Why this matters
Distillation datasets that preserve model reasoning traces let smaller or fine-tuned models learn not just answers but chains of thought. DeepSeek‑V4‑Distill‑8100x packages teacher-generated reasoning outputs (including explicit <think> reasoning blocks) into a compact, cleaned SFT set so practitioners can experiment with reasoning-focused distillation without collecting or re-running expensive teacher generations.
What Sets It Apart
- Teacher-derived reasoning outputs: Answers were produced by the DeepSeek‑V4‑Flash teacher and often include intermediate reasoning followed by a final answer, which helps downstream models learn multi-step justification patterns rather than only end results.
- Cleaned, focused prompt pool: Source prompts come from Jackrong/GLM-5.1-Reasoning-1M-Cleaned and were filtered to remove real-time, identity, and overlong questions, producing 7,716 higher-quality training examples—small but concentrated for reasoning transfer.
- Ready-to-use format: Distributed as a single JSONL train split with conversation-style and input/output fields plus metadata (input/output token counts, teacher id), making conversion to chat-style or direct I/O SFT pipelines straightforward.
Who it's for — and tradeoffs
Great fit if you want a compact, reasoning-focused distillation set to: finetune LLMs for improved chain-of-thought behavior; run ablations on reasoning transfer; or prototype SFT workflows without regenerating large teacher outputs. Look elsewhere if you need large-scale pretraining data, real-time fact-backed prompts, or domain-specific (non-general reasoning) examples—the dataset intentionally excludes time-sensitive and identity-related prompts and contains teacher-generated text that may inherit factual errors or style biases.
Practical notes
- Size & license: ~7.7K examples (train split); license listed as MIT on the dataset card. Created/updated April 2026 on Hugging Face.
- Typical uses: SFT for reasoning, distillation experiments, format-conversion tests (chat ↔ I/O). Because outputs are synthetic teacher generations, validate model behavior on held-out, human-annotated benchmarks before deploying in safety-critical settings.
Overall, this is a compact, purpose-built distillation resource for researchers and engineers experimenting with reasoning transfer via supervised fine-tuning.
