datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
AIME_2000_2026_Kimi_K3
AIME 2000–2026 — Kimi K3 reasoning traces
🔄 Changelog
2026-08-08 — full re-generation. All reasoning traces were regenerated from scratch and re-verified against the official answer key.
New schema — added gen_attempts_low, gen_attempts_high; renamed gen_parsed_answer → gen_answer_int and answer_note → problem_note; removed gen_effort, gen_pass1.
New generation — only use the bare problem (v1 appended an "ANSWER:" format instruction), so traces are cleaner.… See the full description on the dataset page: https://huggingface.co/datasets/bevangelista/AIME_2000_2026_Kimi_K3.Kimi-K2.5-Reasoning-1M-Cleaned
🪐 Kimi-K2.5-Reasoning-1M-Cleaned
Kimi-K2.5-Reasoning-1M-Cleaned is a cleaned derivative of ianncity/KIMI-K2.5-1000000x. It preserves the original four-config layout from the source dataset and rewrites each record into a unified reasoning-SFT schema with id, conversations, input, output, domain, and meta.
Summary
Source dataset: ianncity/KIMI-K2.5-1000000x
Source author: ianncity
Teacher model recorded in meta.teacher_model: KIMI-K2.5
Token lengths computed with… See the full description on the dataset page: https://huggingface.co/datasets/Jackrong/Kimi-K2.5-Reasoning-1M-Cleaned.KIMI-K2.5-1000000x
KIMI-K2.5-1000000x
1,000,000 reasoning traces distilled from KIMI-K2.5 on high reasoning, (Each subset has different questions)
Distribution:
Coding: 50% (Includes: Webdev, Python, C++, Java, JS, C, Ruby, Lua, Rust, and C#)
Science: 20% (Physics, Chemistry, Biology) - 100k more completions in the PHD-Science subset
Math: 15% (Algebra, Calculus, Probability) - 200k more completions in kimiMath200k.jsonl
Computer Science: 5%
Logical Questions: 5%
Creative Writing: 5%… See the full description on the dataset page: https://huggingface.co/datasets/ianncity/KIMI-K2.5-1000000x.promqa-cooking-frames
Dataset Card
This repository contains pre-sampled frames of CaptainCook4D for ProMQA dataset.
Dataset Details
Please check the following for more details.
GitHub: https://github.com/kimihiroh/promqa-cooking
Paper: https://aclanthology.org/2025.naacl-long.579/
Citation
@inproceedings{hasegawa-etal-2025-promqa,
title={ProMQA: Question Answering Dataset for Multimodal Procedural Activity Understanding},
author={Hasegawa, Kimihiro and Imrattanatrai… See the full description on the dataset page: https://huggingface.co/datasets/kimihiroh/promqa-cooking-frames.promqa-assembly
Dataset Card for Dataset Name
This is the ProMQA-Assembly dataset.
Dataset Details
Dataset Description
This is the ProMQA-Assembly dataset, which is a multimodal question answering dataset. Instruction task graphs are available in assembly101-graph
Curated by: Kimihiro Hasegawa
Language(s) (NLP): English
License: CC BY-NC 4.0
Dataset Sources
Repository: https://github.com/assembly-101
Uses
Direct Use… See the full description on the dataset page: https://huggingface.co/datasets/kimihiroh/promqa-assembly.promqa-assembly-frames
Dataset Card
This repository contains pre-sampled frames of Assembly101 for ProMQA-Assembly dataset.
Dataset Details
Please check the following for more details.
GitHub: https://github.com/kimihiroh/promqa-assembly
Paper: https://arxiv.org/abs/2509.02949
Citation
@article{hasegawa-etal-2025-promqa-assembly,
title={ProMQA-Assembly: Multimodal Procedural QA Dataset on Assembly},
author={Hasegawa, Kimihiro and Imrattanatrai, Wiradee and Asada, Masaki… See the full description on the dataset page: https://huggingface.co/datasets/kimihiroh/promqa-assembly-frames.Kimi-K2.5-Reasoning-1M-Cleaned
🪐 Kimi-K2.5-Reasoning-1M-Cleaned
Kimi-K2.5-Reasoning-1M-Cleaned is a cleaned derivative of ianncity/KIMI-K2.5-1000000x. It preserves the original four-config layout from the source dataset and rewrites each record into a unified reasoning-SFT schema with id, conversations, input, output, domain, and meta.
