license: cc-by-4.0
language:
- ru
- en
task_categories:
- question-answering
- text-generation
size_categories:
- n<1K
pretty_name: ABench75
tags:
- benchmark
- evaluation
- reasoning
- cybersecurity
- math
- logic
- code
ABench75
A compact bilingual (Russian + English) benchmark for instruction-following and short-answer reasoning across five domains: math, logic, code, cybersecurity, general.
- Total tasks: 375 (75 per domain)
- Public split: 300 tasks (
data/tasks_public.jsonl) - Private holdout: 75 tasks (15 per domain, not published) — for honest tracking
- Languages: ~59% English / ~41% Russian
- Scoring: regex match on the final answer (
refield, case-insensitive)
Canary:
ABENCH75_CANARY_8f3c1d9a. If you find this string in a training corpus, the benchmark has leaked — treat contaminated scores accordingly.
Format
Each line of data/tasks_public.jsonl is one JSON object:
{"id": "math_ru_0", "lang": "ru", "domain": "math",
"q": "Сколько будет 74 умножить на 56? Ответь только числом.",
"re": "\\b4144\\b"}
id— stable identifierlang—ru|endomain—math|logic|code|cyber|generalq— the promptre— Python regex the generated answer must match (search, ignore case)
Usage
pip install -r requirements.txt # torch, transformers, peft (optional)
python run_eval.py --model <hf-model-or-path> --tasks data/tasks_public.jsonl
# optional: LoRA adapter + reasoning-level tag
python run_eval.py --model ./merged --adapter ./lora --effort high --batch 8
The runner prints overall accuracy and breakdowns by domain and language.
Scoring is intentionally simple: the answer must match re. Models that emit
reasoning should put the final answer in plain text (the runner strips common
<think>…</think> wrappers first).
Results (public split, 300 tasks)
| model | params | overall | math | logic | code | cyber | general |
|---|---|---|---|---|---|---|---|
| Velary 1.2 Flash (medium) | 1.2B | 82.7% | 97% | 49% | 85% | 96% | 85% |
| Velary 1.2 Flash (high) | 1.2B | 81.6% | 96% | 43% | 85% | 96% | 88% |
| Velary Mini (no-think) | 0.35B | 50.7% | 32% | 39% | 67% | 55% | 61% |
Numbers are from the public 300-task split; the private 75-task holdout is reported separately by the maintainers.
Design notes & limitations
- Short-answer regex scoring favours direct final answers; it is not a judge for long-form quality.
- 75 tasks per domain is enough for a quick signal, not for fine-grained ranking; treat differences of a few points as noise.
- Categories are deliberately broad; see
data/tasks_public.jsonlfor coverage.
Split policy
The public/private split is stratified by domain (60 public / 15 private per domain). The private holdout is kept out of this repository to reduce contamination and to provide a cleaner progress signal.
License
- Data (
data/tasks_public.jsonl): CC BY 4.0. - Code (
run_eval.py, tooling): MIT — seeLICENSE.