Create README.md

README.md

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+---
+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 (`re` field, 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:
+
+```json
+{"id": "math_ru_0", "lang": "ru", "domain": "math",
+ "q": "Сколько будет 74 умножить на 56? Ответь только числом.",
+ "re": "\\b4144\\b"}
+```
+
+- `id` — stable identifier
+- `lang` — `ru` | `en`
+- `domain` — `math` | `logic` | `code` | `cyber` | `general`
+- `q` — the prompt
+- `re` — Python regex the generated answer must match (search, ignore case)
+
+## Usage
+
+```bash
+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.jsonl` for 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 — see `LICENSE`.