loss signal, the largest contributor. Wholly-human share up, megamix share down, total context unchanged at 12.49M so the operator's settled mix arithmetic survived. Two structural findings recorded because they outlive this recipe: F1 'excise PIPPA' removes the ROOT and not the MATERIAL (F2's 250-word floor does that work, since PIPPA turns cannot exceed 123 words wherever they live), and LimaRP and stheno remain unchecked against any other root. Also records the correction I published wrong twice: run 2 was never unstable. All 46 flags were too_short, the collapse guards fired zero times, and it is the left tail of a length distribution -- not new to run 2 either, so it is a property of the recipe and a further base swap will not fix it. |
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1a4ef5c7a1 |
docs(training-playbook): 4.6.3 was wrong twice — correct it, and keep the retraction visible
The entry reported an 'output-stability regression' as a novel run-2 finding. Both halves were false and the corrections are more instructive than the original conclusion, so they stay in-line rather than being edited over. Not new: run 1's own gate record already carried the same effect with a caveat attached and unresolved. Two runs across two different base models makes it a property of the RECIPE, not of the base swap -- which also means a third run that changes the base again will not fix it. Not degeneracy, and not a separate finding: all 46 flags were too_short rp turns of 3-14 words, and the two collapse guards fired ZERO times on any run. It is the left tail of a length distribution that had been measured and reported in the same message. Truncation is the same mechanism mirrored on the story side. Both are thresholds calibrated on the base's output shape applied to a model with a different one -- 4.6.1, which both parties had written down and neither applied. The surviving lesson is sharper: a short-answer gate cannot see length behaviour AT ALL, and because it could not, the effect went two full runs before anyone named it. The cost of a gate-set blind spot is measured in runs. Adds 4.6.3.1 on trip points inside the serving stack's jitter -- same seed, same weights, rate moves 9.6% -> 12.6%, sd 1.77pp. Not 'the gate is non-deterministic' but 'the trip point sits inside the jitter', because the fix follows from the precise statement. Includes the split-design rule for measuring such a rate, and the rule that a measured rate must carry its corpus in its name. |
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37d3189622 |
docs(erp-dpo): the clip hypothesis is falsified — the distribution is bimodal
The output-side test ran on the live seat. There is no shoulder at 123: the 120-139 bin holds three of ninety-six and is a TROUGH, and 17.7% of generations cross a cap PIPPA can never cross. The clip-as-boundary reading is dead, killed by the test that could have confirmed it. Corrects this document's own earlier read, which compared the tuned MEAN (88.5) to PIPPA's MEDIAN (67) and concluded 'comfortably inside the upper body'. Median to median it is 62 against 67. Mixing statistics across a comparison produced a more reassuring answer than the data supports. What the data shows instead is bimodality -- a mode at 20-39, a trough, a second mode astride PIPPA's centre, a tail to 505, against a base with no such shape. The tune changed rp length's SHAPE rather than its centre: roots whose length distributions do not overlap learned as distinct modes rather than blended into an average. And the skew is rp-ONLY, which localises it to the family the clipped root lives in and is the strongest support the turn-share mechanism gets from the output side. Consequence for pair generation: chosen/rejected sampled from a bimodal generator inherit the mixture, not a mean, and naive sampling over-draws the short mode. Also records that the degeneracy rate is NOT yet a usable baseline -- same arm, same seed, VOID flipped no->YES across a re-run because the 10% budget sits at the noise boundary. A guard whose trip point is at the noise floor produces disagreement between honest observers rather than silence. Replicates running. |
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1e4d827c5d |
memory: erp-tune-v2 registered in the LiteLLM gateway at operator request
Operator asked for it so he can evaluate the failed tune by hand, overriding my not-in-the-gateway recommendation. His call. erp-tune-v1 was DELETED from the config in the same reload rather than repointed, so the name now 400s cleanly instead of 500ing against a stopped backend. Deleting rather than repointing is the point: repointing would resolve a name a consumer already knows to different weights, silently. The config entry carries the failed-gate table, the long-form truncation (9.9%) and degeneracy (4.9%) rates, and the rp-length caveat in-line -- so someone reading the gateway config learns what they are calling without having to find the althing thread. Fleet verified healthy after the restart. |
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b5bbc29b91 |
memory: gate verdict FAIL — and the T6/T3 trade is what the pair of runs bought
Records the verdict as a FAIL without rounding it off, and the three findings
worth more than the verdict:
- T6 spatial +15.0 where run 1 failed the same axis at -3.5, with the base
swap as the only intended variable. Neither run ships; together they price
what the abliteration was costing, which neither could answer alone.
- an output-stability regression visible ONLY on long-form (truncated 0->38,
degenerate 0->19 per 384) that the reasoning battery could not see across
four passes because its answers are short
- PIPPA's 123-word product clip sitting in the length signal at 70.3% of bot
TURNS against 37.5% of bot WORDS, with the counter-evidence recorded too
(the tune landed near the median, not the cap)
Also records why keeping the tune out of the LiteLLM gateway now reads as
clearly right rather than merely cautious: a FAILED tune must not be one alias
resolution away from a consumer who has not read the thread.
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5171f19e16 |
docs(erp-dpo): the PIPPA length clip, measured — DPO pairs would inherit it
The run-2 gate found tuned rp turns 36% shorter than base. brokkr hypothesised the mix was teaching PIPPA's 2023 Character.AI product clip; the corpus side is now measured and confirmed. PIPPA's max is 123 words EXACTLY, 100% at or under it, and 0.00% in the 124-130 band -- a wall, not a preference. Every other root crosses its own p99 smoothly. The mechanism is sharper than 'PIPPA is in the mix'. PIPPA is 70.3% of bot TURNS but only 37.5% of bot WORDS, precisely because its turns are clipped -- and length is learned per turn, not per token. By loss tokens it looks like a third of the dialogue signal; by end-of-turn demonstrations it is seven in ten from a source that cannot exceed 123 words. Generalises: a length-clipped root is over-represented in the length signal by exactly the ratio its clipping creates. Counter-evidence recorded too: the tune landed near PIPPA's MEDIAN (67), not its CAP, which is central tendency rather than learning the boundary. Weaker claim than the hypothesis, and not demonstrated either way. Filed here rather than only in the gate record because preference pairs generated FROM this tune inherit its length distribution in both chosen and rejected -- DPO would train an artifact in as an explicit objective. Settle the length question before generating pairs. |
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0bb9ee7777 |
docs(training-playbook): 4.6.3 — a short-answer gate cannot see a long-form defect
Run 2's reasoning battery reported zero truncations and zero degenerates on both arms across four passes. The same tune, measured on long-form generation in the same session: truncated 0/384 -> 38/384, degenerate 0/384 -> 19/384. A real output-stability regression, structurally invisible to that gate because its answers are short. Not a bug in the battery -- a coverage property. An instrument measures the regime it samples, and output length is a regime. Generalises to context length, conversation depth, and any axis where the gate's operating point is narrower than production's. The actionable form: enumerate the regimes your gate set spans, name the ones it does not, and decide deliberately rather than discovering the gap downstream. Corollary on sequencing -- put a long-form generation in the gate and put it early, because a length-dependent regression is exactly the one you want found before four clean short-task passes make everyone comfortable. |
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3ae32ddc7f |
memory: base set complete, tuned arm live with digests verified identical
Records the floors the tuned deltas have to clear, since they are the whole
point of the base pass and are not recoverable from anywhere else: reasoning
core 0.5 pt, diversity overall 0.0125, story attractor 0.0000.
Two caveats that would otherwise be misread:
- the rp family froze ZERO markers, so its attractor hit rate is structurally
0.0 on both arms. That reads as a clean result and means the instrument
cannot discriminate on that family; rp is measured on the distance axis
only.
- 'Elias' in 92/96 base stories is an independent replication of a published
102/144 on the same family, at a higher rate -- not a novel finding.
Image digest sha256:4091d5593f77 verified identical across both arms, which was
brokkr's stated void condition.
