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LANA-Science-Qwen3.8-Distill
Verified science supervised fine-tuning data distilled from Qwen3.8-27B by the LANA-research data engine, one config per data source (the source of the prompt). Every row is one teacher response that passed automatic verification, in the canonical slf-sft-v1 schema with full provenance.
Verification: the extracted option letter matches the key (explicit-choice parser, gold-independent).
producer.stage/producer.modeidentify the sampling stage and thinking mode.
Subsets (one per source)
| config | source dataset | train | validation |
|---|---|---|---|
sciv1_mcq |
nvidia/Nemotron-Science-v1:MCQ | 35,396 | 336 |
sciv2_so |
nvidia/Nemotron-SFT-Science-v2:so | 23,502 | 209 |
kmcqa |
nvidia/Nemotron-RL-knowledge-mcqa | 12,734 | 127 |
sciv2_rqa_mcq |
nvidia/Nemotron-SFT-Science-v2:rqa(MCQ) | 4,053 | 33 |
sciv2_synmcq |
nvidia/Nemotron-SFT-Science-v2:syn_mcq | 4,011 | 41 |
crossthink_qa |
nvidia/Nemotron-CrossThink:QA | 3,730 | 45 |
goose_stem |
nvidia/Nemotron-Research-GooseReason-0.7M:stem | 3,322 | 27 |
webmcqa |
nvidia/Nemotron-RL-knowledge-web_search-mcqa | 77 | 0 |
Total: 87,643 rows. validation is a 1% hold-out by question_hash.
Loading
from datasets import load_dataset
ds = load_dataset("LANA-research/LANA-Science-Qwen3.8-Distill", "sciv1_mcq", split="train")
row = ds[0]; prompt = row["messages_in"]; target = (row["reasoning"], row["response"])
Fields
Schema slf-sft-v1 (31 fields): identity (uid, family, family_version, domain, task_type, language); input (messages_in, tools, problem_text, question_hash, source_dataset, source_id, source_split, license, seed_pool, difficulty); reference (kind, answer, choices, tests_ref, constraints, gold_source); producer (teacher model, serving, mode, sampling, stage, sample index); gen_run (client, hash, time); target (reasoning = text inside <think>, response = visible answer, finish_reason, n_tokens); verify (method, version, passed, score, details); accepted (always true here), reject_reason, flags, decontam (method, checked_against, hit), split, meta (JSON).
Decontamination: exact + 13-gram checks of the prompt against MATH-500, GPQA-Diamond, IFEval, MMLU-Pro, LiveCodeBench v6, OJBench and other evaluation inputs (per-row record in decontam).
License
Mixed: each row's license field gives the license of its prompt source; responses were generated with Qwen3.8-27B.
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