Summary
Source dataset: ianncity/KIMI-K2.5-1000000x
Source author: ianncity
Teacher model recorded in meta.teacher_model: KIMI-K2.5
Token lengths computed with… See the full description on the dataset page: https://huggingface.co/datasets/rAVEUK/Kimi-K2.5-Reasoning-1M-Cleaned.Kimi-K2.5-Reasoning-1M-Cleaned
🪐 Kimi-K2.5-Reasoning-1M-Cleaned
Kimi-K2.5-Reasoning-1M-Cleaned is a cleaned derivative of ianncity/KIMI-K2.5-1000000x. It preserves the original four-config layout from the source dataset and rewrites each record into a unified reasoning-SFT schema with id, conversations, input, output, domain, and meta.
Summary
Source dataset: ianncity/KIMI-K2.5-1000000x
Source author: ianncity
Teacher model recorded in meta.teacher_model: KIMI-K2.5
Token lengths computed… See the full description on the dataset page: https://huggingface.co/datasets/JBrightmanAI/Kimi-K2.5-Reasoning-1M-Cleaned.AIME_1983_2026_Kimi_K3
AIME 1983–2026 — Kimi K3 reasoning traces
🔄 Changelog
2026-08-08 — full re-generation. All reasoning traces were regenerated from scratch and re-verified against the official answer key.
New schema — added gen_attempts_low, gen_attempts_high; renamed gen_parsed_answer → gen_answer_int and answer_note → problem_note; removed gen_effort, gen_pass1.
New generation — only use the bare problem (v1 appended an "ANSWER:" format instruction), so traces are cleaner.… See the full description on the dataset page: https://huggingface.co/datasets/bevangelista/AIME_1983_2026_Kimi_K3.Kimi-K2.6-Reasoning-3300x-WandB
Kimi-K2.6-Reasoning-3300x-WandB
Kimi-K2.6-Reasoning-3300x-WandB is a W&B-only synthetic reasoning dataset generated with Kimi-K2.6 through Weights & Biases Inference.
This dataset is the pure W&B-generated subset from a larger planned 8,000-example Kimi reasoning distillation run. Generation stopped when the W&B quota limit was reached, and the completed accepted rows were audited, cleaned, and exported as a standalone dataset.
This release contains 3,303 accepted W&B-generated rows… See the full description on the dataset page: https://huggingface.co/datasets/trjxter/Kimi-K2.6-Reasoning-3300x-WandB.KIMI-K2.5-1000000-RU
KIMI-K2.5-1000000x
1,000,000 reasoning traces distilled from KIMI-K2.5 on high reasoning, (Each subset has different questions)
Distribution:
Coding: 50% (Includes: Webdev, Python, C++, Java, JS, C, Ruby, Lua, Rust, and C#)
Science: 20% (Physics, Chemistry, Biology) - 100k more completions in the PHD-Science subset
Math: 15% (Algebra, Calculus, Probability) - 200k more completions in kimiMath200k.jsonl
Computer Science: 5%
Logical Questions: 5%
Creative Writing: 5%… See the full description on the dataset page: https://huggingface.co/datasets/YurinKO/KIMI-K2.5-1000000-RU.KimiK2.5-2000x
Kimi K2.5 9000x Dataset
Dataset Description
This dataset contains 2144 high-quality samples generated using Kimi K2.5 model, covering diverse tasks including code generation, mathematical reasoning, and general problem-solving.
