datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
thinking-cap-tier-raw-traces
Thinking Cap Tier Raw Traces (TCS v4)
[!IMPORTANT]
Dataset Release v1.2 (Sept 2026) — Clean Delimiters & Zero-Padding Architecture:
All 38,158 candidate reasoning traces across all 4 tiers (candidates_low.jsonl, candidates_mid.jsonl, candidates_high.jsonl, candidates_xhigh.jsonl) are 100% sanitized:
Zero batch-padding residues (<|pad|>): Completely purged across all records.
Strict Delimiter Integrity: Generation blocks cleanly separate thought deliberation tags… See the full description on the dataset page: https://huggingface.co/datasets/Davd-b01/thinking-cap-tier-raw-traces.qwen35-a3b-thinking-traces
Qwen3.5-35B-A3B Thinking Traces — SAE Training Data
Per-sentence L17 residual activations from Qwen/Qwen3.5-35B-A3B generating CoT on MMLU-Pro.
Stats
Model: Qwen/Qwen3.5-35B-A3B
Layer: L17 residual (~42% depth of 40-layer hybrid MoE)
Prompts: 2000 from MMLU-Pro test
Sentences: 41285
d_model: 2048
Activation dtype: float16
Purpose
Replication of Venhoff et al. 2025 (arXiv:2510.07364) "Base Models Know How to Reason, Thinking Models Learn When" applied to… See the full description on the dataset page: https://huggingface.co/datasets/caiovicentino1/qwen35-a3b-thinking-traces.thinking-traces-sft-100k
Thinking Traces SFT (100K)
100,000 ShareGPT-format conversations where the assistant shows explicit extended reasoning in <thinking> tags before giving a clean, structured final answer. Designed for training R1/o1-style reasoning models that separate the internal scratchpad from the public response.
Motivation
Standard SFT datasets train models to output correct answers. This dataset trains models to reason correctly — showing the full deliberation process before… See the full description on the dataset page: https://huggingface.co/datasets/stindardlogic/thinking-traces-sft-100k.