digit-router-dataset

34 709 Russian training rows for a two-step tool router over a catalogue of 95 headless utilities in 14 categories. Generated deterministically from the catalogue's JSON schemas — no teacher model was used. 23.8 % of the rows are refusals, and that fraction is the point of the dataset.

This is the set the published digitable-lol/digit-router-0.6b and digitable-lol/digit-router-1.7b adapters were trained on.

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1. Read this first: what this dataset is for, and what it is useless for

The router does not produce the content of an answer. It does two things:

query → {"category": "crypto"} → {"tool_id": "hash_text", "args": {"text": "Привет", "algorithm": "SHA256"}}

Step 1 maps a query to one of 14 categories, or refuses. Step 2, given the JSON schemas of that category's tools, emits a tool call with extracted arguments, or refuses.

It is bound to one specific catalogue of 95 tools. The tool ids, argument names and enum values are that catalogue's. Outside it the dataset teaches a model to emit {"tool_id": "bip39_from_entropy", ...} for tools that do not exist in your system. It is not a general function-calling corpus, and using it as one will produce a model that confidently routes to nothing. What generalises here is the method and the refusal design, not the labels.

Everything else in this card is downstream of that sentence.

2. Composition

	v1	v2	v3 (this release)
rows	32 798	34 421	34 709
unique queries	18 532	18 473	18 597
train / validation	31 186 / 1612	32 684 / 1737	32 982 / 1727 (5 % by query)
tools covered	94 of 94	94 of 94	95 of 95
categories	14	14	14
queries per tool	median 128	median 128	min 70, median 128
refusal share	24.4 %	24.0 %	23.8 % (target band 15–25 %)

Source: reports/stats.json and the producing pipeline's README.

By label

class	rows	share
tool_call (routing)	26 450	76.2 %
MISSING_ARGUMENT	3422	9.9 %
OUT_OF_SCOPE	2446	7.0 %
FALSE_PREMISE	2391	6.9 %
refusals total	8259	23.8 %

Step 1 (query → category): 16 791 rows. Step 2 (query + schemas → tool call): 17 918 rows.

By provenance (meta.family)

family	rows	what
core	28 644	the base classes
domain_fp	1780	FALSE_PREMISE about the product, the corpus and technical folklore
decoy_route	1343	step 1 for decoy queries (routing is still required)
decoy_mode	1162	MISSING_ARGUMENT with a decoy mode drawn from the schema's enum
decoy_nearmiss	1011	MISSING_ARGUMENT with a value of the wrong shape
domain_fp_route	769	step 1 for those false-premise queries that do name a computation

The 14 categories

converter 5403 · encoding 4035 · crypto 3500 · web 3095 · text 2591 · generators 2649 · auth 2156 · development 1843 · math 1767 · network 1677 · data 1396 · images 1027 · measurement 761 · datetime 465.

3. Row format

Every row is a system / user / assistant chat triple plus meta for validation.

Step 1 — the system prompt carries an index of the 14 categories (1447 characters):

{"category": "crypto"}
{"refuse": "OUT_OF_SCOPE"}

Step 2 — the system prompt carries compact schemas for one category only (681–2182 characters; all 95 schemas at once would not fit a sane context for a 0.6B model):

{"tool_id": "hash_text", "args": {"text": "Привет", "algorithm": "MD5"}}
{"refuse": "MISSING_ARGUMENT", "missing": ["text"]}
{"refuse": "FALSE_PREMISE", "correction": "Значения «SHA-999» у аргумента algorithm не существует; допустимые: MD5|SHA1|SHA256|..."}
{"refuse": "OUT_OF_SCOPE"}

Schemas render compactly (text*:string, algorithm:enum(MD5|SHA1|…)); the asterisk marks a required argument.

Because the schemas are embedded in every step-2 system prompt, the dataset is self-contained for training: you do not need the catalogue file to fine-tune on it. You do need it to use the resulting model.

4. Why a quarter of the rows are refusals

A dataset in which every query has an answer teaches one thing above all others: that an answer always exists. A router trained that way will route. It will pick the closest tool for a request no tool serves, invent an argument that was never given, and accept a premise that is false — because it has never seen the alternative demonstrated.

Three refusal classes are therefore first-class labels, not an afterthought:

• OUT_OF_SCOPE — no tool in the catalogue does this;
• MISSING_ARGUMENT — the right tool, a required argument absent from the query;
• FALSE_PREMISE — the query embeds something untrue (a non-existent enum value, a non-existent scale, a non-existent property).

The decoy families exist because the naive version of MISSING_ARGUMENT is too easy: decoy_mode supplies a plausible mode taken from the tool's own enum while omitting the argument that actually matters, and decoy_nearmiss supplies a value of the wrong shape. Both make "something was provided" a useless signal.

