Operator: "start the 1.7b training."
The 0.6B adapter learned the Gutenberg transcription's ~70-character line breaks
along with the prose -- its output wrapped at a mid-length-line ratio of 0.85
against the base model's 0.00. That is typography rather than style, and every
further rung would have inherited it, so the corpus is reflowed before rung 2
rather than after the sweep.
The reflow joins 57,430 of 85,380 paragraph blocks and keeps 27,950. Verse is the
hazard a blind join would destroy, so the decision is per block by median line
length: blocks whose lines cluster near the wrap width are flowed prose, blocks of
consistently short lines keep their breaks. Every kept multi-line block in the
sample was genuinely verse with its lineation intact. No line ended in a lone
hyphen, so the space-join could not split a word across lines. The acceptance
check is content identity -- " ".join(text.split()) byte-identical before and
after -- and it passed on all 852 records, proving only whitespace changed.
Concrete cost of the old defect: 5.7% of the training budget was newline tokens.
The same words pack to 5,210,112 tokens unwrapped against 5,525,504 wrapped.
The 1.7B run is live at 159 steps and roughly 18.7 s/it. Everything but the
carrier and the corpus is held from the 0.6B run: seed 4919, rank 32, lr 1e-4, seq
4096, batch 1 by accum 8, one epoch, eval and save every 25 steps so the minimum
is located rather than assumed.
That corpus change is a second variable and it is named as one. A 0.6B-vs-1.7B
comparison is descriptive, not attributable, until the chained 0.6B rerun on the
same unwrapped corpus lands behind it -- gated on the 1.7B actually producing an
adapter, because a chain that fires on failure turns one lost run into two.
"Did sense come back at 1.7B" is a within-arm reading and survives the confound;
any between-rung delta does not.
The original wrapped corpus is untouched, so the 0.6B run's pinned corpus sha
3959036cf851bf62 stays reproducible.
Ships the two pending code changes alongside the memory that describes them:
train_voice_lora.py gains --eval-steps/--save-steps (the 3-epoch pilot overfit
with per-epoch eval and save_strategy="no", so the minimum was neither visible
nor recoverable), and the althing post-office compose is pinned to 3.6.2.
Index rewritten: in-flight compressed from 176 lines to the live state, three
detail files added for R49 D1-D3, the MeroMero four-failure chain, and the
althing rollout. Seven closed pre-08-27 entries archived; the guards held back
the rest because they carry open deferred pointers.
The LossLog callback filtered on `"loss" in logs`. Trainer emits eval under
`eval_loss` with no `loss` key, so every eval record was discarded and
loss-series.json showed zero eval points while the log a metre away carried
`eval_loss: 3.198`. An artefact that omits data which demonstrably exists reads as
"no eval was run" rather than "the collector dropped it", which is the failure mode
that costs someone a re-run.
Collector now accepts either key. Seed 2 gets it from launch; seed 1 is already
running with the old code, so recover_eval_series.py parses the eval records back
out of its log -- the data was printed, not lost -- and the chain runs that
recovery before cutting the generation arms, so the artefact is complete before
anything reads it.
The two seeds therefore differ in logging code but not in training math: the
callback only affects what is recorded, never what is computed, so the weight
trajectories remain comparable. Noting it because a difference between the two
arms whose spread sets the decision threshold is worth stating even when it is
provably inert.
brokkr-smithy corrected H02's incumbent naming: qwen3.6-35-a3b-heretic was retired
from the gateway roster on 2026-08-15 and is not what Skaldsong would call today.
Verified against the gateway and the seat itself -- alias `gen` resolves to
hosted_vllm/qwen3.8-27b-uncensored on ana-ml2:8015, container vllm-gen, 262,144
ctx. The arm targets that.
24 records, style-prompted on the same prompts and sampler as the other arms.
Alias resolved at run start AND end and confirmed stable across the run, per the
fleet rule that an artefact records the backing model rather than the alias.
Two things recorded rather than glossed:
The harness is NOT matched to the other arms and the artefact says so. Base and
adapted arms are local transformers on gx10; the incumbent is a served NVFP4 27B
reached over the gateway, and it is an instruct model receiving a style
instruction where the others are base models receiving none. That asymmetry is the
comparison H02 asks for -- prompted imitation against trained voice -- but it must
not be reported as if the harnesses were identical.
The gateway echoes the ALIAS in each response's `model` field, so a row read on
its own would have recorded "gen" as provenance -- the same class of mistake that
inflated an exposure count 4.7x on this fleet. Rows now carry
alias_echoed_by_gateway beside backing_model_resolved and its date, and the
generator was fixed at source rather than only in the emitted file.
Sanity: median 392 completion tokens, zero records opening with markdown or
meta-commentary, output reads as continuation prose. The style prompt was written
to be a fair incumbent rather than a strawman, since this arm is what the adapter
must beat.