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a0f59d2778 |
docs(training-playbook): 4.6.2 — a null result needs a positive control
From the run-2 gate. A memorisation probe reporting 0.00% across all 72 items is the correct output for a model that has not seen the corpus, and is also the exact output of a probe that is not firing. Nothing in the number distinguishes them. brokkr-smithy-dev drove the overlap function with known-answer inputs (identical 100%, half-verbatim 65.38%, unrelated 0%, empty 0%) before trusting the null, which is what converts a suspicious zero into evidence. This is 4.5's inert gate wearing a different face: there a check that could not return 'fail', here a measurement that cannot return non-zero. A clean null is the most reassuring output an instrument produces and the least self-evidencing. Same section records the identical-on-both-arms variant: the diversity battery's rp family froze zero markers, so its attractor hit rate read 0.0 on base AND tuned. That reads as a clean result and means the instrument cannot discriminate on that family. Report as a bounded limitation, never as a delta of zero -- a check returning the same value for every input is not measuring. Checklist gains the line. |
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3df8707e28 |
memory: base arm live, tuned arm down — battery running sequentially
brokkr withdrew the both-arms-concurrent requirement himself: his diversity battery emits the frozen marker list to a FILE, so the arms were never a live dependency. The real constraint is narrower -- all of one arm's passes on one served instance before the swap -- and sequential satisfies it. No fleet seats displaced, operator not woken. Records the two parity guards, both of which came out of failures rather than foresight: the image is pinned by DIGEST (a vLLM version change between arms six hours apart is a base swap that appears in no config diff), and /tank/aimodels is mounted for BOTH arms even though only the base needs it, because a mount that differs between arms is a difference between arms. |
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62f01a02da |
memory: snapshot — run 2 trained, merged, coherence-gated and serving as erp-tune-v2
Rewrites the in-flight section: run 1's seat is down, run 2 is up on :8098, and
the base decision the previous snapshot recorded as OPEN is resolved (stock
instruct, operator 2026-08-25).
Four new decisions, and the detail file carries the arc: the two operator calls
that produced run 2, all five gates, the harness commit chain, and the caveat
that its own provenance names a commit AHEAD of the code that ran.
Records three things a future session would otherwise get wrong:
- the mask is proven by the loss-token delta, NOT by the matching p50 step
times -- step time is insensitive to which positions carry loss, so that
check cannot go red on the axis I originally cited it for
- two bf16 26B arms do not fit on one 97.9 GB card (98 GB of weights before
any KV cache), so brokkr's both-arms-in-one-window requirement is a GPU
resourcing call, not a scheduling one
- erp-tune-v1 is still registered in the gateway and returns HTTP 500; the
fix needs a config edit plus a reload that interrupts fleet traffic, so it
is batched for morning rather than done at 2am
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d54f25605f |
docs(training-playbook): 4.4.1 sample identity at launch; 4.6.1 calibrate gates against correct input
4.4.1 -- the dirty-tree case was only half of the harness_commit problem. Run 2 launched CLEAN at 1909d86 and recorded 460f372, because three commits landed on the same checkout during its seven hours and _git_commit() was called at save time. Commit AHEAD of the code that ran, naming changes it never executed -- including the provenance fields this section prompted. Same defect as run 1's BEHIND, opposite sign: the identity was sampled at the wrong moment. Sample at launch, carry it, and record the dirty flag beside the commit rather than instead of it. Generalises to every run-scoped identity: anything read at save time describes the world at save time. 4.6.1 -- the inverse of the inert gate, and it costs trust rather than correctness. A coherence gate false-rejected 'The capital of Portugal is Lisbon' as degenerate against a global 15-word floor. The floor was calibrated against the wrong reference, not set too strict. Lowering it globally would blunt the check where short output genuinely is degeneration; the fix is a floor per prompt. Write the positive test alongside the negative one. |
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bcf63db527 |
docs(erp-dpo): readiness survey for the DPO stage
Run 2 is an SFT on the official instruct base, so it will refuse at near-stock rates by design; targeted DPO is where refusals get pruned on chosen axes. That was the trade accepted when the stock base was picked over a third-party abliteration. Surveys what is on disk against what the stage needs. Ready: the merged tune, the SFT adapter, GPU0 once the eval seat comes down, the whole non-loss half of the SFT harness, two unvetted Gutenberg preference sets, and the LitBench-RM judge. Missing, in order of pain: preference data for the refusal axes (nothing on disk targets it -- the Gutenberg sets are prose-quality), the axis list itself, and a DPO trainer (trl is not installed). The gating item is not technical: WHICH refusal axes are in scope and which are explicitly kept. Data generation, pair counts, the held-out split and the success probe are all functions of that list, so nobody should generate a pair before it is written down. Flags that the domain-compliance probe should measure run 2 BEFORE pruning, since the pre-number is the only baseline that will ever exist. Also records the operational trap: do the trl install AFTER a run finishes, never during one -- a resolution that upgrades transformers under a live process can break its save path. |
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dbca9a3c66 |
docs(training-playbook): audit the whole manifest against the pairing rule
§4.3's generalisation was stated and then not applied to the manifest that prompted it. brokkr-smithy-dev did the audit: most fields are intent-only, and the one pairing that would have caught the §4.1 cache failure -- the mask's sha against the loss-token delta -- existed by accident, because someone had asked for an encode report for unrelated reasons. Adds the audit table, and the rider that matters more than the table: put the observed check where it can actually FAIL. chat_template_sha256's pair is the sha of the string the tokenizer carries, but asserting that in the parent one line after assigning the file to the tokenizer compares a value to itself. It belongs in the encode worker -- a different process, across a pickle boundary, where an unset config key silently leaves every worker rendering through the checkpoint's own template. |
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c1db188e6a |
docs(training-playbook): §4.3 records an OBSERVED consequence, not just a config string
brokkr-smithy-dev pointed §4.5's own test at §4.3's remedy: recording `attn_implementation_resolved` is a check that cannot fail on the axis the failure lives on. A silent Dynamo fallback to uncompiled flex leaves `config._attn_implementation == "flex_attention"` untouched while the run computes at ~20x the cost and, per torch's own docs, does not work correctly through the backward pass. The field records the request's RESOLUTION, not its SURVIVAL. On the failure mode that matters it reports success either way. So the section now requires the step-time distribution beside it -- n, min, p50, p99, max -- which is the check that can actually fail. Compiled sits at p50 ~20 s; a fallback at ~400 s. One perf_counter() in on_step_end buys it. Distribution rather than a mean, because a mean hides exactly the bimodality a PARTIAL fallback produces. Generalised past this instance: any provenance field recording a CONFIGURED value is a claim about intent. If the failure you fear is the configuration silently not taking effect, you need a second field recording an OBSERVED consequence, and the pairing is the check. A settings dump alone is decorative. Two implementation details are called out because both were wrong in the first draft -- percentiles nearest-rank so every reported value is a real observation, and exclude the FIRST step rather than the slowest, since step 1 carries compilation but is not reliably the maximum on a variable-width run. New §4.7.1: rotate the log on relaunch. Run 2's first attempt died on the warmup_ratio TypeError and the relaunch appended, so the traceback sat at line 15 of a file whose live run began at line 39 -- and a `tail -n +1 -F` monitor replayed the dead traceback as a fresh event. One file describes one run. Checklist gains both lines. |
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dae6ede8e2 |
docs(training-playbook): §4 — when the artifact lies about itself
The playbook covered why a run is SLOW. It did not cover the more expensive
failure: a run that COMPLETES, reports plausible numbers, and is wrong about
itself. Seven of those turned up on the Gemma-4 ERP/RP tune between 08-24 and
08-26 and not one raised an error.
New §4, seven landmines plus a pre-launch checklist:
4.1 a cache key must cover the MEANING of the cached thing. The encode
cache missed the impersonation mask; run 2 would have reused run 1's
unmasked encodings and written impersonation_mask_sha256 into its own
manifest while doing it. No error, no count change, normal loss curve.
4.2 validating a VALUE is not validating the PARAMETER. warmup_ratio was
in range and deleted from transformers 5. Build kwargs as data and
diff the NAMES against the installed signature -- you cannot check the
argument list of a call you have already made.
4.3 record what the run RESOLVED to, never what it requested. Run 1
recorded no attention backend, so an MFU panel profiled the serving
seat under sdpa and recommended adopting flex_attention for a run that
was already using it.
4.4 never train from a dirty tree; harness_commit will name a commit that
does not describe the run. Annotate afterwards, never edit the shipped
artifact -- and state what is NOT wrong, or the note casts doubt on
every field it omits.
4.5 a watchdog whose pgrep pattern appears in its own argv can only ever
return "alive". The inert-gate shape in a liveness check.
4.6 an instrument nobody runs is not an instrument. Mutation-check any
test guarding a property that fails silently.
4.7 fix a stale measurement at the source. "~4.3 HOURS to rebuild the
encode cache" (really 145.5 s) was copied into a new launcher by the
same person who had just measured the real number.
4.8 the pre-launch honesty checklist, ten minutes.
Also:
- Header and framing widened. The file is now a training playbook with a
throughput half and an integrity half; the filename stays for inbound links.
- Sections 4-7 renumbered to 5-8. External refs are all to §1.1 and §3.4 and
are unaffected.
- Four rows added to the superseded-claims table, including the kernel table /
68% quadratic / 8.6% MFU set, which describe the serving seat rather than
the training run.
