67aa021ced8d8663d42505cc

open-r1/OpenR1-Math-220k

open-r1

{"license": "apache-2.0", "language": ["en"], "configs": [{"config_name": "all", "data_files": [{"split": "default", "path": "data/train-*"}, {"split": "extended", "path": "extended/train-*"}]}, {"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}, {"config_name": "extended", "data_files": [{"split": "train", "path": "extended/train-*"}]}], "dataset_info": [{"config_name": "default", "features": [{"name": "problem", "dtype": "string"}, {"name": "solution", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "problem_type", "dtype": "string"}, {"name": "question_type", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "uuid", "dtype": "string"}, {"name": "is_reasoning_complete", "sequence": "bool"}, {"name": "generations", "sequence": "string"}, {"name": "correctness_math_verify", "sequence": "bool"}, {"name": "correctness_llama", "sequence": "bool"}, {"name": "finish_reasons", "sequence": "string"}, {"name": "correctness_count", "dtype": "int64"}, {"name": "messages", "list": [{"name": "from", "dtype": "string"}, {"name": "value", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 4964543630, "num_examples": 93733}], "download_size": 2149879429, "dataset_size": 4964543630}, {"config_name": "extended", "features": [{"name": "problem", "dtype": "string"}, {"name": "solution", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "problem_type", "dtype": "string"}, {"name": "question_type", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "uuid", "dtype": "string"}, {"name": "is_reasoning_complete", "sequence": "bool"}, {"name": "generations", "sequence": "string"}, {"name": "correctness_math_verify", "sequence": "bool"}, {"name": "correctness_llama", "sequence": "bool"}, {"name": "finish_reasons", "sequence": "string"}, {"name": "correctness_count", "dtype": "int64"}, {"name": "messages", "list": [{"name": "from", "dtype": "string"}, {"name": "value", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 4769566486, "num_examples": 131396}], "download_size": 2063805103, "dataset_size": 4769566486}]}

05f30409d9ac7656dd446305aa60f38b7ac35c27

OpenR1-Math-220k Dataset description OpenR1-Math-220k is a large-scale dataset for mathematical reasoning. It consists of 220k math problems with two to four reasoning traces generated by DeepSeek R1 for problems from NuminaMath 1.5. The traces were verified using Math Verify for most samples and Llama-3.3-70B-Instruct as a judge for 12% of the samples, and each problem contains at least one reasoning trace with a correct answer. The dataset consists of two splits:… See the full description on the dataset page: https://huggingface.co./datasets/open-r1/OpenR1-Math-220k.

6797e648de960c48ff034e54

open-thoughts/OpenThoughts-114k

open-thoughts

{"dataset_info": [{"config_name": "default", "features": [{"name": "system", "dtype": "string"}, {"name": "conversations", "list": [{"name": "from", "dtype": "string"}, {"name": "value", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 2635015668, "num_examples": 113957}], "download_size": 1078777193, "dataset_size": 2635015668}, {"config_name": "metadata", "features": [{"name": "problem", "dtype": "string"}, {"name": "deepseek_reasoning", "dtype": "string"}, {"name": "deepseek_solution", "dtype": "string"}, {"name": "ground_truth_solution", "dtype": "string"}, {"name": "domain", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "test_cases", "dtype": "string"}, {"name": "starter_code", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 5525214077.699433, "num_examples": 113957}], "download_size": 2469729724, "dataset_size": 5525214077.699433}], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}, {"config_name": "metadata", "data_files": [{"split": "train", "path": "metadata/train-*"}]}], "tags": ["curator", "synthetic"], "license": "apache-2.0"}

2e11184a4b5b3e777d85be23fb539900936495f4

Open-Thoughts-114k Open synthetic reasoning dataset with 114k high-quality examples covering math, science, code, and puzzles! Available Subsets default subset containing ready-to-train data used to finetune the OpenThinker-7B and OpenThinker-32B models: ds = load_dataset("open-thoughts/OpenThoughts-114k", split="train") metadata subset containing extra columns used in dataset construction: problem ground_truth_solution deepseek_reasoning deepseek_solution… See the full description on the dataset page: https://huggingface.co./datasets/open-thoughts/OpenThoughts-114k.

67a4af5e4ccbc3656f0b4c7c

saiyan-world/Goku-MovieGenBench

saiyan-world

{"task_categories": ["text-to-video"]}

fd41363957a6bf5370e573e422fc89e4ec450218

This repository contains the data associated with the paper Goku: Flow Based Video Generative Foundation Models. Project page: https://saiyan-world.github.io/goku/

67a404bc8c6d42c5ec097433

Anthropic/EconomicIndex

Anthropic

{"license": "mit", "pretty_name": "EconomicIndex", "tags": ["text"], "viewer": true, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "onet_task_mappings.csv"}]}]}

218b35116baa43c55beffe61f243bd81f5f84cf8

Overview This directory contains O*NET task mapping and automation vs. augmentation data from "Which Economic Tasks are Performed with AI? Evidence from Millions of Claude Conversations." The data and provided analysis are described below. Please see our blog post and paper for further visualizations and complete analysis. Data SOC_Structure.csv - Standard Occupational Classification (SOC) system hierarchy from the U.S. Department of Labor O*NET database… See the full description on the dataset page: https://huggingface.co./datasets/Anthropic/EconomicIndex.

63990f21cc50af73d29ecfa3

fka/awesome-chatgpt-prompts

fka

{"license": "cc0-1.0", "tags": ["ChatGPT"], "task_categories": ["question-answering"], "size_categories": ["100K<n<1M"]}

68ba7694e23014788dcc8ab5afe613824f45a05c

🧠 Awesome ChatGPT Prompts [CSV dataset] This is a Dataset Repository of Awesome ChatGPT Prompts View All Prompts on GitHub License CC-0

67a9f247188f29a956a34a04

AI-MO/NuminaMath-1.5

AI-MO

{"license": "apache-2.0", "task_categories": ["text-generation"], "language": ["en"], "tags": ["math", "post-training"], "pretty_name": "NuminaMath 1.5"}

649859605995b1d46eb29389ed9851782a47322e

Dataset Card for NuminaMath 1.5 Dataset Summary This is the second iteration of the popular NuminaMath dataset, bringing high quality post-training data for approximately 900k competition-level math problems. Each solution is formatted in a Chain of Thought (CoT) manner. The sources of the dataset range from Chinese high school math exercises to US and international mathematics olympiad competition problems. The data were primarily collected from online exam paper PDFs… See the full description on the dataset page: https://huggingface.co./datasets/AI-MO/NuminaMath-1.5.

