calib-corpora — a pool of calibration material, and per-model builds from it

This repository is not a corpus. It is a pool of raw units with provenance, plus recipes that turn the pool into a calibration set for one specific model, plus the measurement corpora that quant is scored against.

That split exists because the previous layout could not survive a change of model. The old calib_train.txt had DeepSeek-V4's chat markup baked into its agentic and reasoning slices and a vocabulary sweep built for DeepSeek's 129,280 embedding rows. Pointed at meta-models/Muse-Glimmer-30B it covered 47.56% of that model's 202,048 rows. Rebuilt from the same pool under a Muse Glimmer recipe, the same material plus fresh collection covers 97.22%.

pool/          raw units, model-agnostic, one JSONL line per unit with licence
               and provenance. Dialogue is stored as `messages`, never as
               rendered markup.
recipes/       what a build is made of: shares, budgets, tokenizer, seed
builds/        the output of tools/build.py under a recipe, with a manifest
eval/          measurement corpora; disjoint from every build by construction
tools/         the pipeline
pipeline/      the previous DeepSeek-era pipeline, kept as it was

Current build: muse-glimmer-30b

builds/muse-glimmer-30b/ — recipe recipes/muse-glimmer-30b.yaml, seed 20260810.

	requested	actual	tokens
agentic	25.0%	25.50%	1,250,047
code	18.0%	18.66%	914,575
reasoning	15.0%	15.31%	750,307
longctx	12.0%	12.75%	624,756
multilingual	12.0%	12.27%	601,231
vocab_sweep	10.0%	8.50%	416,579
structured	5.0%	3.93%	192,839
graphics	3.0%	3.08%	151,161

calib_train.txt — 3,363 documents, 4,901,495 tokens by tokenizer.json (4,954,537 by llama-tokenize; see Two tokenizers below). 50.7% of the tokens are synthetic, all of it template-generated and marked in the manifest.

calib_longctx.txt — 29 unbroken documents, 751,109 tokens, each 17,745 to 32,253 tokens. Separate from calib_train.txt because 13 of the model's 52 layers are full-attention with RoPE disabled, and nothing shorter than the 2,048-token sliding window exercises them.

Document length in calib_train.txt: p50 = 562, p90 = 3,349, p95 = 4,958, p99 = 14,814, max = 49,527. 68 documents are ≥ 8k tokens and carry 25.6% of all tokens.

Vocabulary coverage

Denominator is 202,048 embedding rows.

	old corpus	this build
seen ≥ 1	96,099 (47.56%)	196,430 (97.22%)
seen ≥ 10	14,454 (7.15%)	24,831 (12.29%)
seen ≥ 100	2,097 (1.04%)	4,968 (2.46%)
unseen	105,949 (52.44%)	5,618 (2.78%)

The jump is the vocabulary sweep, regenerated for this tokenizer by tools/vocab_sweep.py: 200,185 of the 200,220 ids that have any standalone textual form, at 2.09 tokens per id. The remaining 1,828 ids are fragments of multi-byte characters and cannot appear alone in any text at all — that is the real ceiling, 99.08%, not 100%.

Running the imatrix

llama-imatrix -m Muse-Glimmer-30B-BF16.gguf \
  -f builds/muse-glimmer-30b/calib_train.txt \
  --parse-special -c 4096 -o imatrix.dat

--parse-special is not optional. llama-imatrix defaults to parse_special = false, and without the flag every <|start|>, <|message|>, <|eot|> and <|eom|> in the agentic and reasoning slices is tokenized as literal punctuation — 40% of the corpus would calibrate token sequences the model never emits. The flag is registered for the imatrix example only.

Measurement corpora

eval/ never intersects a build. Verified pairwise with tools/crosscheck.py.

corpus	tokens	chunks @4096	scored positions	vocab	dup lines
eval/neutral/eval_neutral.txt	353,771	86	176,128	19.43%	1.37%
eval/code/eval_code_full.txt	350,887	85	174,080	15.31%	31.63%
eval/agentic/eval_agentic.txt	350,438	85	174,080	5.50%	44.55%

llama-perplexity scores the second half of each context window, so a corpus must be about twice the size of the measurement you want out of it.

• neutral — 30 languages, no code. Latin script is 46.5% of letters; Arabic 7.6%, Armenian 6.3%, Cyrillic 5.4%, Han 5.3%, Greek 4.3%, Hebrew 4.2%, Devanagari 3.7%, Myanmar 3.6%, then Bengali, Thai, Georgian, Tamil, Hangul, Hiragana, Katakana, Ethiopic.
• code — eval_code.txt is the file previously called eval_neutral.txt, byte-identical so old measurements stay comparable. It was never neutral prose: measured, it is a source-code corpus. eval_code_ext.txt extends it from seven repositories that appear nowhere else; eval_code_full.txt is the two concatenated and is what to measure against.
• agentic — conversations in the model's own markup, all four reasoning strengths (low 50 / medium 46 / high 48 / xhigh 48), grounded in four repositories reserved for this purpose.

Two things to know before using eval/agentic:

1. llama-perplexity has no --parse-special. Its special tokens will be scored as their literal characters. That is still a valid comparison between quants of the same model — the text is identical for all of them — but it is not what the model sees at inference.
2. Its duplicate-line share is 44.6% (30.3% excluding the chat template's own scaffolding). A conversation that declares tools must repeat the template's tool-definition block verbatim; there is no way to have native markup and a 2% duplicate-line ceiling at once. The same applies to code (}, });, #[test]). Only eval/neutral meets 2%, at 1.37%.