- gemma4-erp-tune-sizing.md §6 carries a correction banner with the explicit
falls/survives split, because that is the doc someone actually reads before
a run.
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2656196f47 |
memory: snapshot — the tune is trained, gated, and serving
Run-01 completed in 7:21:52 (47% faster than the 13.85h round-1 projection),
lora_B gate 205/205 non-zero at median norm 1.708, and the acceptance gate says
it did the thing it was built for: diversity +0.178 against a 0.008 floor (22x),
attractor hit rate -11.3pt against a 2.0pt floor, memorisation 0.0000 on both
arms — which closes the R20 licensed-prose exposure on measurement rather than
argument.
Five new detail files carry the substance:
erp-tune-run2-complete the run, the gate, the noise-floor near-miss
(brokkr was one step from reporting a 13-point
T6 regression sitting inside twice his
instrument's own variance)
mfu-root-caused-attention 8.6% MFU was an accounting artifact; real
utilisation 17-20%, cost was attention on
AMPERE kernels. Two independent methods agreed
to 2.6 points.
nvfp4-serving-pipeline merged weights are MANDATORY — vLLM cannot
serve a LoRA on ANY Gemma-4 — plus the recipe
that silently misses all 11,520 expert tensors
refusal-retention-probe measured base 0/100 -> tuned 29/100, then had
to accept it was the wrong axis
worldtree-b188-b189-and-selene three arcs closed, and a #411 diagnosis I got
wrong twice before a directory probe settled it
Current state rewritten end to end — the previous snapshot had the run in
flight at ~17h with MFU unexplained. Both are now closed.
The open operator decision is run 2's base, deliberately unstaged and flagged
against being filed as a config knob: it is a reversal of the trainee-selection
decision, and the pretrained-base option removes the last non-lexical floor on
the CSAM axis given stage-2-detector-inert and contamination-scan-absent are
both already overridden.
Tried-and-abandoned gains four measured-dead throughput levers, the packing
correction (bucketing wins under sdpa and the conclusion flips under flex — do
not carry it past the backend decision), and the merge-back-undoes-abliteration
trap brokkr caught in his own advice.
Index stays at 291 lines, under the soft cap. No archival this run.
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2a05ae91af |
feat(training-probes): counted-not-surfaced classifier scaffold
Reusable measurement discipline for probes that must classify how a model
responds to material that should not be printed, logged, or pasted into a
report. Supplies the discipline; the axis map and prompts stay with the caller.
Four rules, each because skipping it produced a wrong number:
- classify, never surface. Completion text is held inside classify() and does
not cross the return boundary. A probe that prints what it measured has
turned a measurement into a distribution channel.
- three-way, not binary. A refusal regex undercounts — models decline by
redirecting with no refusal token present, measured at 2/5 to 5/5 on models
a regex scored 0.
- the deflection count is a FREE CONTROL. Run both arms: zero on both means
the model is binary and the regex is sound; only one means the difference is
real. An artifact does not care which arm it runs against.
- EMPTY and ERROR get their own buckets. Folding them into either side biases
the result, and a truncation-heavy arm flatters itself if its failures land
in the wrong bucket.
Requested by brokkr-smithy-dev for the domain-compliance probe — the discipline
in code rather than reimplemented, with the axis map his side of the line.
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64bf9d313f |
docs(training-playbook): measure refusal retention on the abliteration's OWN axis
§3.13, plus the probe that produced it. Two lessons, both about measuring the wrong thing confidently. First: a tune applied AFTER an abliteration can walk it back, and a reasoning/craft/memorisation gate cannot see that. brokkr-smithy-dev's preregistered gate measured none of it — a tune that gains 41 items of contradiction detection and quietly restores refusals passes every check. The compliance axis has to be added explicitly. Second, and this is the trap: measure the axis the abliteration was actually FOR. Ours was run so the model engages explicit fiction. The probe reached for mlabonne/harmful_behaviors — weapons, malware, fraud — because it was cached and carried a recorded baseline. Different refusal surface entirely, and a model moves on them independently. 29/100 general-harm refusals on a tune whose prose the operator was praising at the time is not obviously a defect and may be desirable: general-harm refusals returning while domain compliance holds is close to the ideal shape for an internal creative seat. The measurement was real; its relevance was assumed. Also recorded, because both were nearly missed: - Read the interesting cell. In 29 hard / 0 deflect / 71 comply, the load-bearing number is 71. Stock refused 100/100; near that would mean the abliteration was undone. 71 complying means partially walked back on one axis — a different finding, and only one of the two threatens the seat. - A baseline from a different harness is not a baseline. The recorded 3/100 came from the abliteration tool's scorer, which reads first-token probability distributions; a probe that generates and regexes is a different instrument. Run your own against both arms on the same seat or report the number alone. - A refusal regex undercounts, so classify hard/deflect/comply — and the free discriminator: if both arms return zero deflections the model is binary; if only one does, the regex is fine. An artifact does not care which arm it runs against. |
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a696b49e2a |
docs(training-playbook): merging a tune back toward stock can UNDO an abliteration
§3.12. brokkr-smithy-dev caught and retracted his own recommendation mid-thread; recording it before it reads back later as advice. A common remedy for an overfit tune is a partial merge back toward the base to recover general capability. The published recipes that recommend it merge into the STOCK instruct checkpoint. On an abliterated base, following that literally re-introduces the exact refusal directions the abliteration was run to remove — and it is silent, because the merged model looks healthier on general benchmarks while the property the seat exists for quietly returns. Rule: any merge-back targets the SAME base the LoRA was trained against, never the upstream stock weights however similar the name. The wider lesson is about recipe-card provenance. Community cards are per-checkpoint artifacts and do not transfer across dense-vs-MoE, stock-vs-abliterated, or size variants. The worked example: a recommendation carried from a card for a DENSE STOCK 31B onto a MoE ABLITERATED 26B-A4B on the strength of a shared family name. The overfitting warning on that card happened to come from the right architecture; the pipeline, reward stacks and merge-back came from the wrong one. Same family, three axes apart. So: before quoting a recipe card at a decision, state which checkpoint it was written for and which axes differ. "Same family" is not an answer. |
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2ec8f42297 |
docs(training-playbook): base-viability pre-flight, three greps before you pick
§3.11. Three consecutive "what about X as a base?" questions in one session,
each answerable in minutes, none of which had been asked before a 7-hour
training window was committed. Writing the check down so it runs first.
1. does it fit for TRAINING - BF16 weights against the real measured peak,
not the weight figure (Gemma-4 is 48.1 GiB of weights and peaks at
79.7 GiB at mb2/seq-16k). Model-line names lie: "Mistral Small 4" is 119 B,
238 GB in BF16, more than both cards combined. QLoRA is not an escape
hatch for MoE - bitsandbytes walks nn.Linear and fused 3-D experts are not
that.
2. if MoE - does the serving engine implement get_expert_mapping. Zero means
LoRA cannot be served at all. gemma4*.py -> 0; deepseek_v2, mixtral,
glm4_moe, ernie45_moe -> present.
3. does the model class support LoRA - and GREP THE CLASS, NOT THE FILE.
Point 3 has teeth and I nearly got it wrong twice in one turn. mistral.py greps
as SupportsLoRA=0 and is fully LoRA-capable via LlamaForCausalLM.
mistral_large_3.py greps as 0 for both and inherits get_expert_mapping from
DeepseekV3ForCausalLM. Capability is inherited; a file-level grep misses it and
only MRO resolution answers it. Same class of error as asserting a substring
instead of an effective value.
Worked results recorded for the three candidates evaluated:
Gemma-4 26B-A4B fits, no expert mapping -> trainable, MERGE-ONLY
Mistral Small 4 119B 238 GB, has mapping -> servable, NOT trainable here
Ministral 3 14B ~28 GB, dense, inherited -> passes all three
Adds a fourth glance at architecture shape, since it predicts how much of this
playbook applies at all: uniform head_dim <= 128 with no sliding window keeps
both flash and cuDNN reachable and makes §3.1/§3.3 moot, while mixed head dims
plus a sliding window is exactly what forces dense O(n^2) attention onto
Ampere-generation kernels for 65% of the step.