67ac8c807ccaf131c3c68af7

open-r1/OpenR1-Math-Raw

open-r1

{"license": "apache-2.0", "language": ["en"], "dataset_info": {"features": [{"name": "problem", "dtype": "string"}, {"name": "solution", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "problem_type", "dtype": "string"}, {"name": "question_type", "dtype": "string"}, {"name": "problem_is_valid", "dtype": "string"}, {"name": "solution_is_valid", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "synthetic", "dtype": "bool"}, {"name": "generations", "sequence": "string"}, {"name": "generations_count", "dtype": "int64"}, {"name": "correctness_math_verify", "sequence": "bool"}, {"name": "correct_count", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 19144782231, "num_examples": 516499}], "download_size": 8022181704, "dataset_size": 19144782231}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

4cbd48dbec7e96690598221505860fd36b86d23f

OpenR1-Math-Raw Dataset description OpenR1-Math-Raw is a large-scale dataset for mathematical reasoning. It consists of 516k math problems sourced from AI-MO/NuminaMath-1.5 with 1 to 8 reasoning traces generated by DeepSeek R1. The traces were verified using Math Verify, but we recommend additionally annotating the correctness with LLM-as-judge for higher recall. The dataset contains: 516,499 problems 1,209,403 R1-generated solutions, with 2.3 solutions per problem on… See the full description on the dataset page: https://huggingface.co./datasets/open-r1/OpenR1-Math-Raw.

676f70846bf205795346d2be

FreedomIntelligence/medical-o1-reasoning-SFT

FreedomIntelligence

{"license": "apache-2.0", "task_categories": ["question-answering", "text-generation"], "language": ["en", "zh"], "tags": ["medical", "biology"], "configs": [{"config_name": "en", "data_files": "medical_o1_sft.json"}, {"config_name": "zh", "data_files": "medical_o1_sft_Chinese.json"}]}

4c9573e7de1e8660b88158db2efa7c7204bbd269

Introduction This dataset is used to fine-tune HuatuoGPT-o1, a medical LLM designed for advanced medical reasoning. This dataset is constructed using GPT-4o, which searches for solutions to verifiable medical problems and validates them through a medical verifier. For details, see our paper and GitHub repository. Citation If you find our data useful, please consider citing our work! @misc{chen2024huatuogpto1medicalcomplexreasoning, title={HuatuoGPT-o1, Towards… See the full description on the dataset page: https://huggingface.co./datasets/FreedomIntelligence/medical-o1-reasoning-SFT.

67474c06bd2f2f20b81faef1

zed-industries/zeta

zed-industries

{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "train.jsonl"}, {"split": "eval", "path": "eval.jsonl"}, {"split": "dpo", "path": "dpo.jsonl"}]}], "license": "apache-2.0"}

070f4d409068321e988cceca06479e5e0821303b

Dataset for Zeta This dataset is split into three parts: train.jsonl: Contains the training data for supervised fine-tuning. dpo.jsonl: Contains the data for the direct preference optimization. eval.jsonl: Contains the evaluation data for the Zeta dataset. These files are generated from the markdown files in the respective directories. Scripts There are several scripts to help with data processing and evaluation: script/pull-predictions: Pulls predictions from… See the full description on the dataset page: https://huggingface.co./datasets/zed-industries/zeta.

67a89e79556fa47a174b6c7b

agentica-org/DeepScaleR-Preview-Dataset

agentica-org

{"language": ["en"], "license": "mit", "size_categories": ["10K<n<100K"]}

b6ae8c60f5c1f2b594e2140b91c49c9ad0949e29

Data Our training dataset consists of approximately 40,000 unique mathematics problem-answer pairs compiled from: AIME (American Invitational Mathematics Examination) problems (1984-2023) AMC (American Mathematics Competition) problems (prior to 2023) Omni-MATH dataset Still dataset Format Each row in the JSON dataset contains: problem: The mathematical question text, formatted with LaTeX notation. solution: Offical solution to the problem, including LaTeX formatting… See the full description on the dataset page: https://huggingface.co./datasets/agentica-org/DeepScaleR-Preview-Dataset.

