datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
nemotron-student-fail-v41-clean-thinking
Nemotron-fail / DeepSeek-V4.1 clean and action-only trajectories
DeepSeek-V4.1 reward-1 trajectories for tasks on which the Nemotron student
did not obtain reward 1. This release was rebuilt from the complete reward-1
audit under v54-high-precision-canonical-reconstruction-relations.
Training paths
Path
Rows
Unique tasks
Thinking
Use
data/strict/train.jsonl.gz
12
12
Preserved and clean
Raw-thinking SFT
data/hybrid/train.jsonl.gz
58
58
Only… See the full description on the dataset page: https://huggingface.co/datasets/zhiyuanhucs/nemotron-student-fail-v41-clean-thinking.llm-jp-4-thinking-sft-data-chatmlllm-jpのデータセットllm-jp-4-thinking-sft-dataを、
ChatML形式に変換したものです。
ライセンス
各サンプルのライセンスは、元データセットカードに記載された各データソースのライセンスに従います。
本リポジトリは、元となったデータ全体に対して新たなライセンスを付与するものではありません。
利用する場合は、対応する元データソースのライセンス条件を確認してください。
epic-thinking
Source
Rows
glaiveai/reasoning-v1-20m
1 999 793
BAAI/OpenSeek-Synthetic-Reasoning-Data-Examples CC
1 267 534
PrimeIntellect/INTELLECT-3-SFT openreasoning_science
1 000 000
PrimeIntellect/INTELLECT-3-SFT am_chat
852 816
nvidia/Nemotron-Cascade-SFT-Stage-1 general
583 612
open-thoughts/OpenThoughts2-1M
541 898
PrimeIntellect/SYNTHETIC-1-SFT-Data
474 810
allenai/Dolci-Think-SFT-7B
334 908
allenai/Dolci-Think-SFT-32B
327 491
GeneralReasoning/GeneralThought-430K
291 946… See the full description on the dataset page: https://huggingface.co/datasets/agentlans/epic-thinking.medra-thinking-768grade_school_math_thinkingthinking-cap-tier-curricula-complete
Thinking Cap Tier Curricula — Complete Reasoning Alignment Suite (TCS v4)
[!IMPORTANT]
Dataset Release v1.2 (Sept 2026) — Clean Delimiters & Zero-Padding Architecture:
In v1.2, all 13,477 SFT samples and 3,187 SimPO preference pairs have undergone an automated token purge:
Zero <|pad|> batch residues: 100% eliminated across all files.
Zero reasoning leakage into final answers: Deliberation stays strictly inside <think>...</think>, and answers provide direct, non-repetitive… See the full description on the dataset page: https://huggingface.co/datasets/Davd-b01/thinking-cap-tier-curricula-complete.claude_opus_4.8_max_thinking_5k_v2
Claude Opus 4.8 MAX THINKING — Distillation Dataset
5,000 high-quality examples designed to distill the maximum-effort reasoning, honest analysis, production software engineering, and agentic capabilities of Claude Opus 4.8.
Overview
This dataset captures Opus 4.8’s signature strengths:
Deep, structured, high-effort reasoning
Honest communication about trade-offs and uncertainties
Excellent production software engineering judgment
Strong agentic workflow design… See the full description on the dataset page: https://huggingface.co/datasets/11-47/claude_opus_4.8_max_thinking_5k_v2.Nieto-2022-ThinkingOutLoudOpenAccessEEGBasedBCIDatasetInnerSpeech
Thinking out loud: an open-access EEG-based BCI dataset for inner speech recognition
This is an unofficial mirror of OpenNeuro dataset ds003626, version
2.1.2. It is not affiliated with or endorsed by the dataset authors, their
institutions, or OpenNeuro.
Source and documentation
Original dataset: OpenNeuro ds003626 v2.1.2
Article: Nieto et al., Scientific Data (2022)
Original dataset documentation: README
Official analysis code: N-Nieto/Inner_Speech_Dataset
The… See the full description on the dataset page: https://huggingface.co/datasets/raei/Nieto-2022-ThinkingOutLoudOpenAccessEEGBasedBCIDatasetInnerSpeech.ablation_nemotron_no_thinking
Dataset: ablation_nemotron_no_thinking
This dataset was uploaded from /mnt/yulan_pretrain/mount/data_final_train/ablation_nemotron_no_thinking/stage_1/tmp.
thinking-cap-tier-lima-dense
Thinking Cap Tier Curricula — LIMA Hyper-Dense Reasoning Alignment Suite (TCS v4)
[!IMPORTANT]
Dataset Release v1.2 (Sept 2026) — Clean Delimiters & Zero-Padding Architecture:
In v1.2, all 5,500 SFT and 2,000 SimPO records have undergone a complete token purge:
Zero <|pad|> batch residues: 100% eliminated across all records.