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96731bb090 |
docs(training-playbook): prove the serving path before spending the window
§3.10. The quantization playbook already says prove your targets before spending GPU time; this is the same rule one step later, and easier to skip. A ~7h LoRA run was built assuming the adapter could be hot-swapped onto a quantized base at serve time. The sizing doc flagged serving as unsettled and said the requirement was needed "while he is early, not after the run" — the concern was identified correctly and then the check was deferred. Tested afterwards, vLLM refuses outright: gemma4's model class implements zero occurrences of get_expert_mapping, which process_packed_modules_mapping requires for any MoE model. One grep, available months earlier. Two generalisations recorded: - Feature support is per-architecture, not per-family. LoRA works for the DENSE sibling of this same model family and not the MoE one, so "model X is supported" says nothing about X's variants. - A capability gap in the serving engine cannot be worked around from the training side. The adapter here never touched experts and was refused anyway, because the refusal keys on the model being MoE, not on what the adapter targets. Includes the mechanical check: grep the engine's model class for the capability, then start the engine with the feature flag alone — no adapter required, since --enable-lora forces the machinery to initialise and that is where it fails. The recovery is cheap here (merge, ~35 min per tune). The cost of finding out late is that it forecloses an architecture choice after the training window has already been spent. |
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8de5f7a73c |
docs(gemma4-erp-tune): merged weights are mandatory — vLLM cannot LoRA any Gemma-4
The §5 open question was whether LoRA-on-NVFP4 hot-swap still silently no-ops as it did on vLLM 0.24.0 (#47639), with merged weights as the fallback if it did. Retested on vllm/vllm-openai:latest against the NVFP4A16 base plus the live run's checkpoint adapter. It does not no-op. It refuses to start: AttributeError: To support LoRA for MoE model, 'get_expert_mapping' must be implemented And the reason is bigger than the quant. The check is in vllm/lora/utils.py::process_packed_modules_mapping and branches on whether the model is MoE — quantization is not in the condition. gemma4.py, gemma4_mm.py, gemma4_mtp.py and gemma4_unified.py contain zero occurrences of get_expert_mapping, while deepseek_v2, glm4_moe and ernie45_moe do implement it. So vLLM cannot serve a LoRA on Gemma-4 at all, BF16 or quantized. Merging is not a workaround for a quantization limitation; it is the only path for this architecture. This holds even though the adapter never touches experts — validate_adapter_parameters forbids per-expert params, so all 205 targets are attention and dense MLP. The refusal is about the model being MoE, not about what the adapter targets. Worth recording that the current behaviour is an improvement: a loud refusal beats the 0.24.0 silent no-op, which would ship a base model wearing the tune's name and pass every check that does not compare against base. |
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ab980e9345 |
fix(erp-tune-serve): four defects the end-to-end dry run found, all silent
Validated the full adapter -> merge -> NVFP4A16 -> serve pipeline against
checkpoint-100 of the live run. It works, and it produced a served model
generating coherent prose. Getting there surfaced four failures, none of which
announced itself as the thing it actually was.
1. transformers 5.15 MIGRATES the config schema on save. It drops Gemma-4's
`global_head_dim` / `num_global_key_value_heads` and writes `per_layer_config`
instead. transformers 5.10 (what the llmcompressor venv pins) does not know
the new key and resolves num_key_value_heads to None:
TypeError: unsupported operand type(s) for //: 'int' and 'NoneType'
Every working artifact on the box - bf16 base, served nvfp4 prod seat,
nvfp4a16 build - uses the OLD schema. Merging changes weights, not
architecture, so the merge now downgrades the schema and asserts the result.
2. llmcompressor cannot auto-init a processor for a multimodal checkpoint and
dies with a message that names neither the model nor the cause. Calibration
here is text-only, so the tokenizer is passed explicitly as `processor`.
3. save_pretrained writes tokenizer files only, so `processor_config.json` was
never carried. vLLM then fails at startup with "Can't load feature extractor",
which reads as a vision bug and is actually a missing-file bug. Both scripts
now carry the base's auxiliary configs.
4. The quant needs more than the 32 GiB free on GPU1 alongside the resident
seats. Rather than leave that to a caller, quant_with_gen_down.sh stops
vllm-gen and restores it from a trap on EVERY exit path - crash, OOM, kill,
or success - because the restore must not depend on the calling session
surviving. Uses `docker start`, not `compose up`, so the container comes back
with its exact original config. Measured window: ~15 min, gen healthy after.
Verified on the resulting artifact:
merge 410 adapter tensors, sampled target weights confirmed CHANGED,
upstream 390-line chat template shipped (not the base's stale 365)
quant 49 GB -> 17 GB, format nvfp4-pack-quantized, a=null (genuine A16),
weight_packed 11,725 of which 11,520 expert = 30 x 128 x 3,
tokenizer truncation clean
serve Marlin NVFP4 kernel + Marlin MoE backend, 40,492-token KV cache,
coherent generation with content correctly populated
One quality note: the reference nvfp4a16 artifact triggers a vLLM warning that
parallel layers (q/k/v) carry different weight global scales, "likely to result
in reduced accuracy". Our build does not - llmcompressor 0.12 links weight
observers across fused groups for a shared global_scale automatically. The
in-house quant is better than the downloaded one on that axis.
Separately: the lora_B inert-adapter gate PASSED on checkpoint-100 - 205/205
non-zero, median norm 0.829, zero vision_tower tensors. That check never ran in
round 1, and it is the only failure mode that stays invisible until the
acceptance gate reports base-identical numbers.
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6a8582936e |
feat(erp-tune): NVFP4A16 serving pipeline, and the MoE landmine it uncovered
Merge + quantize path for turning the Gemma-4 26B-A4B ERP/RP LoRA into a
servable NVFP4A16 seat, plus a playbook entry for the defect found while
validating it.
The landmine (playbook §3.15): a `targets=["Linear"]` NVFP4 recipe silently
misses every MoE expert on this architecture. Gemma-4 stores each layer's 128
experts as two fused 3-D nn.Parameter tensors, not nn.Linear modules, so the
recipe resolves 205 of 427 modules and ZERO experts — 22.84 B params, 88.5% of
the model, left in BF16 with no warning. This is the same blind spot that
killed QLoRA here via bitsandbytes; the tool changed, the checkpoint layout did
not.
before linearize_moe: 427 Linears, 205 targeted, experts 0
after linearize_moe: 11,947 Linears, 11,725 targeted, experts 11,520
(30 layers x 128 experts x 3 projections)
llmcompressor's linearize_moe unfuses them; no registration needed because
Gemma-4 satisfies FusedExpertsProtocol structurally. Caught by an §4.1 dry run
that asserts the expert count before any GPU spend, which is now the documented
requirement rather than an optional step.
Scheme is NVFP4A16, deviating from the playbook's mixed-W4A4 default on
measured grounds: brokkr-smithy-dev benched the W4A4 quant of this checkpoint
at 12% on contradiction detection with CoT off against gen's 81%, the signature
of 4-bit input activations on a reasoning-dense task, and W4A4 KLD degrades
2-4x past ~10k ctx on sm_120. This is a 16,384-ctx RP seat. Marlin's prefill
cost is accepted.
Two further silent-failure guards, both from prior hard-won lessons:
- the merged model ships the UPSTREAM chat template, not the trainee base's
stale 365-line one, because training rendered through upstream and the
mismatch would present as a tuning failure
- calibration reads the run's own encode cache rather than re-tokenizing, which
sidesteps §3.14 (a fast tokenizer mutated by truncation=True and persisted by
save_pretrained clamps every prompt forever)
Merge-then-quantize rather than LoRA hot-swap, since hot-swap onto NVFP4 was a
silent no-op on vLLM 0.24.0 (#47639). merge_lora.py asserts sampled target
weights actually changed, so an inert adapter cannot ship as a tune.