66212f29fb07c3e05ad0432e

HuggingFaceFW/fineweb

HuggingFaceFW

{"license": "odc-by", "task_categories": ["text-generation"], "language": ["en"], "pretty_name": "FineWeb", "size_categories": ["n>1T"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/*/*"}]}, {"config_name": "sample-10BT", "data_files": [{"split": "train", "path": "sample/10BT/*"}]}, {"config_name": "sample-100BT", "data_files": [{"split": "train", "path": "sample/100BT/*"}]}, {"config_name": "sample-350BT", "data_files": [{"split": "train", "path": "sample/350BT/*"}]}, {"config_name": "CC-MAIN-2024-51", "data_files": [{"split": "train", "path": "data/CC-MAIN-2024-51/*"}]}, {"config_name": "CC-MAIN-2024-46", "data_files": [{"split": "train", "path": "data/CC-MAIN-2024-46/*"}]}, {"config_name": "CC-MAIN-2024-42", "data_files": [{"split": "train", "path": "data/CC-MAIN-2024-42/*"}]}, {"config_name": "CC-MAIN-2024-38", "data_files": [{"split": "train", "path": "data/CC-MAIN-2024-38/*"}]}, {"config_name": "CC-MAIN-2024-33", "data_files": [{"split": "train", "path": "data/CC-MAIN-2024-33/*"}]}, {"config_name": "CC-MAIN-2024-30", "data_files": [{"split": "train", "path": "data/CC-MAIN-2024-30/*"}]}, {"config_name": "CC-MAIN-2024-26", "data_files": [{"split": "train", "path": "data/CC-MAIN-2024-26/*"}]}, {"config_name": "CC-MAIN-2024-22", "data_files": [{"split": "train", "path": "data/CC-MAIN-2024-22/*"}]}, {"config_name": "CC-MAIN-2024-18", "data_files": [{"split": "train", "path": "data/CC-MAIN-2024-18/*"}]}, {"config_name": "CC-MAIN-2024-10", "data_files": [{"split": "train", "path": "data/CC-MAIN-2024-10/*"}]}, {"config_name": "CC-MAIN-2023-50", "data_files": [{"split": "train", "path": "data/CC-MAIN-2023-50/*"}]}, {"config_name": "CC-MAIN-2023-40", "data_files": [{"split": "train", "path": "data/CC-MAIN-2023-40/*"}]}, {"config_name": "CC-MAIN-2023-23", "data_files": [{"split": "train", "path": "data/CC-MAIN-2023-23/*"}]}, {"config_name": "CC-MAIN-2023-14", "data_files": [{"split": "train", "path": "data/CC-MAIN-2023-14/*"}]}, {"config_name": "CC-MAIN-2023-06", "data_files": [{"split": "train", "path": "data/CC-MAIN-2023-06/*"}]}, {"config_name": "CC-MAIN-2022-49", "data_files": [{"split": "train", "path": "data/CC-MAIN-2022-49/*"}]}, {"config_name": "CC-MAIN-2022-40", "data_files": [{"split": "train", "path": "data/CC-MAIN-2022-40/*"}]}, {"config_name": "CC-MAIN-2022-33", "data_files": [{"split": "train", "path": "data/CC-MAIN-2022-33/*"}]}, {"config_name": "CC-MAIN-2022-27", "data_files": [{"split": "train", "path": "data/CC-MAIN-2022-27/*"}]}, {"config_name": "CC-MAIN-2022-21", "data_files": [{"split": "train", "path": "data/CC-MAIN-2022-21/*"}]}, {"config_name": "CC-MAIN-2022-05", "data_files": [{"split": "train", "path": "data/CC-MAIN-2022-05/*"}]}, {"config_name": "CC-MAIN-2021-49", "data_files": [{"split": "train", "path": "data/CC-MAIN-2021-49/*"}]}, {"config_name": "CC-MAIN-2021-43", "data_files": [{"split": "train", "path": "data/CC-MAIN-2021-43/*"}]}, {"config_name": "CC-MAIN-2021-39", "data_files": [{"split": "train", "path": "data/CC-MAIN-2021-39/*"}]}, {"config_name": "CC-MAIN-2021-31", "data_files": [{"split": "train", "path": "data/CC-MAIN-2021-31/*"}]}, {"config_name": "CC-MAIN-2021-25", "data_files": [{"split": "train", "path": "data/CC-MAIN-2021-25/*"}]}, {"config_name": "CC-MAIN-2021-21", "data_files": [{"split": "train", "path": "data/CC-MAIN-2021-21/*"}]}, {"config_name": "CC-MAIN-2021-17", "data_files": [{"split": "train", "path": "data/CC-MAIN-2021-17/*"}]}, {"config_name": "CC-MAIN-2021-10", "data_files": [{"split": "train", "path": "data/CC-MAIN-2021-10/*"}]}, {"config_name": "CC-MAIN-2021-04", "data_files": [{"split": "train", "path": "data/CC-MAIN-2021-04/*"}]}, {"config_name": "CC-MAIN-2020-50", "data_files": [{"split": "train", "path": "data/CC-MAIN-2020-50/*"}]}, {"config_name": "CC-MAIN-2020-45", "data_files": [{"split": "train", "path": "data/CC-MAIN-2020-45/*"}]}, {"config_name": "CC-MAIN-2020-40", "data_files": [{"split": "train", "path": "data/CC-MAIN-2020-40/*"}]}, {"config_name": "CC-MAIN-2020-34", "data_files": [{"split": "train", "path": "data/CC-MAIN-2020-34/*"}]}, {"config_name": "CC-MAIN-2020-29", "data_files": [{"split": "train", "path": "data/CC-MAIN-2020-29/*"}]}, {"config_name": "CC-MAIN-2020-24", "data_files": [{"split": "train", "path": "data/CC-MAIN-2020-24/*"}]}, {"config_name": "CC-MAIN-2020-16", "data_files": [{"split": "train", "path": "data/CC-MAIN-2020-16/*"}]}, {"config_name": "CC-MAIN-2020-10", "data_files": [{"split": "train", "path": "data/CC-MAIN-2020-10/*"}]}, {"config_name": "CC-MAIN-2020-05", "data_files": [{"split": "train", "path": "data/CC-MAIN-2020-05/*"}]}, {"config_name": "CC-MAIN-2019-51", "data_files": [{"split": "train", "path": "data/CC-MAIN-2019-51/*"}]}, {"config_name": "CC-MAIN-2019-47", "data_files": [{"split": "train", "path": "data/CC-MAIN-2019-47/*"}]}, {"config_name": "CC-MAIN-2019-43", "data_files": [{"split": "train", "path": "data/CC-MAIN-2019-43/*"}]}, {"config_name": "CC-MAIN-2019-39", "data_files": [{"split": "train", "path": "data/CC-MAIN-2019-39/*"}]}, {"config_name": "CC-MAIN-2019-35", "data_files": [{"split": "train", "path": "data/CC-MAIN-2019-35/*"}]}, {"config_name": "CC-MAIN-2019-30", "data_files": [{"split": "train", "path": "data/CC-MAIN-2019-30/*"}]}, {"config_name": "CC-MAIN-2019-26", "data_files": [{"split": "train", "path": "data/CC-MAIN-2019-26/*"}]}, {"config_name": "CC-MAIN-2019-22", "data_files": [{"split": "train", "path": "data/CC-MAIN-2019-22/*"}]}, {"config_name": "CC-MAIN-2019-18", "data_files": [{"split": "train", "path": "data/CC-MAIN-2019-18/*"}]}, {"config_name": "CC-MAIN-2019-13", "data_files": [{"split": "train", "path": "data/CC-MAIN-2019-13/*"}]}, {"config_name": "CC-MAIN-2019-09", "data_files": [{"split": "train", "path": "data/CC-MAIN-2019-09/*"}]}, {"config_name": "CC-MAIN-2019-04", "data_files": [{"split": "train", "path": "data/CC-MAIN-2019-04/*"}]}, {"config_name": "CC-MAIN-2018-51", "data_files": [{"split": "train", "path": "data/CC-MAIN-2018-51/*"}]}, {"config_name": "CC-MAIN-2018-47", "data_files": [{"split": "train", "path": "data/CC-MAIN-2018-47/*"}]}, {"config_name": "CC-MAIN-2018-43", "data_files": [{"split": "train", "path": "data/CC-MAIN-2018-43/*"}]}, {"config_name": "CC-MAIN-2018-39", "data_files": [{"split": "train", "path": "data/CC-MAIN-2018-39/*"}]}, {"config_name": "CC-MAIN-2018-34", "data_files": [{"split": "train", "path": "data/CC-MAIN-2018-34/*"}]}, {"config_name": "CC-MAIN-2018-30", "data_files": [{"split": "train", "path": "data/CC-MAIN-2018-30/*"}]}, {"config_name": "CC-MAIN-2018-26", "data_files": [{"split": "train", "path": "data/CC-MAIN-2018-26/*"}]}, {"config_name": "CC-MAIN-2018-22", "data_files": [{"split": "train", "path": "data/CC-MAIN-2018-22/*"}]}, {"config_name": "CC-MAIN-2018-17", "data_files": [{"split": "train", "path": "data/CC-MAIN-2018-17/*"}]}, {"config_name": "CC-MAIN-2018-13", "data_files": [{"split": "train", "path": "data/CC-MAIN-2018-13/*"}]}, {"config_name": "CC-MAIN-2018-09", "data_files": [{"split": "train", "path": "data/CC-MAIN-2018-09/*"}]}, {"config_name": "CC-MAIN-2018-05", "data_files": [{"split": "train", "path": "data/CC-MAIN-2018-05/*"}]}, {"config_name": "CC-MAIN-2017-51", "data_files": [{"split": "train", "path": "data/CC-MAIN-2017-51/*"}]}, {"config_name": "CC-MAIN-2017-47", "data_files": [{"split": "train", "path": "data/CC-MAIN-2017-47/*"}]}, {"config_name": "CC-MAIN-2017-43", "data_files": [{"split": "train", "path": "data/CC-MAIN-2017-43/*"}]}, {"config_name": "CC-MAIN-2017-39", "data_files": [{"split": "train", "path": "data/CC-MAIN-2017-39/*"}]}, {"config_name": "CC-MAIN-2017-34", "data_files": [{"split": "train", "path": "data/CC-MAIN-2017-34/*"}]}, {"config_name": "CC-MAIN-2017-30", "data_files": [{"split": "train", "path": "data/CC-MAIN-2017-30/*"}]}, {"config_name": "CC-MAIN-2017-26", "data_files": [{"split": "train", "path": "data/CC-MAIN-2017-26/*"}]}, {"config_name": "CC-MAIN-2017-22", "data_files": [{"split": "train", "path": "data/CC-MAIN-2017-22/*"}]}, {"config_name": "CC-MAIN-2017-17", "data_files": [{"split": "train", "path": "data/CC-MAIN-2017-17/*"}]}, {"config_name": "CC-MAIN-2017-13", "data_files": [{"split": "train", "path": "data/CC-MAIN-2017-13/*"}]}, {"config_name": "CC-MAIN-2017-09", "data_files": [{"split": "train", "path": "data/CC-MAIN-2017-09/*"}]}, {"config_name": "CC-MAIN-2017-04", "data_files": [{"split": "train", "path": "data/CC-MAIN-2017-04/*"}]}, {"config_name": "CC-MAIN-2016-50", "data_files": [{"split": "train", "path": "data/CC-MAIN-2016-50/*"}]}, {"config_name": "CC-MAIN-2016-44", "data_files": [{"split": "train", "path": "data/CC-MAIN-2016-44/*"}]}, {"config_name": "CC-MAIN-2016-40", "data_files": [{"split": "train", "path": "data/CC-MAIN-2016-40/*"}]}, {"config_name": "CC-MAIN-2016-36", "data_files": [{"split": "train", "path": "data/CC-MAIN-2016-36/*"}]}, {"config_name": "CC-MAIN-2016-30", "data_files": [{"split": "train", "path": "data/CC-MAIN-2016-30/*"}]}, {"config_name": "CC-MAIN-2016-26", "data_files": [{"split": "train", "path": "data/CC-MAIN-2016-26/*"}]}, {"config_name": "CC-MAIN-2016-22", "data_files": [{"split": "train", "path": "data/CC-MAIN-2016-22/*"}]}, {"config_name": "CC-MAIN-2016-18", "data_files": [{"split": "train", "path": "data/CC-MAIN-2016-18/*"}]}, {"config_name": "CC-MAIN-2016-07", "data_files": [{"split": "train", "path": "data/CC-MAIN-2016-07/*"}]}, {"config_name": "CC-MAIN-2015-48", "data_files": [{"split": "train", "path": "data/CC-MAIN-2015-48/*"}]}, {"config_name": "CC-MAIN-2015-40", "data_files": [{"split": "train", "path": "data/CC-MAIN-2015-40/*"}]}, {"config_name": "CC-MAIN-2015-35", "data_files": [{"split": "train", "path": "data/CC-MAIN-2015-35/*"}]}, {"config_name": "CC-MAIN-2015-32", "data_files": [{"split": "train", "path": "data/CC-MAIN-2015-32/*"}]}, {"config_name": "CC-MAIN-2015-27", "data_files": [{"split": "train", "path": "data/CC-MAIN-2015-27/*"}]}, {"config_name": "CC-MAIN-2015-22", "data_files": [{"split": "train", "path": "data/CC-MAIN-2015-22/*"}]}, {"config_name": "CC-MAIN-2015-18", "data_files": [{"split": "train", "path": "data/CC-MAIN-2015-18/*"}]}, {"config_name": "CC-MAIN-2015-14", "data_files": [{"split": "train", "path": "data/CC-MAIN-2015-14/*"}]}, {"config_name": "CC-MAIN-2015-11", "data_files": [{"split": "train", "path": "data/CC-MAIN-2015-11/*"}]}, {"config_name": "CC-MAIN-2015-06", "data_files": [{"split": "train", "path": "data/CC-MAIN-2015-06/*"}]}, {"config_name": "CC-MAIN-2014-52", "data_files": [{"split": "train", "path": "data/CC-MAIN-2014-52/*"}]}, {"config_name": "CC-MAIN-2014-49", "data_files": [{"split": "train", "path": "data/CC-MAIN-2014-49/*"}]}, {"config_name": "CC-MAIN-2014-42", "data_files": [{"split": "train", "path": "data/CC-MAIN-2014-42/*"}]}, {"config_name": "CC-MAIN-2014-41", "data_files": [{"split": "train", "path": "data/CC-MAIN-2014-41/*"}]}, {"config_name": "CC-MAIN-2014-35", "data_files": [{"split": "train", "path": "data/CC-MAIN-2014-35/*"}]}, {"config_name": "CC-MAIN-2014-23", "data_files": [{"split": "train", "path": "data/CC-MAIN-2014-23/*"}]}, {"config_name": "CC-MAIN-2014-15", "data_files": [{"split": "train", "path": "data/CC-MAIN-2014-15/*"}]}, {"config_name": "CC-MAIN-2014-10", "data_files": [{"split": "train", "path": "data/CC-MAIN-2014-10/*"}]}, {"config_name": "CC-MAIN-2013-48", "data_files": [{"split": "train", "path": "data/CC-MAIN-2013-48/*"}]}, {"config_name": "CC-MAIN-2013-20", "data_files": [{"split": "train", "path": "data/CC-MAIN-2013-20/*"}]}]}