The measured effect

Same harness, same 150 red-team tasks, the only change being what the model was trained on. "Credited" refusals include empty and unparseable output, which the harness scores as a refusal; "conscious" refusals are explicit {"refuse": ...} with a code. The gap between the two columns is why both are given.

model	credited refusal	conscious refusal	unreadable answers / 250
bare Qwen3-0.6B, no fine-tune	75.3 %	9.3 %	115
bare Qwen3-1.7B, no fine-tune	81.3 %	70.7 %	21
Qwen3-0.6B + this dataset	91.3 %	90.7 %	2
Qwen3-1.7B + this dataset	91.3 %	91.3 %	1

Source: train/RESULTS.md §7, §13.2 of the producing project.

Which version was measured. Those runs used v2 (34 421 rows). What is published here is v3 (34 709). v3 adds no new classes: it resynchronises with a catalogue that had grown to 95 tools (emoji_search, +256 rows — one median tool's worth), removes 28 queries that collided with the held-out evaluation set, and refreshes 21 false-premise corrections that had gone stale against the live corpus. Adding those rows to the old file was not possible, because the step-1 category index and the step-2 text schemas both changed, and the file would have ended up with two different system prompts for one category. No v3-vs-v2 ablation was run, so the numbers above are v2's; treat them as applying to v3 only to the extent you accept that a 0.8 % row change with no new class does not move them.

The untuned 0.6B did not refuse — it broke, and the harness credited the wreckage. The untuned 1.7B genuinely can decline without any fine-tuning, which is worth knowing before attributing everything to the data.

An independent run on a different harness makes the same point on the same axis: four bare Qwen3 models over 630 tasks each through vLLM, share of credited refusals that were actual decisions — 2.8 % → 51.5 % → 52.5 % → 64.0 % for 0.6B → 1.7B → 8B → 32B-AWQ. Source: eval/results/BASELINE_VLLM.md. Note that this 2.8 % and the 9.3 % in the table above are not the same measurement: different harness, different denominator, different task mix. Quoting "2.8 % → 91.3 %" as a single before/after would be crossing two rulers, and this card does not.

Which lever did the work

Both combinations were measured, so the two can be separated:

lever	comparison	conscious refusal	tool_accuracy	arg_accuracy
data (v1 → v2) on 1.7B	1.7B+v1 → 1.7B+v2	83.3 → 91.3	86.0 → 85.0	92.7 → 90.1
data (v1 → v2) on 0.6B	0.6B+v1 → 0.6B+v2	75.3 → 90.7	83.0 → 81.0	93.7 → 88.9
base size (0.6B → 1.7B) on v2	0.6B+v2 → 1.7B+v2	90.7 → 91.3	81.0 → 85.0	88.9 → 90.1

Tripling the base model buys 4 points of routing accuracy and 0.6 points of conscious refusal. Changing the dataset buys 8–15 points of conscious refusal on either base. The data did the work. Note the cost, stated in the same table: argument accuracy went down 3–5 points on both bases. Teaching a model to decline makes it slightly worse at filling in the cases where it does not decline, and that trade is not free.

5. How the queries are built

Phrasings are not written one at a time. Each of the 95 tools has lexical seeds substituted into grammatically safe frames — imperatives ("посчитай хеш"), infinitives ("посчитать хеш"), noun phrases ("хеш строки") — with typo, casing and politeness noise layered on. The generator is deterministic under seed = 20260802, so the file regenerates byte-for-byte.

This is the dataset's other big limitation, and §7 says what it costs.

6. Validation

reports/validation.json is the gate the generator must pass:

check	result
structural errors	0
rows / unique queries	34 709 / 18 597
refusal share within the 15–25 % band	true (23.8 %)
tools covered	95 of 95
train/validation leakage	0
verbatim overlap with the evaluation sets	0
near-duplicate overlap (Jaccard ≥ 0.7) with the evaluation sets	0
red-team span overlap	0

Two categories of near-overlap are reported rather than suppressed, because "zero overlap" is a claim that deserves its exceptions in writing:

• 8 rows share a ≥ 6-word span with a main-set evaluation query. They are literal payloads a user would paste — a docker run line, a User-Agent string, a SQL statement, 192.168.1.0/24. The requests around them differ; the shared span is the data, not the task.
• 6 red-team evaluation queries are contained inside a longer training query, of which 5 belong to the published set and are listed in reports/validation.json; the sixth belonged to the held-out set and its entry was removed along with everything else naming that set. "переведи в base64" (a missing_param task) is a prefix of "ДАВАЙ переведи в base64 The quick brown fox" — a complete request with the argument present. The eval task is a refusal case precisely because the tail is absent.

During generation, 186 candidate rows were rejected for colliding with an evaluation task, and 4607 more were dropped for containing an unjustified required argument.

7. Limits — read before using

1. Bound to one catalogue. Repeated because it is the one that ends most attempts to reuse this: 95 specific tool ids, their argument names and their enum values. Outside that catalogue the labels are wrong, not merely unhelpful.

2. Templated phrasing. Queries are generated from seeds and frames, not collected from users. Indirect, conversational and elliptical phrasing is under-represented. This is a known and measured gap, which is what digitable-lol/hard-negatives-ru (config paraphrases, 2967 rows) exists to patch — and the fact that a patch was needed is the honest summary of this limitation.

3. Russian only. Argument values are frequently English or symbolic (the emoji_search tool searches by English keyword, so its arguments are English by necessity), but every request frame is Russian.