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7b5fd91d3c |
docs(gemma4-erp-tune): root-cause the 8.6% MFU — attention on Ampere kernels, 29.9% padding
Run-01 was killed at step 19 by operator instruction to root-cause before spending a ~13.9h window. Two independent methods now agree on where the step time went, and neither was the hypothesis the consult panel converged on. Scaling fit (3 points, 2 params, residuals <3ms over an 8x range): A = 6.87e-4 s/token, B = 8.85e-8 s/token^2 quadratic share 20.9% @ w=2048 -> 67.8% @ w=16384 No fixed term was needed, which refutes launch-bound outright. Profiler kernel table (device rows only): attention 22,835.8 ms 65.2% fmha_cutlass*_sm80 dense GEMM 2,774.0 ms 7.9% other 5,739.0 ms 16.4% The attention kernels are sm80 — Ampere-generation CUTLASS running on an sm_120 Blackwell card, with the forward on the gmem fallback tier. That is the mechanism behind 100% SM utilisation at 27 of 304 available TFLOPS. Correctness cleared separately: the sliding mask asserts at max 1024 allowed/row, so the 25 windowed layers were genuinely windowed. The same probe found that right-padding is what pins the 5 global layers to an explicit 4D mask and off the is_causal fast path — measured at 9.4% slower for 24% less loss work at fixed width. The largest available win is not the attention kernel. The corpus is 29.9% padding, and bucket-to-pair + shuffle-to-mix takes it to 0.0% for >=35.5% wall clock, no new dependency, unchanged peak memory. Bucket size turned out not to be a diversity knob — roots per accumulation window are flat across a 256x range, so the global micro-batch shuffle does that work alone and the bucket should be tight. Adds docs/pfi/training-throughput-playbook.md as the durable model-agnostic home (sibling to the quantization playbook), the four probes under scripts/training-probes/ with raw output kept for re-derivation, and a §6 to the sizing doc carrying the Gemma-4-specific numbers and round-2 restart parameters. Measured negatives recorded so they are not re-chased: grouped_mm (0.9% slower, and MoE is only 7.9% of the step), CUDA graphs / torch.compile over the expert loop (no fixed cost to amortise), liger fused CE (~1-3% lever), FA4 on sm_120. Round-1 state preserved: 609MB encode cache, order manifest, truncation report, resume script. No checkpoints — it died at step 19 and the first was due at 100, so the lora_B inert-adapter gate never ran and moves to the restart. |
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872c2c562f |
memory: the MFU hunt — two hypotheses measured and killed, consult dispatched
Records what has actually been ruled out rather than what is suspected. The hardware is fine: a plain dense GEMM at the same shape reaches 97.1% of the benchmarked 313.8 TFLOPS peak. The Python expert loop is not the cause, which was my hypothesis and I was confident in it. transformers' grouped_mm experts backend runs 0.9% SLOWER than eager with bit-identical output and identical peak memory, and torch 2.13 has the kernel available, so it is not falling back for lack of one. MoE is not the bottleneck at all. Isolated at real shapes the block runs at 26.5% of peak with 36% of its time in pure gather/scatter, and a dispatch-free bmm version would reach 80.9% — but the whole MoE contribution is only about 10% of a step. Making it free buys 7%. So roughly 90% of the time is unaccounted for. The leading untested hypothesis is that the five full_attention layers use global_head_dim 512, above FlashAttention-2's 256 cap, which would push SDPA onto a slow backend for O(n^2) attention at sequence 16384. Also records that the earlier 5% MFU figure was wrong in two ways — unpadded tokens and a guessed peak — and that the operator caught it. Padding is real but secondary at 29.9%. Consult dispatched to brokkr-smithy-dev for the frontier-dwarf panel. |
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07743c6aff |
memory: snapshot — tune training unattended, MFU root-caused to a Python expert loop
The in-flight section is rewritten around the run itself rather than the decisions that led to it. The sizing and seat-call bullet collapses to a pointer now that both are executed; its detail lives in docs/pfi/gemma4-erp-tune-sizing.md. Adds the measured MFU finding: 27.1 TFLOPS against a benchmarked 313.8 TFLOPS peak, root-caused by reading the source rather than inferring — transformers runs the Gemma-4 experts in a Python loop, 128 experts across 30 layers, roughly 11,500 iterations per optimizer step under gradient checkpointing. Padding is a secondary 29.9% tax. Records that my first estimate of 5% MFU was wrong in two compounding ways: divided by unpadded tokens, and compared against a guessed peak rather than a measured one. The operator pushed back on the number and was right to. The fused MoE kernel is deferred work with a tracking surface — park id 47 — per the snapshot rule that deferred decisions go in Recent decisions with a pointer, never into the volatile in-flight section. Also records the resume trap: the original launch command begins with rm -rf on the output directory, which would destroy both the encode cache and every checkpoint. resume-run-01.sh exists so that cannot happen. |
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d6dfd61c91 |
memory: the ERP tune is running — override granted, 12 defects fixed first
Operator overrode the corpus gate for one run on 2026-08-25, with the grant staged beside the recipe rather than asserted in chat. It deliberately does not flip any root's training_eligible flag, so the signal that made the run stop in the first place survives intact. Records where the run lives, what it is configured with, how to restore the fleet, and the two lessons that generalise past this project. The first is inert gates. Two turned up in one evening — auditcore, whose CSAM hard-drop never fired across 42,662 records, and validate_vision_keys, which compared model.state_dict() against itself and could not fail on any input. Both read as guards. The question that catches them is not whether the check passes but whether it can fail. The second is an invariant enforced on one code path and not its sibling. That was my own bug: INV-T9 requires a window to hold at least one complete assistant turn, and I enforced it where the window is cut but not where it fits, so a trailing user-only remainder became a zero-loss window and killed the first launch. Same shape as the inert gates, in code I wrote an hour earlier. Two further foot-guns worth the space: enable_input_require_grads is mandatory beside gradient checkpointing on a frozen base, or every adapter stays at its initialisation and the run completes successfully having learned nothing; and the upstream Gemma-4 template forward-scans to suppress a closing turn marker before another assistant message, so incremental rendering cannot tile against it and assistant runs must be merged first. |
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33433e0d1e |
docs(gemma4-erp-tune): replace the estimates with measurements — they were 3x optimistic
Ran the loss path on the real checkpoint on GPU0 with synthetic tokens.
The arithmetic held for parameter counts and was badly wrong for
activation memory.
naive CE bsz1 seq 8192 81.93 GiB
naive CE bsz1 seq16384 OOM
chunked CE bsz1 seq16384 65.66 GiB
chunked CE bsz2 seq16384 79.71 GiB <- the run config
chunked CE bsz4 seq16384 OOM
The marginal cost of an extra 16,384-token sequence is ~14 GiB, not the
~5 GiB estimated: the estimate modelled gradient checkpointing as
storing layer inputs plus a modest recompute peak, and the real MoE
recompute peak with top-8-of-128 routing and its scatter/gather buffers
is far heavier. Dense-model intuition does not size an MoE run.
Two predictions landed exactly — 205 target modules and 74,342,400
trainable params at r64 — which is why the rest of the model of the
thing is still worth trusting.
The headline is that chunked CE at seq 16384 costs 16 GiB less than
naive CE at seq 8192, so chunking is what makes brokkr's 16384
recommendation reachable rather than an optimisation on top of it.
max_seq_len moves 8192 -> 16384 on his truncation finding: the cap
drops 6.2% of samples but 22.4% of tokens, concentrated entirely in
dialogue, which is 60% of the mix.
Also records the four harness changes this required (eitri-smithy
62b556b), including the inert-adapter trap: without
enable_input_require_grads() alongside gradient checkpointing on a
frozen base, no gradient reaches the adapters, every one stays at its
initialisation, and the run completes successfully having learned
nothing.
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47ec3d1a97 |
memory: the ERP tune is blocked on a corpus gate only the operator can clear
Every clean-v1 CLEANROOT carries training_eligible: false with two named blockers, and the recipe states plainly that nothing in it is Charter §3 training-eligible. I initially read scoped_grant: operator-2026-08-22 as authorization and told brokkr-smithy-dev I was proceeding. That was wrong, and the person who wrote the field corrected it: the grant governs INV-4 one-way tier inheritance — the adapter is permanently internal-erp-rnd and never distributable — not training clearance. The stage-2 detector is measured-inert rather than merely unvalidated. auditcore v3.7.2 returned its hard-drop exit code zero times across 42,662 raw RP records, its printed verdict ignores its own printed threshold, and it passed a record a blind audit had already identified as sexual content involving a participant the text marks as a child. Verified the one thing that decides whether that specific record reaches training: pippa-5083 is present in kept-manifest.jsonl (4,551 rows) and absent from recipe-dedup-kept.jsonl (20,473 rows), which is the survivor list the harness gates on. The substitute lexical screen caught it. That is one known instance caught by a stopgap and says nothing about what the screen misses. Both brokkr and I recommend stopping. Neither blocker is hours of work. |
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c9943b1507 |
docs(gemma4-erp-tune): whole-card placement — gen moves to GPU1, sec stands down
Operator chose a third placement over the two the sizing offered: rather than train beside gen on GPU0 or on GPU1 in mog-sec's slot, move gen to GPU1 and empty GPU0 completely. The tune gets 95.60 GiB with no co-tenant and gen never goes dark beyond its own restart. Revised run parameters, since a whole card changes them: - micro-batch 8 (71.8 GiB of 95.60) rather than 4, grad-accum 1, giving 888 optimizer steps instead of 444. At one epoch the step count is worth having, and 8 x 8192 tokens puts ~4,096 rows through each expert per step against ~512 at micro-batch 1 — a far healthier GEMM on 704-wide experts. - Gradient checkpointing stays ON. Dropping it takes ~17% off wall-clock but pushes activations to ~24 GiB per sequence, which forces micro-batch 1 and costs 8x on MoE efficiency. Wide beats shallow. - Scriberr stays on GPU1. The previous revision suggested moving it to GPU0, which was correct only while training was going to live on GPU1. Records the ordering constraint in both directions, the elway identity requirement, and that sec's aliases should be allowed to fail at the gateway rather than be substituted with another model. |
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9d70100867 |
feat(ana-ml2): elway playbooks to open and close the ERP/RP tune window
Operator call: rather than train beside gen on GPU0, move gen to GPU1 and
stand sec down for the night, so the tune gets a whole 95.60 GiB card and
the fleet's general seat never goes dark beyond its own restart.