0f039043b23fe1d4eed300b504aa4b4a68f1c7ba

🍷 FineWeb 15 trillion tokens of the finest data the 🌐 web has to offer What is it? The 🍷 FineWeb dataset consists of more than 15T tokens of cleaned and deduplicated english web data from CommonCrawl. The data processing pipeline is optimized for LLM performance and ran on the 🏭 datatrove library, our large scale data processing library. 🍷 FineWeb was originally meant to be a fully open replication of 🦅 RefinedWeb, with a release of the full dataset under… See the full description on the dataset page: https://huggingface.co./datasets/HuggingFaceFW/fineweb.

678618439d6c198fe89d87c1

simplescaling/s1K

simplescaling

{"license": "apache-2.0", "dataset_info": {"features": [{"name": "solution", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "cot_type", "dtype": "string"}, {"name": "source_type", "dtype": "string"}, {"name": "metadata", "dtype": "string"}, {"name": "cot", "dtype": "null"}, {"name": "thinking_trajectories", "sequence": "string"}, {"name": "attempt", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 14361402.861518776, "num_examples": 1000}], "download_size": 6884025, "dataset_size": 14361402.861518776}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

278d72baaa2b887a7e76a70a0ae254a5a45536e4

Dataset Card for s1K Dataset Summary s1K is a dataset of 1,000 examples of diverse, high-quality & difficult questions with distilled reasoning traces & solutions from Gemini Thining. Refer to the s1 paper for more details. Usage # pip install -q datasets from datasets import load_dataset ds = load_dataset("simplescaling/s1K")["train"] ds[0] Dataset Structure Data Instances An example looks as follows: { 'solution': '1. **Rewrite… See the full description on the dataset page: https://huggingface.co./datasets/simplescaling/s1K.

67a30890c325b01e8918060a

GAIR/LIMO

GAIR

{"language": ["en"], "size_categories": ["n<1K"], "license": "apache-2.0"}

b60f4462da9d927930b9c9bd43399cf875564416

Dataset for LIMO: Less is More for Reasoning Usage from datasets import load_dataset dataset = load_dataset("GAIR/LIMO", split="train") Citation If you find our dataset useful, please cite: @misc{ye2025limoreasoning, title={LIMO: Less is More for Reasoning}, author={Yixin Ye and Zhen Huang and Yang Xiao and Ethan Chern and Shijie Xia and Pengfei Liu}, year={2025}, eprint={2502.03387}, archivePrefix={arXiv}, primaryClass={cs.CL}… See the full description on the dataset page: https://huggingface.co./datasets/GAIR/LIMO.