4. No teacher, therefore no teacher's judgement. The labels are correct by construction relative to the catalogue schemas. They contain no notion of what a person would actually have meant. A control measurement from the paraphrase work is worth knowing: a 32B teacher shown these rows reproduces the dataset's own label for only 64.6 % of them. That is not evidence the labels are wrong — the teacher is the less reliable of the two here — but it is evidence that "obviously correct" is doing work in that sentence.

5. refusal_share is a design parameter, not a discovered fact. The 15–25 % band was chosen, and the generator was tuned to it. If your deployment's real out-of-scope rate is 60 %, this mixture is wrong for you.

6. The evaluation sets are not in here. Overlap checks were run against both the published digitable-lol/digit-eval-tasks and a held-out set that is deliberately not published. Entries in reports/ that named tasks from the held-out set have been removed, with the removal recorded in the file.

7. meta.mentions and meta.extracted are generator bookkeeping. They are shipped because dropping them would make the rows unverifiable, not because they are a documented interface.

8. Origin and licensing

The metadata licence is other, deliberately.

The tool descriptions, tool ids, argument names, enum values and schema shapes in this corpus are derived from the project's tools-core catalogue, which is GPL-3.0, inherited from it-tools. Query phrasings, refusal classes, false-premise corrections and the generation method are the project's own.

Whether a copyleft licence on a source catalogue propagates to a dataset generated from its schemas, or to models trained on that dataset, is an unsettled question, and this repository does not pretend to settle it. We state the provenance and decline to declare the dataset GPL-3.0. If your compliance posture requires a definite answer, treat the GPL-3.0 provenance of the catalogue as a fact you must evaluate. This paragraph is not legal advice and not a grant.

Neither gpl-3.0 nor any permissive tag would be an honest single-token summary, which is why the field says other.

The same reasoning, and the same wording, was applied to the model repositories trained on this data.

9. Files and loading

path	what
data/all.jsonl	all 34 709 rows, canonical, byte-identical to what trained the models
data/train.jsonl	32 982 rows
data/val.jsonl	1727 rows (5 % split by query, so no query straddles the split)
hf/*.jsonl	the same rows with an Arrow-friendly schema
reports/stats.json	full composition: per-tool counts, per-category counts, decoy counts
reports/validation.json	the validation gate's output
MANIFEST.json	sha256 of every published file

from datasets import load_dataset
ds = load_dataset("digitable-lol/digit-router-dataset")
ds["train"], ds["validation"], ds["full"]

Why there are two copies. meta is a free-form object whose args key differs in shape and type per tool. On datasets ≥ 5 that is fine — the library types the column as Json and hands back a plain dict — and load_dataset("json", data_files="data/all.jsonl") was tested and works. Earlier versions infer an Arrow struct from a sample of rows and fail on the union. The hf/ view avoids the question entirely: it carries meta_json (the original object as a JSON string, json.loads round-trips it exactly) alongside lifted scalar columns step, label, tool_id, category, family, plus query and target pulled out of messages so you can filter without parsing. The system prompt is deliberately not duplicated into its own column — it is 0.7–2.2 kB per row and copying it would double the published size; read it from messages[0]. Train on data/; slice with hf/.

10. Reproduction

python3 src/build_tracks.py
python3 src/generate.py --per-tool 128 --missing-per-tool 14 --oos 1600 \
                        --fp-enum 750 --decoy-per-tool 14 --domain-fp 1400
python3 src/validate.py          # must return PASS

The generator is deterministic under seed = 20260802. Regenerating requires the tools-core catalogue at the same revision; the catalogue is live, and this release corresponds to the 95-tool state of it.

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По-русски, коротко

Что это. 34 709 строк на русском для двухшагового маршрутизатора к каталогу из 95 утилит и 14 категорий. Построено детерминированно из JSON-схем каталога, teacher-модель не использовалась. Парафразы — отдельный файл в digitable-lol/hard-negatives-ru.

Отказы — 23.8 %. Три класса: OUT_OF_SCOPE, MISSING_ARGUMENT, FALSE_PREMISE. Зачем: без них модель выучивает, что ответ существует всегда, и начинает маршрутизировать туда, где утилиты нет, и подставлять аргумент, которого не было.

Измеренный эффект. Один и тот же харнесс, 150 red-team задач: осознанный отказ 9.3 % → 90.7 % (0.6B без тюна → 0.6B на этих данных) и 70.7 % → 91.3 % для 1.7B. Основную работу сделали данные, а не размер базы: смена базы даёт +0.6 п.п. осознанного отказа, смена датасета — +8…15 п.п. Цена названа там же: точность аргументов падает на 3–5 п.п.

Применим только с нашим каталогом из 95 утилит. Вне его ids, имена аргументов и enum-значения просто неверны — это не общий корпус function-calling.

Происхождение. Описания и схемы инструментов производны от каталога it-tools под GPL-3.0. Вопрос о распространении copyleft на производные данные в отрасли не решён: мы указываем происхождение и не объявляем датасет GPL-3.0. Поле лицензии — other, сознательно.