Order is load-bearing in both directions and the playbooks enforce it.
gen runs at --gpu-memory-utilization 0.43, which vLLM reads as a fraction
of TOTAL card memory: 42,091 MiB must be FREE at startup or the engine
refuses to boot. GPU1 has 19,446 MiB free while mog-sec is up, so
recreating gen onto GPU1 first would take the main seat down and leave it
down. mog-sec stops first and a hard gate checks the freed memory before
gen is touched. The close playbook mirrors it: gen must vacate GPU1
before mog-sec starts, since mog-sec needs 50,901 MiB of its own.
Close opens with a gate that refuses to run while a process is still
resident on GPU0, so it cannot evict a training run mid-flight.
Override with --var allow_busy_gpu0=true.
Three defects found and fixed while landing this, all worth keeping:
- Verifying GPU residency via `docker inspect --format {{.State.Pid}}`
never matches. vLLM V1 runs EngineCore as a child of the container's
pid 1, and it is the child that holds the memory and that nvidia-smi
reports. Match by cgroup instead.
- A step's `sudo: true` does not extend to its when/creates/changed_when
guards, which run as the login user. The root-only .env made an
unsudo'd grep exit 2, so the GPU-id flip SILENTLY SKIPPED. The
effective-value assert is what caught it.
- That assert originally grepped the config YAML for -\s*'?1'? and failed
against compose's double-quoted `- "1"`. Parse the JSON with jq; an
assert that fails for the wrong reason is worse than no assert.
elway must be invoked as infra-ops@10.250.50.54 rather than the ana-ml2
ssh-target, which resolves to lkraven and has no NOPASSWD sudo.
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c507db9ac0 |
docs(gemma4-erp-tune): size the run against the checkpoint — QLoRA is structurally unavailable
The proposed shape was QLoRA r64. It cannot be run as specified. The checkpoint stores each layer's 128 experts as two fused 3-D nn.Parameter tensors (experts.gate_up_proj [128,1408,2816], experts.down_proj [128,2816,704] — no .weight suffix, so they are parameters, not modules). bitsandbytes 4-bit replacement walks nn.Linear only, so 22.84B params / 42.54 GiB — 88.5% of the model — is skipped and stays BF16. load_in_4bit saves ~3.1 GiB of 48.07 and does not error while doing it. Verdict: plain LoRA on BF16, ~57.6 GiB at micro-batch 1, +2.5 GiB per additional 8192-token sequence. Two sizing items were absent from the brief and both are load-bearing: - vocab 262,144 x seq 8,192 = 2.147B logits, with final_logit_softcapping 30.0 adding a saved pre-cap tensor. Naive HF cross-entropy peaks at ~28-30 GiB transient at batch 1, which puts the run at ~85.6 GiB on a 95.6 GiB card — it starts, then OOMs on the first long sample. Fused or chunked linear CE is mandatory and must be smoke-proven before a window is booked, since Liger may not carry a Gemma-4 MoE patch. - v_proj does not exist on layers 5/11/17/23/29 (attention_k_eq_v on the full-attention layers). A v_proj target silently produces no adapter there, and k_proj adapts K and V simultaneously. 45.96M trainable at r64 across q/k/v/o. Placement, measured: GPU0 has 53.46 GiB free beside gen, ~4 GiB short, and gen's footprint grows with uptime. Stopping mog-sec frees 74.29 GiB on GPU1, which holds micro-batch 4 at 61.8 GiB with margin for Scriberr. Recommend standing down sec (2 aliases, last request ~5h ago) rather than gen (7 aliases, 765 busy-engine log lines in 24h). Estimated 1.28e18 FLOPs for the epoch at ~3.67B active params; 4-10 hours at 10-25% MFU. 7,104 packed sequences is only 444 optimizer steps at effective batch 16, which makes the wall-clock-checkpointing amendment concrete rather than hypothetical. Package as a uv venv on /tank: root is 91% full (36 GB) with /var/lib/docker on it. |
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9d0e628643 |
memory: correct the vLLM version claim — ana-ml2 runs a spread, and 0.27.1 is on disk
The snapshot recorded "ana-ml2 now runs vLLM 0.26.0". That is true of the char-rp seat's pin and false of the box, which the operator caught immediately. Measured per running container: gen is on nightly-311b3513 reporting 0.27.2rc1.dev150, mog-sec on nightly-e9d1398d reporting 0.26.1rc1.dev1102, and rerank-a3 / coder / reward / embed still on 0.24.0. char-rp and the trainee bench stack are pinned to v0.26.0. So there is no single "the version" for this host, and stating one invites exactly the wrong retest. The correction improves the LoRA question rather than complicating it: vllm/vllm-openai:v0.27.1 is already on disk and unused — a TAGGED release, not a nightly, roughly four months past the 0.24.0 where the silent-no-op was diagnosed. That is the right target for a decision test: no nightly variance, no pull. The retest instruction in both the decision entry and the handoff now names it. |
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668e590e7d |
memory: snapshot — char-rp on the Gemma-4 MoE, abliterated trainee staged, QLoRA sizing next
Captures an evening that ran from an OOM crash-loop to a measured trainee base. The durable lessons, none of which CLAUDE.md can carry: --gpu-memory-utilization sizes the KV cache and does not cover CUDA context or graphs, which is half of why a seat that fit on the 21st stopped fitting on the 24th; the other half is that gen's footprint GROWS WITH UPTIME (38.5 GiB fresh against 45.6 GiB after three days, same container, same flag), so headroom arithmetic against a long-running gen measures a moving number. The stale-chat-template trap turned out to be endemic across third-party Gemma-4 derivatives rather than a one-off, and it is silent in both directions — wrong prompt when serving, train/serve skew when tuning. And a benchmark finding was retracted because 12% on a five-option task is below the 20% chance floor: a below-chance score indicts the instrument before the model, and a preflight can be thorough while aimed in the wrong direction. Records the serving decision for the tuned model with its history intact: LoRA-on-NVFP4 is preferred if it works, merged weights the expected fallback, but the archived root-cause says the objection was never NVFP4-specific — vLLM 0.24.0's LoRA application was a silent no-op proven quant-agnostic, and ana-ml2 now runs 0.26.0. Retest before designing around merge; the answer changes what Eitri's harness must emit, and he is still early. Auto-archival moved 5 entries (Recent decisions) to archival-memory.md; the guards held back the rest of the 78 age-eligible candidates because their bodies carry open deferred-work language, per the keep-when-unsure rule. Index sits at 286 lines, above the ~250 target and reported rather than forced. |
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5415fd4b30 |
docs(gemma4-charrp): abliteration measured in isolation — close to free, but it MOVES capability
Second bench window, operator-authorised after an initial decline and reversal. Stock BF16 against the llmfan46 abliterated BF16: same precision, same pinned upstream template, same 192 items, CoT off. Abliteration was the only axis that moved, which is what the previous run could not claim. Net core cost is 0.6 points — but the headline understates what happened. Capability MOVED rather than degraded: five items lost on contradiction detection, four gained on spatial composition, nearly cancelling. A gain was not predicted by anyone, least of all on that axis. The decision this was authorised to settle: llmfan46 stands as the trainee base. No case for re-staging on TrevorJS at KL 0.09 over 0.6 points — the KL gap between the builds is smaller than the gap this measurement failed to find. Both limits recorded rather than buried, per brokkr-smithy-dev: the swings are ~5 and ~4 items at n=32, so the -15.6/+12.5 percentages read more precisely than the measurement supports and only marginals were run; and this says nothing about quantization, because the stock-NVFP4 T2 figure came from n=16 against n=32 here — different item counts mean different item sets, so that comparison is n-confounded and is not being made. Turnaround was five minutes rather than fifteen because the gemma4-trainee-bench stack already existed — itself the residue of debugging a 35-restart crash-loop caused by the production compose hardcoding --quantization compressed-tensors. The fix outlasted the incident. gen restored and verified through the gateway; char-rp remains down deliberately; bench stack env reset to the heretic base for the post-tune gate. |
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019ccff7e8 |
feat(gemma4-trainee-bench): BF16 bench stack; record that gen's footprint grows with uptime