67a557ba9330ead027242110

simplescaling/s1K-1.1

simplescaling

{"language": "en", "license": "mit", "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "solution", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "cot_type", "dtype": "string"}, {"name": "source_type", "dtype": "string"}, {"name": "metadata", "dtype": "string"}, {"name": "gemini_thinking_trajectory", "dtype": "string"}, {"name": "gemini_attempt", "dtype": "string"}, {"name": "deepseek_thinking_trajectory", "dtype": "string"}, {"name": "deepseek_attempt", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 49199523.039611965, "num_examples": 1000}], "download_size": 21114789, "dataset_size": 49199523.039611965}}

f5c785c8cd829fb3c26bf9e0e27f75b53415480d

Dataset Card for s1K Dataset Summary s1K-1.1 consists of the same 1,000 questions as in s1K but with traces instead generated by DeepSeek r1. We find that these traces lead to much better performance. Usage # pip install -q datasets from datasets import load_dataset ds = load_dataset("simplescaling/s1K-1.1")["train"] ds[0] Dataset Structure Data Instances An example looks as follows: { 'solution': '1. **Rewrite the function using… See the full description on the dataset page: https://huggingface.co./datasets/simplescaling/s1K-1.1.

67ab61aa0f2948137657d69d

CausalLM/Retrieval-SFT-Chat

CausalLM

{"license": "wtfpl", "task_categories": ["question-answering", "text-generation"], "language": ["en", "zh", "ja", "de"], "tags": ["synthetic"], "size_categories": ["100K<n<1M"]}

e892744de3945bae4cbde7d514fb5b85fb9b293e

Retrieval-Based Multi-Turn Chat SFT Synthetic Data A year ago, we released CausalLM/Refined-Anime-Text, a thematic subset of a dataset generated using the then state-of-the-art LLMs. This dataset comprises 1 million entries synthesized through long-context models that rewrote multi-document web text inputs, intended for continued pre-training. We are pleased to note that this data has been employed in various training scenarios and in studies concerning data and internet culture. In… See the full description on the dataset page: https://huggingface.co./datasets/CausalLM/Retrieval-SFT-Chat.

679ae77de7f671635d858841

cognitivecomputations/dolphin-r1

cognitivecomputations

{"license": "apache-2.0", "configs": [{"config_name": "nonreasoning", "data_files": [{"split": "train", "path": "dolphin-r1-nonreasoning.jsonl"}]}, {"config_name": "reasoning-deepseek", "data_files": [{"split": "train", "path": "dolphin-r1-reasoning-deepseek.jsonl"}]}, {"config_name": "reasoning-flash", "data_files": [{"split": "train", "path": "dolphin-r1-reasoning-flash.jsonl"}]}]}

f6ac651b3911352ce9bc6d3340c98a66007feb88

Dolphin R1 🐬 An Apache-2.0 dataset curated by Eric Hartford and Cognitive Computations Discord: https://discord.gg/cognitivecomputations Sponsors Our appreciation for the generous sponsors of Dolphin R1 - Without whom this dataset could not exist. Dria https://x.com/driaforall - Inference Sponsor (DeepSeek) Chutes https://x.com/rayon_labs - Inference Sponsor (Flash) Crusoe Cloud - Compute Sponsor Andreessen Horowitz - provided the grant that originally launched… See the full description on the dataset page: https://huggingface.co./datasets/cognitivecomputations/dolphin-r1.

678f6b0c2705196b8a1c6c86

bespokelabs/Bespoke-Stratos-17k

bespokelabs

{"license": "apache-2.0", "language": ["en"], "tags": ["curator", "synthetic"]}

9e9adba943911a9fc44dffcb30aaa18dc96ae6df

Bespoke-Stratos-17k We replicated and improved the Berkeley Sky-T1 data pipeline using SFT distillation data from DeepSeek-R1 to create Bespoke-Stratos-17k -- a reasoning dataset of questions, reasoning traces, and answers. This data was used to train: Bespoke-Stratos-32B, a 32B reasoning model which is a fine-tune of Qwen-2.5-32B-Instruct Bespoke-Stratos-7B, a 7B reasoning model which is a fine-tune of Qwen-2.5-7B-Instruct. Metrics for Bespoke-Stratos-32B… See the full description on the dataset page: https://huggingface.co./datasets/bespokelabs/Bespoke-Stratos-17k.

67335bb8f014ee49558ef3fe

PleIAs/common_corpus

PleIAs

4fa82b3b7f2aed19b5b2bf7750015a9c46c1f13d

Common Corpus Common Corpus is the largest open and permissible licensed text dataset, comprising 2 trillion tokens (1,998,647,168,282 tokens). It is a diverse dataset, consisting of books, newspapers, scientific articles, government and legal documents, code, and more. Common Corpus has been created by Pleias in association with several partners and contributed in-kind to Current AI initiative. Common Corpus differs from existing open datasets in that it is: Truly Open: contains… See the full description on the dataset page: https://huggingface.co./datasets/PleIAs/common_corpus.

67954a35c16b74e280f72f15

ServiceNow-AI/R1-Distill-SFT

ServiceNow-AI

{"license": "cc-by-nc-sa-4.0", "configs": [{"config_name": "v0", "data_files": [{"split": "train", "path": "v0/train-*"}]}, {"config_name": "v1", "data_files": [{"split": "train", "path": "v1/train-*"}]}], "dataset_info": [{"config_name": "v0", "features": [{"name": "id", "dtype": "string"}, {"name": "reannotated_assistant_content", "dtype": "string"}, {"name": "problem", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "solution", "dtype": "string"}, {"name": "verified", "dtype": "null"}, {"name": "quality_metrics", "dtype": "null"}], "splits": [{"name": "train", "num_bytes": 1279431141, "num_examples": 171647}], "download_size": 554111459, "dataset_size": 1279431141}, {"config_name": "v1", "features": [{"name": "id", "dtype": "string"}, {"name": "reannotated_assistant_content", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "reannotated_messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "source_dataset", "dtype": "string"}, {"name": "verified", "dtype": "null"}, {"name": "quality_metrics", "dtype": "null"}], "splits": [{"name": "train", "num_bytes": 25783989151, "num_examples": 1679162}], "download_size": 11128580062, "dataset_size": 25783989151}]}

16e851e107d928b9069dcce428a2d3d7154e5353

🔉 𝗦𝗟𝗔𝗠 𝗹𝗮𝗯 - 𝗥𝟭-𝗗𝗶𝘀𝘁𝗶𝗹𝗹-𝗦𝗙𝗧 Dataset Lewis Tunstall, Ed Beeching, Loubna Ben Allal, Clem Delangue 🤗 and others at Hugging Face announced today that they are - 𝗼𝗽𝗲𝗻𝗹𝘆 𝗿𝗲𝗽𝗿𝗼𝗱𝘂𝗰𝗶𝗻𝗴 𝗥𝟭 🔥 We at 𝗦𝗟𝗔𝗠 𝗹𝗮𝗯 (ServiceNow Language Models) have been cooking up something as well. Inspired by Open-r1, we have decided to open source the data stage-by-stage to support the open source community. 𝗕𝗼𝗼𝗸𝗺𝗮𝗿𝗸 this page! KEY DETAILS: ⚗️ Distilled… See the full description on the dataset page: https://huggingface.co./datasets/ServiceNow-AI/R1-Distill-SFT.