Dataset Summary
Total Samples: 2144
Model: Kimi K2.5
Languages: English
Format: JSON
License: Apache 2.0
Task Distribution
The dataset includes samples across multiple domains:
Code Generation: Programming… See the full description on the dataset page: https://huggingface.co/datasets/Crownelius/KimiK2.5-2000x.KIMI-K2.5-1000000x
KIMI-K2.5-1000000x
1,000,000 reasoning traces distilled from KIMI-K2.5 on high reasoning, (Each subset has different questions)
Distribution:
Coding: 50% (Includes: Webdev, Python, C++, Java, JS, C, Ruby, Lua, Rust, and C#)
Science: 20% (Physics, Chemistry, Biology) - 100k more completions in the PHD-Science subset
Math: 15% (Algebra, Calculus, Probability) - 200k more completions in kimiMath200k.jsonl
Computer Science: 5%
Logical Questions: 5%
Creative Writing: 5%… See the full description on the dataset page: https://huggingface.co/datasets/nick007x/KIMI-K2.5-1000000x.Kimi-K2.5-Reasoning-Reduced-Luna
Kimi K2.5 Reasoning Reduced with GPT-5.6 Luna
This dataset contains synthetic, lossy compressions of reasoning traces from
Jackrong/Kimi-K2.5-Reasoning-1M-Cleaned,
configuration General-Distillation.
The final-answer suffix is copied programmatically from the source and is not
regenerated by the model. The compressed reasoning is synthetic and is not
guaranteed to preserve every logical detail.
Training columns
Train on conversations_reduced or output_reduced. The… See the full description on the dataset page: https://huggingface.co/datasets/Miska25/Kimi-K2.5-Reasoning-Reduced-Luna.kimi-k2.5-reasoning-1m-cleaned
🪐 Kimi-K2.5-Reasoning-1M-Cleaned
Kimi-K2.5-Reasoning-1M-Cleaned is a cleaned derivative of ianncity/KIMI-K2.5-1000000x. It preserves the original four-config layout from the source dataset and rewrites each record into a unified reasoning-SFT schema with id, conversations, input, output, domain, and meta.
Summary
Source dataset: ianncity/KIMI-K2.5-1000000x
Source author: ianncity
Teacher model recorded in meta.teacher_model: KIMI-K2.5
Token lengths computed… See the full description on the dataset page: https://huggingface.co/datasets/ansulev/kimi-k2.5-reasoning-1m-cleaned.Kimi-K2.6-Thinking-200x
Dataset Card (Kimi-K2.6-Thinking-200x)
Dataset Summary
Kimi-K2.6-Reasoning-207 is a high-quality distilled reasoning dataset designed for supervised fine-tuning (SFT) of small language models.
This dataset uses a curated seed question set covering Mathematics, Code, Logic, Science, Analysis, and Instruction-following domains. By calling the Kimi-K2.6 model via the Moonshot AI API as the teacher model, it generates high-quality responses featuring long-form step-by-step… See the full description on the dataset page: https://huggingface.co/datasets/uniquealexx/Kimi-K2.6-Thinking-200x.KIMI-K2.5-Reasoning
OctoMed/KIMI-K2.5-Reasoning
Multi-turn chain-of-thought conversations converted to OctoMed format for SFT training.
Source
Derived from ianncity/KIMI-K2.5-1000000x
by ianncity. All credit for the original data collection and
distillation goes to the original authors.
Format
Each example contains:
question: User question
responses: the final gpt turn repeated for compatibility with the OctoMed pipeline
Kimi-K2.6-Technical-Reasoning-AddOn-3300x
Kimi-K2.6-Technical-Reasoning-AddOn-3300x
This dataset is a technical reasoning add-on dataset generated with Kimi K2.6 as the teacher model.
The dataset was designed as an additional technical reasoning trace set for downstream SFT experiments, especially around math, graduate-level science, coding, and debugging/code-repair style prompts.
Dataset Summary
Dataset name: Kimi-K2.6-Technical-Reasoning-AddOn-3300x
Teacher model: Kimi-K2.6
Backend: W&B… See the full description on the dataset page: https://huggingface.co/datasets/trjxter/Kimi-K2.6-Technical-Reasoning-AddOn-3300x.Kimi-K3-And-DeepSeek-V4-Pro-0813-Distillation-in-PT-BR
🇧 Destilação PT-BR com Raciocínio (Chain-of-Thought)
Este dataset contém exemplos de alta qualidade gerados através da destilação de modelos de ponta (Teacher Models) disponíveis via NVIDIA NIM, focados em instrução, raciocínio lógico e naturalidade em Português Brasileiro (PT-BR).