Adds an ephemeral stack for serving the BF16 trainee base on :8016 under the char-rp aliases, so the abliterated base can be measured on the same battery and the same gateway routes as the served seat with no harness edit. It is a separate stack rather than another variable on gemma4-charrp because that compose hardcodes `--quantization compressed-tensors` for the NVFP4 build. Pointing it at unquantized BF16 weights crash-loops immediately — `TypeError: CompressedTensorsConfig.__init__() missing 3 required positional arguments: 'target_scheme_map', 'ignore', 'quant_format'` — vLLM trying to read a quantization config out of a checkpoint that has none. 35 restarts before it was caught. `restart: "no"` here so a bench seat cannot resurrect itself and block gen's restore, and no homepage labels so it leaves no permanently-offline dashboard card. It cannot coexist with gen and says so: 48.07 GiB of BF16 weights plus gen's footprint exceeds the 94.97 GiB card before any KV cache. Running it means gen is stopped. THE MORE USEFUL FINDING is in the meromero env note: gen's memory footprint GROWS WITH UPTIME. Measured today at 46,726 MiB (45.6 GiB) after ~3 days up, and 39,424 MiB (38.5 GiB) immediately after a restart — same container, same --gpu-memory-utilization 0.43, ~7 GiB apart. That is the missing half of this afternoon's crash-loop: the char-rp seat "fit on the 21st and stopped fitting on the 24th" because nothing about char-rp changed and gen crept up underneath it. Headroom arithmetic done against a long-running gen is measuring a moving number, so the note now says to measure against a freshly-restarted one. Operator's requested end state reached and verified through the gateway: gen and summarizer both 200, char-rp down deliberately to hold GPU0 headroom for the upcoming trainee run, bench seat stopped. |
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14ff4a3f57 |
docs(gemma4-charrp): stage two abliterated trainee bases; record the endemic stale-template trap
The operator directed that the ERP/RP trainee base be a low-damage abliterated instruct build rather than the stock checkpoint. Two are now staged under /tank/aimodels/, both BF16, both unquantized, both matching upstream's 51.61 GB / 25.8B shape with only transformers_version differing in config: gemma4-26b-a4b-it-heretic-bf16 llmfan46, Heretic v1.2.0 ARA, KL 0.1237, refusals 3/100 gemma4-26b-a4b-it-abliterated-bf16 TrevorJS, KL 0.09, 1/100 effective and 5/686 cross-dataset "Low damage" was treated as a measurable claim rather than a description: the field spreads from KL 0.09 to 0.4118 and the table is in the README so the next choice is made on numbers. Fleet anchor for reading them — our own abliteration work found Heretic at KL 0.12 preserved the MTP head at 83.7% acceptance, so both staged builds sit inside an already-validated band rather than past it. huihui-ai is rejected despite its reputation: no published metrics, its own card calls the method a crude proof-of-concept, it abliterates both thinking and non-thinking modes, and its parameter count runs ~738M over upstream. The operator's independent read matched. The more durable finding is the chat template. NOT ONE third-party Gemma-4 derivative pulled here ships upstream's — three independent repos carry the identical stale 266-line file (sha 58c66fdee4afa297), llmfan46 carries a third 365-line variant, and only the RedHatAI NVFP4 build matches upstream's 6a1015c47ccfcfa6. It propagated through the ecosystem rather than one packager slipping, and it is now recorded as a class rather than as the single incident that surfaced it during the A16 control staging. That matters twice over and silently both times: serving a mismatched template renders a different prompt, which is why production pins it; and training through `base/chat_template.jinja` means training on a different prompt format than production serves — train/serve skew with no error, presenting as a tuning failure. brokkr-smithy-dev has been warned on the training side while the harness contract is still early enough to amend. |
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8d6a9390de |
docs(gemma4-charrp): RETRACT the contradiction-deficit claim — the item was ill-posed
Supersedes what commit
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3446367d5e |
feat(gemma4-charrp): pin the chat template; A16 control run executed and reverted
The template is now passed explicitly, defaulting to the A4 build's chat_template.jinja. That is a no-op for what is served — the A4 build ships that exact file, byte-identical to upstream google/gemma-4-26B-A4B-it once trailing newlines are normalised — and it permanently closes the class of bug found while staging the control: the A16 build ships a stale 266-line template against upstream's 390, with the thinking path built differently and no `thinking` property in its tokenizer_config response_schema. Serving each build with its own template would have moved a second axis. The control ran on the operator's greenlight and has been reverted. Seat is back on the W4A4 build, healthy, RestartCount 0, both aliases verified through the gateway — char-rp returns content with reasoning_content empty, char-rp-reasoning returns both. Result, since it settles a question this repo's config now encodes: activation precision does NOT explain the contradiction-detection deficit. Contradiction detection moved 12% -> 19% between W4A4 and W4A16, which at n=16 is 2/16 -> 3/16 — one item — against gen's 81% on identical items. Every other task is identical across the two builds and the core difference is 2.6 points carried almost entirely by two single items. brokkr-smithy-dev pre-registered that a null result would be the robust branch, because a hidden third axis would tend to create a delta rather than suppress one, so the conclusion survives the residual doubt neither side could close without a dequantization pass. The practical upshot for future scheme choices: W4A4 costs less on this workload than the caution warranted. The caution was still correct to have. Displaced production for 3.7 seconds of measurement plus two container recreates. The A16 build and the BF16 tuning base both stay on disk with the runbook in the stack README, so re-running is a two-minute flip. |
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1bd90eaacc |
docs(gemma4-charrp): stack README — the three model dirs, the A16 control runbook
The stack had no README and now carries three model directories that look interchangeable and are not: the BF16 QLoRA base that cannot be served here, the W4A4 quant that is served, and the W4A16 build that exists solely as an activation-axis control. Writing down which is which, and why, before someone "simplifies" the compose to the BF16 path and rediscovers the OOM. Also captures the A16 control procedure end to end, including the two confounds found while staging it — the two Hub repos named NVFP4A16 that declare 4-bit activations, and the stale chat template the real one ships — and the fact that overriding the template is safe because the tokenizers are identical. Both sides have now cross-checked this: brokkr independently diffed every non-quantization config field of both builds against the upstream BF16 and found only transformers_version differing. Residual risk recorded rather than hidden: config identity is not weight identity and nobody has done a dequantization pass. The Gemma-4 flags are documented as architecture-level rather than checkpoint-level, since that is why they survived the seat swap unchanged, and the enable_thinking:false pin is called out as mandatory rather than stylistic — without it every plain prose turn lands in reasoning_content with a null content and every consumer breaks. Notes the non-termination defect with thinking on (32 of 96 calls truncating at 12k tokens, all 16 constraint items among them, reasoning sound right up to the point it fails to stop) and why VLLM_USE_V2_MODEL_RUNNER=0 is deliberately not applied to a seat whose production mode is thinking-off. No live change: the seat is still serving the W4A4 build. Displacing it for the control run is an operator decision and is still open. |
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24e8826219 |
docs(gemma4-charrp): the A16 control needs a chat-template override, not just a path swap
Pre-flighting the staged A16 build before handing it to brokkr-smithy-dev's
battery found a second axis hiding inside what was supposed to be a
single-variable control.
The A16 build ships a STALE chat template. Verified by hash against the upstream
weights on the same disk: google/gemma-4-26B-A4B-it is 390 lines, the RedHatAI
A4 build's is 389 and byte-identical to upstream once trailing newlines are
normalised, and the prithivMLmods A16 build's is 266 and is not. The delta is
not cosmetic — upstream and A4 open the thinking path with
`{%- set enable_thinking = enable_thinking | default(false) -%}` and branch off
it, while the A16 template has no such set and guards with
`enable_thinking is defined and enable_thinking` instead. tokenizer_config.json
corroborates: A4's response_schema carries a `thinking` property, A16's has only
role and content. That build was quantized from an older revision of the
checkpoint.
Served with its own template, the A16 arm would render a different prompt for
identical messages, and a contradiction-detection delta could be attributed to
activation precision when it was the template. That is the same failure class as
the misnamed-A16 repos — a field nobody validated, believed because the name
looked right — one layer further down, and it would have produced a result that
looked like a finding.
Overriding is safe because the tokenizers agree: vocab identical at 262,144
entries, added_tokens identical, so the same template over the same vocab
renders the same token ids. Everything else pre-flights clean — both artifacts
complete with no missing shards, generation_config.json byte-identical.
Seat NOT flipped; displacing production for the bench window is the operator's
call and is still open.