Entity resolution, deterministic rename augmentation, packing and the pilot
trainer. Qwen3-0.6B-Base is training now: 507 steps, 11.2 s/it, ~1h35m.
D2 -- gender resolution is TITLE-FIRST, and that is a change from F02's method
rather than a port of it. F02 used pronoun proximity and recorded that it is
structurally blind to the first-person narrator, whose name appears mainly in
dialogue surrounded by other people's pronouns. Measured here, proximity called
JANE MALE -- the narrator of Jane Eyre and the single worst entity to get wrong.
Titles have no such blind spot: Miss Eyre, Mrs. Fairfax, Mr. Rochester, Madame
Beck, M. Paul, and a 19th-century novel is saturated with them. Measured: 16
entities resolved, zero wrong, every ambiguous case landing on HELD -- shared
family surnames like Helstone and Pelet genuinely belong to both a man and a
woman and hold as they should.
Held means ungendered, not unrenamed. A HELD entity is still renamed, from the
gender-neutral surname pool, because the operator's Yarros directive was "rename
all proper nouns" and holding a place leaks it -- Thornfield appears 100 times in
Jane Eyre and is as author-specific as Riders Quadrant was. Substituting a neutral
token makes no gender claim, so no gender claim can be wrong.
D3 -- pool is French + English per the operator, weighted per work by setting:
Brussels novels 60% French, Yorkshire novels 25%. Locales restricted to
fr_FR/fr_BE/en_GB/en_IE; en_US and en_AU carry modern surnames that are wrong
register for the 1840s. The pool is filtered against Brontë's own 75-letter
alphabet, so French accents stay and Czech/Latvian marks do not.
Two collision defects found by running the leak gate rather than trusting it:
`Burns` and `Marie` were drawn as replacements while being Brontë characters --
F02's collision filter was built against Yarros and does not carry -- and then
`Pierre-Yves` passed a whole-string filter while `Pierre` (Mademoiselle St.
Pierre) is a Villette character. The filter now compares by COMPONENT. Final gate:
0 of 203 source entities survive in any of 24 copy-files.
Trainer records what the run RESOLVED to rather than what it requested -- attention
implementation, dtype, device, corpus sha and harness cleanliness are read back off
the live objects. transformers 5.x has dropped warmup_ratio, caught by reading the
signature after the first launch failed on it; the 3% warmup is computed into
warmup_steps instead.
scripts/r49-corpus/{build_corpus,verify_corpus}.py; corpus staged at
gx10:~/r49-corpus/. Catalogue ids verified against gutenberg.org's own search
rather than recalled. Charlotte only -- the Bell poems are co-authored and the
Gaskell biography is a different hand, so neither belongs in a single-voice corpus.
Jane Eyre 1260 · Villette 9182 · Shirley 30486 · The Professor 1028
680,291 words · 142 chapters · 950,974 Qwen3 tokens (1.40 tok/word)
alphabet 75 letters, 23 non-ASCII · round-trip lossless · 0 byte-fallback
All 11 acceptance checks pass, including both tokenizer legs run against the pilot
carrier itself. With a real denominator the projections tighten: at 6 rename copies
x 3 epochs = 17.1M tokens, the 0.6B pilot is 1.98 h.
THE ALPHABET INVERTS THE YARROS RESULT. Brontë writes French constantly -- Villette
is set in a French-speaking city, Jane Eyre has Adèle, The Professor is set in
Brussels -- so the corpus carries é 432, è 237, à 93, ê 79, ô 48 plus œ and æ. F02
measured Yarros at 0.0002% non-ASCII and derived an ASCII-fold for the name pool.
Under F02's own subset rule the Brontë pool may keep FRENCH accents and must still
exclude the Czech/Latvian/Slovak/Hungarian marks that never appear here. The fold is
per-work, and this is the first corpus where deriving it changes the answer.
Typography was inconsistent across works and it was the transcriber, not the author:
Shirley uses straight quotes and `--` with zero em-dashes while Jane Eyre and
Villette use curly and em-dash. Normalised toward what the text means.
Three defects, each found by running something rather than reasoning about it:
`Produced by` matched Brontë's own prose four times, which is the adjective-"minor"
shape again and is fixed by anchoring boilerplate patterns to line start; asserting
open/close quote counts must be equal is wrong, because 19th-century multi-paragraph
speech legitimately runs a surplus of opens, so the real error signature is that no
paragraph may begin with a closing quote; and The Professor's table of contents puts
two chapter names per line, so a bare regex returns 38 headings for a 25-chapter
novel and a minimum-gap filter still leaks its tail -- the rule that works is that
the body's "CHAPTER I" is the last one in the file.
Records the operator's pilot ruling: trial on Qwen3-0.6B-Base first, move up only if
it produces something useful.
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