Why this dataset matters
Large instruction-following models improve most when trained on examples that expose not only answers but the intermediate reasoning that produced them. This dataset supplies 40k teacher-generated traces from Qwen3.8-27B where each entry embeds a <think> block (chain-of-thought) plus a final response, giving practitioners realistic, model-originated CoT data at scale without collecting human explanations.
What Sets It Apart
- Teacher-model origin: All traces were generated by Qwen3.8-27B, so the dataset reflects a single high-capability model's reasoning style, which is useful for off-policy distillation and SFT targeting that teacher distribution.
- Domain coverage and size: 40,000 examples spanning code (40%), math (27%), science (17.5%), and logic (15%), with an average of ~5.7k tokens per example—suitable for long-form reasoning and multi-step solution fine-tuning.
- Structured format for SFT: Each record stores user prompt, an assistant content field containing a
<think>block plus final response, and token counts; available in JSONL/parquet to plug into Hugging Face Datasets and SFT toolchains. - Low generation cost footprint: Metadata reports total ~230M tokens generated using vLLM with FP8 precision, which gives a practical reference for replication or scaling.
Who It's For & Trade-offs
Great fit if you are fine-tuning or evaluating LLMs on chain-of-thought reasoning, distillation from a single teacher model, or building SFT pipelines that need realistic assistant traces across coding and STEM tasks. Look elsewhere if you need human-authored explanations, multi-teacher consensus traces, multilingual coverage (this dataset is English-only), or datasets curated for strict human-verified correctness; model-generated chains can contain systematic biases or errors that require filtering before high-stakes deployment.
Where It Fits
Use this dataset as a starting point for teacher-student distillation, synthetic CoT augmentation, or stress-testing SFT recipes for long-context reasoning. Combine with human-verified benchmarks when safety and factual accuracy are critical.
