Datasets:
license: apache-2.0
task_categories:
- text-generation
language:
- en
tags:
- distillation
- reasoning-traces
- function-calling
- synthetic
size_categories:
- 10K<n<100K
scram-curriculum: the training set behind Scram-0.8B
17,837 verified teacher traces. This is the complete supervised fine-tuning set used to train negativevoid/Scram-0.8B-6bit, published so the recipe is checkable rather than merely described.
How it was made
Traces were generated by Qwen3.5-4B and Qwen3.5-9B, then kept only if the final answer exactly matched gold. Roughly 7% of generations were discarded by that filter. Verification is final-answer-only: no one checked that the intermediate reasoning is sound, so a trace can reach the right answer by a wrong route. Treat the reasoning as teacher output, not as ground truth.
Composition
| kind | rows | what it teaches |
|---|---|---|
| reason | 10,612 | multi-step chains (arithmetic 1,241 · syllogism 1,653 · ordering 1,566 · boolean 1,944 · codetrace 1,343 · gsm8k 3,665) |
| tool | 1,595 | JSON-convention tool calls |
| copy | 1,200 | retrieval-grounded answering from notes |
| pytool | 1,000 | pythonic call convention |
| negation | 800 | scope and negation handling |
| distract | 600 | irrelevant-note resistance |
| synth | 600 | multi-note synthesis |
| factory-pos / factory-ref | 400 / 300 | schema-driven call construction and its refusal corner |
| missing-param | 400 | asking for absent arguments instead of inventing them |
| refusal | 330 | declining out-of-scope requests |
The refusal splits are deliberately class-separated. We found the classes do not cross-transfer: teaching wrong-tool refusal does nothing for missing-param refusal. Class coverage mattered more than data volume.
Provenance and licensing
- 3,665 rows (21%) are GSM8K-derived — questions from openai/gsm8k train split (MIT), with traces generated by us. Consequence: any GSM8K evaluation of the resulting model measures trained capability, not transfer. Disjointness from the frozen probe is asserted by exact question match at build time.
- The remaining reasoning items are fully synthetic with zero real-world entities (invented nouns like "Kreskarnpre", "zlupsnus"), so they cannot be answered from memorized world knowledge.
- Tool schemas are synthetic. The model consequently underperforms on real-world function inventories (BFCL simple AST 46 vs Hammer2.1-0.5b's 66) — if you fine-tune on this, expect to add your own domain schemas.
- Teacher outputs come from Qwen3.5 (Apache-2.0). This dataset is Apache-2.0.
Eval hygiene
Checked at publication: zero overlap between these 17,809 unique prompts and every held-out suite used to evaluate the model (reason n=300, heldout n=80, gsm8k probe n=100, know n=301). The evaluation instruments themselves are deliberately not published — releasing them would contaminate them for everyone.
Format
One JSON object per line:
{"question": "...", "trace": "...", "answer": "D", "family": "ordering", "kind": "reason"}
Rows of kind: tool/pytool/refusal etc. use prompt instead of question and
carry no answer field (the trace is the target).