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f509668e45 |
docs(gemma4-charrp): record the A16 activation-axis control and how to run it
brokkr-smithy-dev's first battery on the new seat scored 12% on contradiction detection with CoT off, against gen's 81%, while state tracking, deterministic constraint following, long-context state and the confabulation control all sat at 100%. That is not general degradation — it is the shape 4-bit input activations produce on the most reasoning-dense task, which is exactly the confound flagged when the W4A4 quant was chosen. They have recorded the finding as CONFOUNDED rather than reporting it as a property of Gemma, and asked for an A16 build to separate the two. No quant run was needed: a genuine A16 build of the same checkpoint already exists on the Hub and is now at /tank/aimodels/gemma4-26b-a4b-it-nvfp4a16 (prithivMLmods, compressed-tensors, nvfp4-pack-quantized, input_activations null, 17 GB). Same weights, same loader as the live seat, one axis moved. ⚠ Two other repos would have answered the question wrongly and the note in the env template says so: bg-digitalservices and ManniX-ITA both publish "Gemma-4-26B-A4B-it-NVFP4A16" whose config declares input_activations num_bits 4 — W4A4 under an A16 name, via modelopt, both 16.46 GB against the real one's 17.93. Using either and seeing T2 stay at 12% would have wrongly convicted the model. Running it is a one-line GEMMA4_MODEL swap plus a recreate, because there is no room for a concurrent seat: GPU0 has 3.5 GiB free with gen and the A4 seat on it, and GPU1's 19.4 GiB against ~18 GiB of weights is the same sub-GiB headroom that crash-looped the predecessor this morning. Port and both aliases are unchanged either way, so no consumer config moves. |
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27155c0f3b |
feat(char-rp): swap the seat to the Gemma-4 26B-A4B MoE, NVFP4, same port
Straight-across replacement of the dense G4-MeroMero-v2-31B-NVFP4A16 seat with google/gemma-4-26B-A4B-it on ana-ml2 GPU0. Port, served-model-names and every gateway route are unchanged, so no consumer sees a difference in addressing: `char-rp` -> hosted_vllm/char-rp and `char-rp-reasoning` -> hosted_vllm/char-rp-thinking, both still :8016. The seat's requirements now include chain-of-thought, which makes throughput more critical rather than less — the user waits through the whole reasoning block before the first visible token, and the MoE measures ~114 tok/s @32K against the dense 31B's ~40.7. Both artifacts are on disk and they are NOT interchangeable. The BF16 weights (/tank/aimodels/gemma4-26b-a4b-it-bf16, 49 GB) are the QLoRA tuning base, since QLoRA does its own quantization. They CANNOT be served here: 48.10 GiB of weights against ~49 GiB of free GPU0 leaves nothing for KV cache, and the engine would die at allocation exactly the way the predecessor did this afternoon. The serving copy is RedHatAI/gemma-4-26B-A4B-it-NVFP4 (16 GB), chosen over the other -it quants because it is compressed-tensors (nvfp4-pack-quantized) — the same loader path the outgoing seat used — from the llm-compressor team at 357k downloads. The nvidia/ repo is the base rather than -it, and the thinking channel lives in the instruction-tuned weights. Smaller weights at the same 0.47 memory budget buy a much larger KV pool: 27.37 GiB and 1,724,110 tokens, against the predecessor's 371,023 at the same budget. That is 6.5 full-length 262K sequences concurrent rather than 1.4. The gemma4 tool-call parser, reasoning parser and the enable_thinking:false default all carry over unchanged — they are architecture-level, not checkpoint-level. The --chat-template override does NOT carry over: MeroMero pointed at a jinja hand-patched against that checkpoint, and this model ships its own. Verified that dropping it did not reintroduce the failure that flag existed to prevent — non-thinking prose lands in content with reasoning_content empty, and the thinking alias populates reasoning_content with content carrying the answer. ⚠ Scheme differs from the incumbent and the bench should say so: this quant declares 4-bit input activations (W4A4) where the outgoing seat was NVFP4A16. Faster, and not like-for-like on the activation axis. meromero-charrp is retained stopped in `created` state and relabelled to AI - Dormant, per the house rollback pattern. Both stacks want :8016, so rolling back means stopping the gemma4 seat first. |
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850e0c3351 |
fix(meromero-charrp): drop GPU0 budget to 0.47 — the seat was OOM crash-looping
`vllm-meromero-rp` had been restarting since 2026-08-24 18:2x, 13 times by the time it was looked at, taking both the `char-rp` and `char-rp-reasoning` gateway aliases down with it (they resolve to the same seat on :8016 — hosted_vllm/char-rp and hosted_vllm/char-rp-thinking). Root cause is CUDA OOM on ana-ml2 GPU0, which the startup logs hide well: the engine gets through weights, torch.compile and CUDA-graph capture looking entirely healthy, then dies at KV-cache allocation with `torch.OutOfMemoryError: ... 195.19 MiB is free`. GPU0 is shared with `vllm-gen`. gen is configured at 0.43 but actually holds ~45.6 GiB of the 94.97 GiB card, because --gpu-memory-utilization sizes the KV cache and does not account for CUDA context, graphs and non-torch overhead. This seat was at 0.51, so the pair was committed to 0.94 of the card with about 0.6 GiB of real headroom. That fit on 08-21 and stopped fitting today. 0.47 restores ~4.8 GiB of margin and costs nothing usable: KV cache 27.36 -> 23.56 GiB, 430,825 -> 371,023 tokens against a max-model-len of 262,144, so the pool still holds 1.4x a full-length sequence. What is lost is concurrent long requests, not context. Verified through the gateway rather than at the container: char-rp returns 200 with content, char-rp-reasoning returns 200 with both content and reasoning_content populated. Seat is healthy with RestartCount 0. The arithmetic and the "check used_memory, not the flag" warning are written into the env template, because the next person to raise either budget needs to lower the other in the same change. |
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35adc4a043 |
feat(homepage): rebuild on Australis Skyfall — dual theme, light mode shipped
The board was on the Australis TERMINAL palette, which is dark-only by design
("Always dark first. No light mode in this system"). Skyfall is the dual-theme
web derivative of the same science, and its bundle turned out to be sitting in
this repo's own git history: a predecessor vendored it on 2026-08-19 and a
later commit deleted it. `git show 45c1995:...` returns colors.css with both
`:root` (dark) and `[data-theme="light"]` (Skyfall Day) intact, plus the
calm-depth layout tokens, the typography scale and Supreme 400/500/700. So the
light ramp is canonical rather than derived, which was the entire objection to
building one.
The visual language moves with the palette. Depth is now the recipe and not a
choice — every elevated surface carries a 1px hairline AND a two-layer shadow,
never one without the other. Radii move to Skyfall's scale, cards at
--radius-lg. Widget stat values move from the display face to mono, because
Skyfall is explicit that numbers and telemetry are always --font-mono. The
full-width aurora ribbon under the tab bar is gone: Skyfall sanctions exactly
two accent expressions, the active rail and hero-only glows, and a decorative
gradient across the chrome is neither — so the colour it carried now lands on
the active tab as a 2px accent bar plus an --accent-soft fill, which is the
rail. Every binding is written against the semantic layer; there are no raw
family tokens and no colour literals left in our own file.
build.py now guards the vendoring instead of advising it. The three token files
are hashed and a mismatch FAILS the build — a vendored file is either
byte-identical to the bundle or it is a fork wearing the bundle's name, and the
theme this one replaces had to be torn out twice for exactly that.
⚠ Homepage's own theme toggle is unreachable, and reaching for it breaks the
dashboard. It renders only when settings.yaml leaves `theme:` unpinned, and
with the key absent the page's data loader throws and its catch branch serves
`initialSettings: {}` — no tab bar, no layout, no i18n. Six force-recreates
over seven minutes all came up empty; restoring `theme: dark` rendered
correctly on the next recreate in 12 seconds, while /api/services returned 200
with fully correct content the whole time. That is the first confirmed cause of
the long-running "tab bar goes missing after a recreate" symptom, and it also
retires the homepage.log-size lead recorded earlier today: rolling the log
aside did nothing during this episode, so that coincidence was intermittency.
So the toggle is ours. conf/custom.js renders it and stores the choice;
build.py re-emits each vendored light block twice, once for an explicit
`data-theme` and once inside a prefers-color-scheme media query scoped to
`html:not([data-theme="dark"]):not([data-theme="light"])` — that :not() pair is
what lets a stored dark choice survive a light-mode OS. Verified against both
OS preferences: load, click, click back, reload, all four correct. `data-theme`
is the control surface; Homepage's own `dark` class stays on <html> and does
not fight, because our rules carry !important on the surfaces Tailwind's
`dark:` variants would otherwise claim.
Two font substitutions, both documented rather than silent: Space Grotesk for
Bespoke Sans and JetBrains Mono for Victor Mono. Only Supreme was ever vendored
here and Skyfall's own notes call Victor Mono user-supplied, so this is a
two-line swap when the real faces arrive.
Dark and light, all four tabs: http://10.100.10.50:8090/b/homepage-skyfall/
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