The pool

7,415 units after filtering, in pool/<category>/*.jsonl. Every line carries source, license, path, origin and a provenance object.

Dialogue is stored as messages, not as rendered text, and tools/build.py applies the target model's chat format at build time. Storing one model's special tokens in the pool is exactly what made the previous corpus single-use.

render tells a build what a unit is:

• text — used verbatim
• chat — messages are rendered by tools/glimmer_fmt.py
• dsv4 — already rendered in DeepSeek markup. Kept, never built from. 203 such units are preserved for provenance; their conversations were regenerated structurally instead.

pool/_quarantine/ holds everything removed, with the reason on each record. Nothing is deleted.

Provenance and licences

Repository files keep their upstream licence (MIT, Apache-2.0, BSD-3-Clause, BSL-1.0) and record the commit they were taken from. Wikipedia is CC-BY-SA-4.0. Generated units are CC0-1.0 and live under a synthetic/ subdirectory, with the caveat that agentic traces quote real repository files verbatim inside tool results — those excerpts keep their own licence, which is recorded per unit.

patriciogonzalezvivo/thebookofshaders is an obvious fit for the graphics slice and is all-rights-reserved. It is not here and must not be added.

Deduplication and contamination

All at 13-word shingles, tools/dedupe.py.

check	result
wikitext-103-raw-v1 (superset of wikitext-2)	6 documents removed
pool vs eval, any shared 13-gram	3,672 documents
pool vs eval, distinctive 13-gram	143 removed, 0 remain
exact duplicates within the pool	379 removed
near duplicates at J ≥ 0.8	202 removed
total quarantined	1,056 of 8,471 (12.5%)

The two eval rows differ by a factor of twenty-five and the difference matters. A 13-gram shared by thousands of documents is an MIT header or an SPDX line, not leaked measurement data; treating those as contamination removed 43% of the pool on the first run and improved nothing. A gram counts as evidence only when it occurs in at most two pool documents. Both numbers are reported rather than just the flattering one.

No wikitext of any version is in any build. Grepping for the string proves nothing — wikitext is a curated slice of English Wikipedia and this pool contains English Wikipedia — so the check is shingle overlap against the benchmark text itself.

Two tokenizers, one percent apart

llama-tokenize and tokenizer.json disagree by about 1% on the same file (4,954,537 vs 4,901,495 tokens on calib_train.txt). The disagreement is not spread evenly — it is almost entirely non-Latin text:

slice	tokenizer.json	llama-tokenize
multilingual	34,988	37,072	+5.96%
vocab_sweep	28,389	28,826	+1.54%
longctx	356,401	356,215	−0.05%
code	16,425	16,420	−0.03%
agentic	34,950	34,948	−0.01%
graphics, reasoning, structured			0.00%

Both sides run the same llama4 split regex, but llama.cpp implements the Unicode property classes in it with its own tables rather than a PCRE engine, and on Han, Arabic, Devanagari and the like it splits more finely. Latin-script code and prose agree to within a rounding error.

llama-tokenize is authoritative: it is the vocabulary and the pre-tokenizer llama-imatrix will actually use, and the coverage figures above come from it. The manifest's per-document token counts come from tokenizer.json, because a build needs an in-process tokenizer to hit a budget. Treat the manifest as sizing and the coverage report as measurement — and read the multilingual share as about 6% larger in practice than the manifest states.

A llama.cpp crash worth knowing about

llama-tokenize and llama-imatrix abort on some plain-ASCII input:

$ printf '\xF4\x91\x92\x93' > t.txt      # sixteen ASCII characters
terminate called after throwing an instance of 'std::invalid_argument'
  what():  invalid codepoint

The escape sequence is only described, not encoded; F4 91 92 93 would decode to U+111493, past U+10FFFF, and unicode_cpt_to_utf8 in src/unicode.cpp throws instead of substituting U+FFFD. UTF-8 conformance test suites are full of such literals. The previous calib_train.txt contains one (json-cpp tests/src/unit-unicode1.cpp) and would have killed an imatrix run partway through. tools/screen.py finds and quarantines such documents by divide and conquer; it found exactly two.

Reproducing

python tools/pool_import.py                      # legacy flat corpus -> pool
python tools/harvest.py --raw RAW --wiki WIKI    # collect more
python tools/gen_agentic.py                      # conversations, structurally
python tools/gen_reasoning.py --scale 48
python tools/gen_structured.py
python tools/vocab_sweep.py --tokenizer TOKENIZER_JSON
python tools/dedupe.py --wikitext WIKITEXT
python tools/screen.py --gguf GGUF
python tools/build.py --recipe recipes/muse-glimmer-30b.yaml
python tools/build_eval.py --build builds/muse-glimmer-30b ...
python tools/coverage.py --gguf GGUF --tokenizer TOKENIZER_JSON \
    builds/muse-glimmer-30b/calib_train.txt
python tools/crosscheck.py builds/*/calib_*.txt eval/*/*.txt

Order matters: harvesting excludes the measurement split by (source, path), and the generators draw from the pool, so agentic traces cannot quote a held-out file.

tools/test_glimmer_fmt.py renders 13 conversations through both tools/glimmer_fmt.py and transformers.apply_chat_template and asserts byte equality. Run it before trusting any build; if the markup is wrong, the agentic slice calibrates nothing.

Adding a model

Write recipes/<model>.yaml, regenerate the vocabulary sweep for its tokenizer, run tools/build.py. The pool does not change. A sweep and any render: dsv4 style pre-rendered markup are the only tokenizer-bound things in the tree, and both are excluded from builds by provenance.excluded_from_builds.