O grande diferencial deste dataset é a inclusão explícita do processo de pensamento (Chain-of-Thought / thinking) dos modelos professores, permitindo treinar modelos menores (Student Models) não… See the full description on the dataset page: https://huggingface.co/datasets/Davizig10jojo/Kimi-K3-And-DeepSeek-V4-Pro-0813-Distillation-in-PT-BR.Kimi-K2.5-Reasoning-1M-Cleaned
🪐 Kimi-K2.5-Reasoning-1M-Cleaned
Kimi-K2.5-Reasoning-1M-Cleaned is a cleaned derivative of ianncity/KIMI-K2.5-1000000x. It preserves the original four-config layout from the source dataset and rewrites each record into a unified reasoning-SFT schema with id, conversations, input, output, domain, and meta.
Summary
Source dataset: ianncity/KIMI-K2.5-1000000x
Source author: ianncity
Teacher model recorded in meta.teacher_model: KIMI-K2.5
Token lengths computed with… See the full description on the dataset page: https://huggingface.co/datasets/EngMuhammadAtef/Kimi-K2.5-Reasoning-1M-Cleaned.KIMI-K2.5-1000000x
KIMI-K2.5-1000000x
1,000,000 reasoning traces distilled from KIMI-K2.5 on high reasoning, (Each subset has different questions)
Distribution:
Coding: 50% (Includes: Webdev, Python, C++, Java, JS, C, Ruby, Lua, Rust, and C#)
Science: 20% (Physics, Chemistry, Biology) - 100k more completions in the PHD-Science subset
Math: 15% (Algebra, Calculus, Probability) - 200k more completions in kimiMath200k.jsonl
Computer Science: 5%
Logical Questions: 5%
Creative Writing: 5%… See the full description on the dataset page: https://huggingface.co/datasets/WWX0825/KIMI-K2.5-1000000x.AIME_2026_Kimi_K3
AIME 2026 — Kimi K3 reasoning traces
🔄 Changelog
2026-08-08 — full re-generation. All reasoning traces were regenerated from scratch and re-verified against the official answer key.
New schema — added gen_attempts_low, gen_attempts_high; renamed gen_parsed_answer → gen_answer_int and answer_note → problem_note; removed gen_effort, gen_pass1.
New generation — only use the bare problem (v1 appended an "ANSWER:" format instruction), so traces are cleaner.
AIME… See the full description on the dataset page: https://huggingface.co/datasets/bevangelista/AIME_2026_Kimi_K3.kimi-k2-0905-20M
20M token synthetic instruction dataset (Kimi 0905)
User prompts are extracted from three curated instruction-following datasets. Low-quality and repetitive prompts are identified and removed or rewritten using Gemini 3 Flash (+adding medatada for each message). The resulting 15,825 filtered user prompts are sent to Kimi K2 0905 to generate high-quality synthetic responses.
Difficulty Split
Medium: 48.3% (7,638)
Hard: 27.6% (4,373) — mostly from… See the full description on the dataset page: https://huggingface.co/datasets/xrist0bg/kimi-k2-0905-20M.KIMI-K2.5-1000000x
KIMI-K2.5-1000000x
1,000,000 reasoning traces distilled from KIMI-K2.5 on high reasoning, (Each subset has different questions)
Distribution:
Coding: 50% (Includes: Webdev, Python, C++, Java, JS, C, Ruby, Lua, Rust, and C#)
Science: 20% (Physics, Chemistry, Biology) - 100k more completions in the PHD-Science subset
Math: 15% (Algebra, Calculus, Probability) - 200k more completions in kimiMath200k.jsonl
Computer Science: 5%
Logical Questions: 5%
Creative Writing: 5%… See the full description on the dataset page: https://huggingface.co/datasets/bitsydarel/KIMI-K2.5-1000000x.KIMI-K2.5-1000000x
KIMI-K2.5-1000000x
1,000,000 reasoning traces distilled from KIMI-K2.5 on high reasoning, (Each subset has different questions)
Distribution:
Coding: 50% (Includes: Webdev, Python, C++, Java, JS, C, Ruby, Lua, Rust, and C#)
Science: 20% (Physics, Chemistry, Biology) - 100k more completions in the PHD-Science subset
Math: 15% (Algebra, Calculus, Probability) - 200k more completions in kimiMath200k.jsonl
Computer Science: 5%
Logical Questions: 5%
Creative Writing: 5%… See the full description on the dataset page: https://huggingface.co/datasets/TheDrMoniker/KIMI-K2.5-1000000x.AIME_2025_Kimi_K3
AIME 2025 — Kimi K3 reasoning traces
🔄 Changelog
2026-08-08 — full re-generation. All reasoning traces were regenerated from scratch and re-verified against the official answer key.