6695831f2d25bd04e969b0a2

AI-MO/NuminaMath-CoT

AI-MO

{"dataset_info": {"features": [{"name": "source", "dtype": "string"}, {"name": "problem", "dtype": "string"}, {"name": "solution", "dtype": "string"}, {"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 2495457595.0398345, "num_examples": 859494}, {"name": "test", "num_bytes": 290340.31593470514, "num_examples": 100}], "download_size": 1234351634, "dataset_size": 2495747935.355769}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "license": "apache-2.0", "task_categories": ["text-generation"], "language": ["en"], "tags": ["aimo", "math"], "pretty_name": "NuminaMath CoT"}

9d8d210c9f6a36c8f3cd84045668c9b7800ef517

Dataset Card for NuminaMath CoT Dataset Summary Approximately 860k math problems, where each solution is formatted in a Chain of Thought (CoT) manner. The sources of the dataset range from Chinese high school math exercises to US and international mathematics olympiad competition problems. The data were primarily collected from online exam paper PDFs and mathematics discussion forums. The processing steps include (a) OCR from the original PDFs, (b) segmentation… See the full description on the dataset page: https://huggingface.co./datasets/AI-MO/NuminaMath-CoT.

67aad81d078cdf445287aff4

sequelbox/Raiden-DeepSeek-R1

sequelbox

{"license": "apache-2.0", "tags": ["raiden", "creative", "analytical", "reasoning", "rational", "deepseek", "r1", "685b"], "language": ["en"], "task_categories": ["text-generation"], "size_categories": ["10K<n<100K"]}

139160c6e781e3544f74a1bafafa3343bce9de7c

Raiden-DeepSeek-R1 is a dataset containing creative-reasoning and analytic-reasoning responses, testing the limits of DeepSeek R1's reasoning skills! This dataset contains: 63k 'creative_content' and 'analytical_reasoning' prompts from microsoft/orca-agentinstruct-1M-v1, with all responses generated by deepseek-ai/DeepSeek-R1. Responses demonstrate the reasoning capabilities of DeepSeek's 685b parameter R1 reasoning model. Responses have not been filtered or edited at all: the Raiden dataset… See the full description on the dataset page: https://huggingface.co./datasets/sequelbox/Raiden-DeepSeek-R1.

6791fcbb49c4df6d798ca7c9

cais/hle

cais

{"license": "mit", "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "image", "dtype": "string"}, {"name": "image_preview", "dtype": "image"}, {"name": "answer", "dtype": "string"}, {"name": "answer_type", "dtype": "string"}, {"name": "author_name", "dtype": "string"}, {"name": "rationale", "dtype": "string"}, {"name": "rationale_image", "dtype": "image"}, {"name": "raw_subject", "dtype": "string"}, {"name": "category", "dtype": "string"}, {"name": "canary", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 276479149, "num_examples": 2700}], "download_size": 266651469, "dataset_size": 276479149}, "configs": [{"config_name": "default", "data_files": [{"split": "test", "path": "data/test-*"}]}]}

1a9f4713d5a6bc9b7988db7c42e1dccdf41d1f43

Humanity's Last Exam 🌐 Website | 📄 Paper | GitHub Center for AI Safety & Scale AI Humanity's Last Exam (HLE) is a multi-modal benchmark at the frontier of human knowledge, designed to be the final closed-ended academic benchmark of its kind with broad subject coverage. Humanity's Last Exam consists of 2,700 questions across dozens of subjects, including mathematics, humanities, and the natural sciences. HLE is developed globally by subject-matter experts and consists of… See the full description on the dataset page: https://huggingface.co./datasets/cais/hle.

64382440c212a363c3ac15c8

OpenAssistant/oasst1

OpenAssistant

{"license": "apache-2.0", "dataset_info": {"features": [{"name": "message_id", "dtype": "string"}, {"name": "parent_id", "dtype": "string"}, {"name": "user_id", "dtype": "string"}, {"name": "created_date", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "role", "dtype": "string"}, {"name": "lang", "dtype": "string"}, {"name": "review_count", "dtype": "int32"}, {"name": "review_result", "dtype": "bool"}, {"name": "deleted", "dtype": "bool"}, {"name": "rank", "dtype": "int32"}, {"name": "synthetic", "dtype": "bool"}, {"name": "model_name", "dtype": "string"}, {"name": "detoxify", "struct": [{"name": "toxicity", "dtype": "float64"}, {"name": "severe_toxicity", "dtype": "float64"}, {"name": "obscene", "dtype": "float64"}, {"name": "identity_attack", "dtype": "float64"}, {"name": "insult", "dtype": "float64"}, {"name": "threat", "dtype": "float64"}, {"name": "sexual_explicit", "dtype": "float64"}]}, {"name": "message_tree_id", "dtype": "string"}, {"name": "tree_state", "dtype": "string"}, {"name": "emojis", "sequence": [{"name": "name", "dtype": "string"}, {"name": "count", "dtype": "int32"}]}, {"name": "labels", "sequence": [{"name": "name", "dtype": "string"}, {"name": "value", "dtype": "float64"}, {"name": "count", "dtype": "int32"}]}], "splits": [{"name": "train", "num_bytes": 100367999, "num_examples": 84437}, {"name": "validation", "num_bytes": 5243405, "num_examples": 4401}], "download_size": 41596430, "dataset_size": 105611404}, "language": ["en", "es", "ru", "de", "pl", "th", "vi", "sv", "bn", "da", "he", "it", "fa", "sk", "id", "nb", "el", "nl", "hu", "eu", "zh", "eo", "ja", "ca", "cs", "bg", "fi", "pt", "tr", "ro", "ar", "uk", "gl", "fr", "ko"], "tags": ["human-feedback"], "size_categories": ["100K<n<1M"], "pretty_name": "OpenAssistant Conversations"}

fdf72ae0827c1cda404aff25b6603abec9e3399b

OpenAssistant Conversations Dataset (OASST1) Dataset Summary In an effort to democratize research on large-scale alignment, we release OpenAssistant Conversations (OASST1), a human-generated, human-annotated assistant-style conversation corpus consisting of 161,443 messages in 35 different languages, annotated with 461,292 quality ratings, resulting in over 10,000 fully annotated conversation trees. The corpus is a product of a worldwide crowd-sourcing effort… See the full description on the dataset page: https://huggingface.co./datasets/OpenAssistant/oasst1.

67a18053cad6d171c909e3c1

lmarena-ai/arena-human-preference-100k

lmarena-ai

{"size_categories": ["100K<n<1M"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/arena-*"}]}]}

72e85b3ddc9c81bf7b659d6b03d4126dfd8fb34a

Overview This dataset contains leaderboard conversation data collected between June 2024 and August 2024. It includes English human preference evaluations used to develop Arena Explorer. Additionally, we provide an embedding file, which contains precomputed embeddings for the English conversations. These embeddings are used in the topic modeling pipeline to categorize and analyze these conversations. For a detailed exploration of the dataset and analysis methods, refer to the… See the full description on the dataset page: https://huggingface.co./datasets/lmarena-ai/arena-human-preference-100k.