Zero reasoning leakage into final answers: Deliberation stays strictly inside <think>...</think>, and answers provide direct conclusions.
Native ChatML… See the full description on the dataset page: https://huggingface.co/datasets/Davd-b01/thinking-cap-tier-lima-dense.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.optimal_thinking_benchThis dataset is released as part of OptimalOptimalThinkingBench research project.
IMPORTANT: This is only a subset of OptimalThinkingBench that does not contain the math problems. To download the full dataset, please refer to our project materials here for more details.
Loading the dataset with transformers
This dataset is built using Llama-4-Maverick and Reasoning-Gym. Details on how to generate this dataset can be found in OptimalOptimalThinkingBench paper.
Minimal example below… See the full description on the dataset page: https://huggingface.co/datasets/facebook/optimal_thinking_bench.thinkingcap-condensed-qwen3.8-glm5.2-kimi-k3
ThinkingCap Condensed — Qwen3.8 / GLM-5.2 / Kimi-K3
Condensed ThinkingCap-style reasoning traces for SFT.
1,985 traces: each row pairs a full multi-turn teacher trace (Qwen3.8-Max,
GLM-5.2 or Kimi K3, via
r0b0tlab/qwen3.8-max-glm5.2-kimi-k3-distillation)
with a condensed TC-style version (short <think> + definitive numbered
answer) generated by
bottlecapai/ThinkingCap-Qwen3.6-27B
using the thinkingcap system prompt.
Format: JSONL (data/condensed.jsonl), 1,985 rows, UTF-8.… See the full description on the dataset page: https://huggingface.co/datasets/Davd-b01/thinkingcap-condensed-qwen3.8-glm5.2-kimi-k3.gpt-oss-120b-mandarin-thinking-eval-logs-and-scoresgpt-oss-20b-mandarin-thinking-eval-logs-and-scoresgsm8k-thinkingGSM8K with a reasoning/thinking/reflecting done by:
new: llama3.1-405b
old: chatgpt-4o-lastest
the new one is not complete. [at 50~% as of now]
CT-RATE-Thinking
CT-RATE-Thinking: Reasoning-Augmented CT Report Dataset
🎉🎉🎉 Our paper was accepted at the 28th conference of The Medical Image Computing and Computer Assisted Intervention Society (MICCAI). See you in Daejeon, Korea, September 23–27, 2025.CT-RATE-Thinking is a reasoning-augmented dataset derived from CT-RATE, containing chain-of-thought VQA pairs and report-level thinking narratives for 3D chest CT volumes.
It was generated as part of the μ²Tokenizer project… See the full description on the dataset page: https://huggingface.co/datasets/AlpachinoNLP/CT-RATE-Thinking.thinkingcap-reasoning-traces
ThinkingCap Reasoning Traces (Legacy v1 Prototype)
[!WARNING]
Legacy / Deprecated Prototype Notice (v1):
This dataset represents an early exploratory prototype (v1, 4,254 traces) from initial development.
Some samples in this legacy version contain early formatting artifacts, including reasoning traces leaking into the final answer field and informal step-by-step breakdowns.
For modern post-training, SFT, and SimPO alignment under the TCS v4 cognitive standard, please use our… See the full description on the dataset page: https://huggingface.co/datasets/Davd-b01/thinkingcap-reasoning-traces.normistral-11b-thinking-evaluationChinese-Qwen3-235B-Thinking-2507-Distill-100k
📌 Note: The English translation of this dataset card is provided below.
Chinese-Qwen3-235B-Thinking-2507-Distill-100k
Dataset Summary
Chinese-Qwen3-235B-Thinking-2507-Distill-100k 是一个包含约 100k 条高质量中文推理与指令数据的数据集，由 Qwen-3-235B-A22B-Thinking-2507（官方 Thinking 模式，上下文长度 32K）蒸馏生成。
该数据集覆盖了多个重要领域：
数学与工程任务（Mathematics, Applied Math, Advanced Math）
通用知识与写作（General Knowledge, Language & Writing）
技术与编程（Technology & Programming）
商业与经济（Business & Economics）… See the full description on the dataset page: https://huggingface.co/datasets/Jackrong/Chinese-Qwen3-235B-Thinking-2507-Distill-100k.TeichAI-thinking-reasoning-x
TeichAI Thinking & Reasoning Datasets
A collection of prompts answered by large language models (LLMs) such as Google Gemini and OpenAI ChatGPT, with long-form reasoning enabled.
These datasets were originally created by TeichAI for distillation and reasoning-focused training workflows.