New schema — added gen_attempts_low, gen_attempts_high; renamed gen_parsed_answer → gen_answer_int and answer_note → problem_note; removed gen_effort, gen_pass1.
New generation — only use the bare problem (v1 appended an "ANSWER:" format instruction), so traces are cleaner.
AIME… See the full description on the dataset page: https://huggingface.co/datasets/bevangelista/AIME_2025_Kimi_K3.KIMI-K2.5-1000000x
KIMI-K2.5-1000000x
1,000,000 reasoning traces distilled from KIMI-K2.5 on high reasoning, (Each subset has different questions)
Distribution:
Coding: 50% (Includes: Webdev, Python, C++, Java, JS, C, Ruby, Lua, Rust, and C#)
Science: 20% (Physics, Chemistry, Biology) - 100k more completions in the PHD-Science subset
Math: 15% (Algebra, Calculus, Probability) - 200k more completions in kimiMath200k.jsonl
Computer Science: 5%
Logical Questions: 5%
Creative Writing: 5%… See the full description on the dataset page: https://huggingface.co/datasets/BhaweshSingh/KIMI-K2.5-1000000x.KIMI-K2.5-700000x
KIMI-K2.5-700000x
700,000 reasoning traces distilled from KIMI-K2.5 on high reasoning
Distribution:
Coding: 50% (Includes: Webdev, Python, C++, Java, JS, C, Ruby, Lua, Rust, and C#)
Science: 20% (Physics, Chemistry, Biology) - 100k more completions in the PHD-Science subset
Math: 15% (Algebra, Calculus, Probability)
Computer Science: 5%
Logical Questions 5%
Creative Writing: 5%
Token Count: 2.5B
[!NOTE]
Data Collection
Collected using a modified Datagen… See the full description on the dataset page: https://huggingface.co/datasets/Alptekinege/KIMI-K2.5-700000x.KIMI-K2.5-1000000x
KIMI-K2.5-1000000x
1,000,000 reasoning traces distilled from KIMI-K2.5 on high reasoning, (Each subset has different questions)
Distribution:
Coding: 50% (Includes: Webdev, Python, C++, Java, JS, C, Ruby, Lua, Rust, and C#)
Science: 20% (Physics, Chemistry, Biology) - 100k more completions in the PHD-Science subset
Math: 15% (Algebra, Calculus, Probability) - 200k more completions in kimiMath200k.jsonl
Computer Science: 5%
Logical Questions: 5%
Creative Writing: 5%… See the full description on the dataset page: https://huggingface.co/datasets/invincible-jha/KIMI-K2.5-1000000x.KIMI-K2.5-450000x
KIMI-K2.5-450000x
450,000 reasoning traces distilled from KIMI-K2.5 on high reasoning
Distribution:
Coding: 60% (Includes: Webdev, Python, C++, Java, JS, C, Ruby, Lua, Rust, and C#)
Science: 15% (Physics, Chemistry, Biology)
Math: 10% (Algebra, Calculus, Probability)
Computer Science: 5%
Logical Questions 5%
Creative Writing: 5%
Token Count: 1.8B
[!NOTE]
Data Collection
Collected using a modified Datagen by TeichAI, over the course of about (20) hours… See the full description on the dataset page: https://huggingface.co/datasets/LIwenjun-123/KIMI-K2.5-450000x.