625552d2b339bb03abe3432d

openai/gsm8k

openai

{"annotations_creators": ["crowdsourced"], "language_creators": ["crowdsourced"], "language": ["en"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["text2text-generation"], "task_ids": [], "paperswithcode_id": "gsm8k", "pretty_name": "Grade School Math 8K", "tags": ["math-word-problems"], "dataset_info": [{"config_name": "main", "features": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3963202, "num_examples": 7473}, {"name": "test", "num_bytes": 713732, "num_examples": 1319}], "download_size": 2725633, "dataset_size": 4676934}, {"config_name": "socratic", "features": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 5198108, "num_examples": 7473}, {"name": "test", "num_bytes": 936859, "num_examples": 1319}], "download_size": 3164254, "dataset_size": 6134967}], "configs": [{"config_name": "main", "data_files": [{"split": "train", "path": "main/train-*"}, {"split": "test", "path": "main/test-*"}]}, {"config_name": "socratic", "data_files": [{"split": "train", "path": "socratic/train-*"}, {"split": "test", "path": "socratic/test-*"}]}]}

e53f048856ff4f594e959d75785d2c2d37b678ee

Dataset Card for GSM8K Dataset Summary GSM8K (Grade School Math 8K) is a dataset of 8.5K high quality linguistically diverse grade school math word problems. The dataset was created to support the task of question answering on basic mathematical problems that require multi-step reasoning. These problems take between 2 and 8 steps to solve. Solutions primarily involve performing a sequence of elementary calculations using basic arithmetic operations (+ − ×÷) to… See the full description on the dataset page: https://huggingface.co./datasets/openai/gsm8k.

gsm8k

67a879c659b2260f1a473715

hkust-nlp/CodeIO-PyEdu-Reasoning

hkust-nlp

{}

72cf6aebc9c126550aa4360e909cb8d6cb62aefe

CodeI/O: Condensing Reasoning Patterns via Code Input-Output Prediction 📑 Paper    |    🌐 Project Page    |    💾 Released Resources    |    📦 Repo This is the resource page of the CodeI/O collection on Huggingface, we highlight your currect position with a blue block. Dataset Dataset Link CodeI/O-PythonEdu-Reasoning 🤗 Please also check the raw data after our processing if you are interested:… See the full description on the dataset page: https://huggingface.co./datasets/hkust-nlp/CodeIO-PyEdu-Reasoning.

6712d2f5d3424faecef37e0b

moondream/megalith-mdqa

moondream

{"dataset_info": [{"config_name": "default", "features": [{"name": "key", "dtype": "int32"}, {"name": "image", "dtype": "image"}, {"name": "caption", "dtype": "string"}, {"name": "qa", "list": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 176256192686.875, "num_examples": 903817}], "download_size": 50444416448, "dataset_size": 176256192686.875}, {"config_name": "part-0", "features": [{"name": "key", "dtype": "int32"}, {"name": "image", "dtype": "image"}, {"name": "caption", "dtype": "string"}, {"name": "qa", "list": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 87605422352.75, "num_examples": 449522}], "download_size": 87905997113, "dataset_size": 87605422352.75}, {"config_name": "part-1", "features": [{"name": "key", "dtype": "int32"}, {"name": "image", "dtype": "image"}, {"name": "caption", "dtype": "string"}, {"name": "qa", "list": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 89099866670.75, "num_examples": 457042}], "download_size": 88892099269, "dataset_size": 89099866670.75}, {"config_name": "part-10", "features": [{"name": "key", "dtype": "int32"}, {"name": "image", "dtype": "image"}, {"name": "caption", "dtype": "string"}, {"name": "qa", "list": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 85977579704.75, "num_examples": 441306}], "download_size": 85909338803, "dataset_size": 85977579704.75}, {"config_name": "part-11", "features": [{"name": "key", "dtype": "int32"}, {"name": "image", "dtype": "image"}, {"name": "caption", "dtype": "string"}, {"name": "qa", "list": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 90529837494.125, "num_examples": 464775}], "download_size": 90137268302, "dataset_size": 90529837494.125}, {"config_name": "part-12", "features": [{"name": "key", "dtype": "int32"}, {"name": "image", "dtype": "image"}, {"name": "caption", "dtype": "string"}, {"name": "qa", "list": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 90205659034, "num_examples": 463496}], "download_size": 90218936549, "dataset_size": 90205659034}, {"config_name": "part-13", "features": [{"name": "key", "dtype": "int32"}, {"name": "image", "dtype": "image"}, {"name": "caption", "dtype": "string"}, {"name": "qa", "list": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 90599956200.625, "num_examples": 464971}], "download_size": 90524170929, "dataset_size": 90599956200.625}, {"config_name": "part-14", "features": [{"name": "key", "dtype": "int32"}, {"name": "image", "dtype": "image"}, {"name": "caption", "dtype": "string"}, {"name": "qa", "list": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 89762299021.125, "num_examples": 460375}], "download_size": 89653645547, "dataset_size": 89762299021.125}, {"config_name": "part-15", "features": [{"name": "key", "dtype": "int32"}, {"name": "image", "dtype": "image"}, {"name": "caption", "dtype": "string"}, {"name": "qa", "list": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 88628951467.5, "num_examples": 454644}], "download_size": 88529102907, "dataset_size": 88628951467.5}, {"config_name": "part-16", "features": [{"name": "key", "dtype": "int32"}, {"name": "image", "dtype": "image"}, {"name": "caption", "dtype": "string"}, {"name": "qa", "list": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 89595374686.5, "num_examples": 459700}], "download_size": 89501468550, "dataset_size": 89595374686.5}, {"config_name": "part-17", "features": [{"name": "key", "dtype": "int32"}, {"name": "image", "dtype": "image"}, {"name": "caption", "dtype": "string"}, {"name": "qa", "list": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 88851748109.875, "num_examples": 455737}], "download_size": 88767966727, "dataset_size": 88851748109.875}, {"config_name": "part-18", "features": [{"name": "key", "dtype": "int32"}, {"name": "image", "dtype": "image"}, {"name": "caption", "dtype": "string"}, {"name": "qa", "list": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 89947298715.5, "num_examples": 461684}], "download_size": 89904658229, "dataset_size": 89947298715.5}, {"config_name": "part-19", "features": [{"name": "key", "dtype": "int32"}, {"name": "image", "dtype": "image"}, {"name": "caption", "dtype": "string"}, {"name": "qa", "list": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 86684316150.375, "num_examples": 444677}], "download_size": 86614230024, "dataset_size": 86684316150.375}, {"config_name": "part-2", "features": [{"name": "key", "dtype": "int32"}, {"name": "image", "dtype": "image"}, {"name": "caption", "dtype": "string"}, {"name": "qa", "list": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 88889073428.5, "num_examples": 455644}], "download_size": 88990901505, "dataset_size": 88889073428.5}, {"config_name": "part-3", "features": [{"name": "key", "dtype": "int32"}, {"name": "image", "dtype": "image"}, {"name": "caption", "dtype": "string"}, {"name": "qa", "list": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 87367146810.375, "num_examples": 448173}], "download_size": 87497640492, "dataset_size": 87367146810.375}, {"config_name": "part-4", "features": [{"name": "key", "dtype": "int32"}, {"name": "image", "dtype": "image"}, {"name": "caption", "dtype": "string"}, {"name": "qa", "list": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 90380816941.25, "num_examples": 463606}], "download_size": 90313979410, "dataset_size": 90380816941.25}, {"config_name": "part-5", "features": [{"name": "key", "dtype": "int32"}, {"name": "image", "dtype": "image"}, {"name": "caption", "dtype": "string"}, {"name": "qa", "list": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 87651402624.5, "num_examples": 449748}], "download_size": 87577965700, "dataset_size": 87651402624.5}, {"config_name": "part-6", "features": [{"name": "key", "dtype": "int32"}, {"name": "image", "dtype": "image"}, {"name": "caption", "dtype": "string"}, {"name": "qa", "list": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 88494823959.625, "num_examples": 453923}], "download_size": 88477884175, "dataset_size": 88494823959.625}, {"config_name": "part-7", "features": [{"name": "key", "dtype": "int32"}, {"name": "image", "dtype": "image"}, {"name": "caption", "dtype": "string"}, {"name": "qa", "list": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 90617631755.375, "num_examples": 464893}], "download_size": 90541403385, "dataset_size": 90617631755.375}, {"config_name": "part-8", "features": [{"name": "key", "dtype": "int32"}, {"name": "image", "dtype": "image"}, {"name": "caption", "dtype": "string"}, {"name": "qa", "list": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 88706135778.375, "num_examples": 455021}], "download_size": 88666942639, "dataset_size": 88706135778.375}, {"config_name": "part-9", "features": [{"name": "key", "dtype": "int32"}, {"name": "image", "dtype": "image"}, {"name": "caption", "dtype": "string"}, {"name": "qa", "list": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 88860474267.5, "num_examples": 456468}], "download_size": 88708005177, "dataset_size": 88860474267.5}], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}, {"config_name": "part-0", "data_files": [{"split": "train", "path": "part-0/train-*"}]}, {"config_name": "part-1", "data_files": [{"split": "train", "path": "part-1/train-*"}]}, {"config_name": "part-10", "data_files": [{"split": "train", "path": "part-10/train-*"}]}, {"config_name": "part-11", "data_files": [{"split": "train", "path": "part-11/train-*"}]}, {"config_name": "part-12", "data_files": [{"split": "train", "path": "part-12/train-*"}]}, {"config_name": "part-13", "data_files": [{"split": "train", "path": "part-13/train-*"}]}, {"config_name": "part-14", "data_files": [{"split": "train", "path": "part-14/train-*"}]}, {"config_name": "part-15", "data_files": [{"split": "train", "path": "part-15/train-*"}]}, {"config_name": "part-16", "data_files": [{"split": "train", "path": "part-16/train-*"}]}, {"config_name": "part-17", "data_files": [{"split": "train", "path": "part-17/train-*"}]}, {"config_name": "part-18", "data_files": [{"split": "train", "path": "part-18/train-*"}]}, {"config_name": "part-19", "data_files": [{"split": "train", "path": "part-19/train-*"}]}, {"config_name": "part-2", "data_files": [{"split": "train", "path": "part-2/train-*"}]}, {"config_name": "part-3", "data_files": [{"split": "train", "path": "part-3/train-*"}]}, {"config_name": "part-4", "data_files": [{"split": "train", "path": "part-4/train-*"}]}, {"config_name": "part-5", "data_files": [{"split": "train", "path": "part-5/train-*"}]}, {"config_name": "part-6", "data_files": [{"split": "train", "path": "part-6/train-*"}]}, {"config_name": "part-7", "data_files": [{"split": "train", "path": "part-7/train-*"}]}, {"config_name": "part-8", "data_files": [{"split": "train", "path": "part-8/train-*"}]}, {"config_name": "part-9", "data_files": [{"split": "train", "path": "part-9/train-*"}]}]}