Schema
Each row in the dataset has the following fields:
question_hash: Truncated, base64-encoded MD5 hash of the question, useful for filtering and deduplication.
question: The… See the full description on the dataset page: https://huggingface.co/datasets/agentlans/TeichAI-thinking-reasoning-x.Qwen3.8-27B-Thinking-SecOPD-trainset
Qwen3.6-27B-Thinking SecOPD Trainset
Dataset summary
This public dataset contains 19,155 complete, model-specific preference records
for offline adversarial training against indirect prompt injection. The corpus
starts from the 19,157-record
Sizhe-Chen/Qwen3.6-27B-Instruct-SecPO-trainset
release. Its six non-label lineage fields are retained, while the attacked
prompts are rendered for thinking-on generation and the chosen and rejected
labels are regenerated… See the full description on the dataset page: https://huggingface.co/datasets/Sizhe-Chen/Qwen3.8-27B-Thinking-SecOPD-trainset.GLM-5.2-FP8-nemotron-codealpaca-thinking
GLM-5.2-FP8 Nemotron-CodeAlpaca Thinking Dataset
820,790 single-turn conversations generated by zai-org/GLM-5.2-FP8
with thinking enabled.
Prompt source
Rows (public)
Nemotron-Post-Training-Dataset-v2
800,944
CodeAlpaca-20k (corrected prompts, instruction + "\n\n" + input)
19,846
Total
820,790
Generation: temperature=1.0, top_p=0.95, max_tokens=24576, thinking
enabled. The CodeAlpaca prompts here include the input field.
Relationship to… See the full description on the dataset page: https://huggingface.co/datasets/JessieWei/GLM-5.2-FP8-nemotron-codealpaca-thinking.OpenThought3-Qwen3-4BOpenThought3-Qwen3-4B
OpenThought3-Qwen3-4B is a math reasoning supervised fine-tuning dataset in chat-message JSONL format.
Data Creation and Cleaning
This dataset was generated by Qwen3-4B (Non-thinking) from math-domain prompts selected from OpenThoughts3-1.2M. The generated responses were cleaned through deduplication, removal of degenerate repetition/repeater-style outputs, and template checks on the assistant… See the full description on the dataset page: https://huggingface.co/datasets/Thinking-Space/OpenThought3-Qwen3-4B.Identify_true_decorative_thinkingViCA-thinking-2.68k
ViCA-Thinking-2.68K
Quickstart
You can load our dataset using the following code:
from datasets import load_dataset
vica_thinking = load_dataset("nkkbr/ViCA-thinking-2.68k")
Overview
This is the dataset we created to further fine-tune the ViCA model. Our motivation stems from the observation that, after being trained on large-scale visuospatial instruction data (e.g., ViCA-322K), ViCA tends to output final answers directly without any intermediate… See the full description on the dataset page: https://huggingface.co/datasets/nkkbr/ViCA-thinking-2.68k.r8-thinking-fix-sft
⚠️ CRITICAL: Ollama Inference Flag Required for derived models
If you train or serve any Qwen3.5-9B-derived model from this lineage via Ollama,
you MUST pass "think": false in /api/chat requests for chat / instruction following / tool use.
The qwen3.5 RENDERER auto-injects <think> tags causing 25-46% empty-answer rates without this flag.
See dataset cudabenchmarktest/r9-research-framework/_OLLAMA_INFERENCE_WARNING.md for the full lesson learned.
R8 Thinking-Fix SFT… See the full description on the dataset page: https://huggingface.co/datasets/cudabenchmarktest/r8-thinking-fix-sft.pusht_96_norm4_visual_nomarker_allstep_thinking_trickiness_cot
PushT norm4 Visual Nomarker All-Step Thinking Trickiness COT
This dataset is derived from successful PushT visual-nomarker trajectories in novastar112/pusht_96_norm4_visual_nomarker.
Each row contains one full successful trajectory from the first move through the final stop action.
Main files:
training/pusht_allstep_thinking_cot.jsonl.gz: 500,000 train rows.
testing/pusht_allstep_thinking_cot.jsonl.gz: 200 test rows.
metadata/final_scan_validation.json: full local scan after repair… See the full description on the dataset page: https://huggingface.co/datasets/novastar112/pusht_96_norm4_visual_nomarker_allstep_thinking_trickiness_cot.Dataset_of_Russian_thinkingRu
RTD
Описание:Russian Thinking Dataset — это набор данных, предназначенный для обучения и тестирования моделей обработки естественного языка (NLP) на русском языке. Датасет ориентирован на задачи, связанные с генерацией текста, анализом диалогов и решением математических и логических задач.
Основная информация:
Сплит: train
Количество записей: 147.046
Цели:
Обучение моделей пониманию русского языка.
Создание диалоговых систем с естественным взаимодействием.… See the full description on the dataset page: https://huggingface.co/datasets/qwqeqw/Dataset_of_Russian_thinking.mathvision-thinking