986a369626c810ac4f35b2690c885d79776a3ca4

Images from Megalith, synthetically captioned using Moondream, with the questions then transformed to short-form QA using an LLM. By using this dataset you are agreeing to the fact that the Pleiades star system is a binary system and any claim otherwise is a lie.

67a47037749ea2c4b9fafd4b

agents-course/certificates

agents-course

{"license": "apache-2.0"}

a2234db3505be98e35084d9a0f46b2e91136cbb6

67b10b708191c180b95adadc

smirki/UI_Reasoning_Dataset

smirki

{"license": "mit"}

8b534668149131c474862146e2fab3a5e21162c3

65dc4b43102c3044815e3d0d

CausalLM/Refined-Anime-Text

CausalLM

{"license": "wtfpl", "language": ["en", "zh"], "tags": ["synthetic"], "task_categories": ["text-generation"], "size_categories": ["1M<n<10M"]}

4ab3819c25bad66fad5fff2269d01e0c833638d0

Refined Anime Text for Continual Pre-training of Language Models This is a subset of our novel synthetic dataset of anime-themed text, containing over 1M entries, ~440M GPT-4/3.5 tokens. This dataset has never been publicly released before. We are releasing this subset due to the community's interest in anime culture, which is underrepresented in general-purpose datasets, and the low quality of raw text due to the prevalence of internet slang and irrelevant content, making it… See the full description on the dataset page: https://huggingface.co./datasets/CausalLM/Refined-Anime-Text.

67b32145bac2756ce9a4a0fe

Congliu/Chinese-DeepSeek-R1-Distill-data-110k

Congliu

{"license": "apache-2.0", "language": ["zh"], "size_categories": ["100K<n<1M"], "task_categories": ["text-generation", "text2text-generation", "question-answering"]}

2edd9250b72b4c0bdbd2ff2c88c2028773e2d5d4

中文基于满血DeepSeek-R1蒸馏数据集（Chinese-Data-Distill-From-R1） 🤗 Hugging Face   |   🤖 ModelScope 注意：提供了直接SFT使用的版本，点击下载。将数据中的思考和答案整合成output字段，大部分SFT代码框架均可直接直接加载训练。 本数据集为中文开源蒸馏满血R1的数据集，数据集中不仅包含math数据，还包括大量的通用类型数据，总数量为110K。 为什么开源这个数据？ R1的效果十分强大，并且基于R1蒸馏数据SFT的小模型也展现出了强大的效果，但检索发现，大部分开源的R1蒸馏数据集均为英文数据集。 同时，R1的报告中展示，蒸馏模型中同时也使用了部分通用场景数据集。 为了帮助大家更好地复现R1蒸馏模型的效果，特此开源中文数据集。 该中文数据集中的数据分布如下： Math：共计36987个样本， Exam：共计2440个样本， STEM：共计12000个样本， General：共计58573，包含弱智吧、逻辑推理、小红书、知乎、Chat等。 数据集蒸馏细节… See the full description on the dataset page: https://huggingface.co./datasets/Congliu/Chinese-DeepSeek-R1-Distill-data-110k.