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"]}

c9f81f90713ba659223d0d2c1507f49a27c4a32b

中文基于满血DeepSeek-R1蒸馏数据集（Chinese-Data-Distill-From-R1） 🤗 Hugging Face   |   🤖 ModelScope    |   🚀 Github    |   📑 Blog 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36568个样本， Exam：共计2432个样本， STEM：共计12648个样本，… See the full description on the dataset page: https://huggingface.co/datasets/Congliu/Chinese-DeepSeek-R1-Distill-data-110k.

67aa021ced8d8663d42505cc

open-r1/OpenR1-Math-220k

open-r1

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e4e141ec9dea9f8326f4d347be56105859b2bd68

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

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56b06e3066a8163577ac93b24613a560e685d029

Open-Thoughts-114k Open synthetic reasoning dataset with 114k high-quality examples covering math, science, code, and puzzles! Inspect the content with rich formatting with Curator Viewer. 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:… See the full description on the dataset page: https://huggingface.co/datasets/open-thoughts/OpenThoughts-114k.

679c0b5c32cf4c58bdcba8eb

facebook/natural_reasoning

facebook

{"license": "cc-by-nc-4.0", "task_categories": ["text-generation"], "language": ["en"], "pretty_name": "Natural Reasoning", "size_categories": ["1M<n<10M"]}

8c9292b8932b86f90360ca6b152c2e23ca9252ab

NaturalReasoning is a large-scale dataset for general reasoning tasks. It consists of high-quality challenging reasoning questions backtranslated from pretraining corpora DCLM and FineMath. The questions have been deduplicated and decontaminated from popular reasoning benchmarks including MATH, GPQA, MMLU-Pro, MMLU-STEM. For each question, we extract the reference final answer from the original document from the pretraining corpora if possible. We also provide a model-generated response from… See the full description on the dataset page: https://huggingface.co/datasets/facebook/natural_reasoning.

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

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.

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.

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/

67ac9b0ae2c56194379f17a9

SakanaAI/AI-CUDA-Engineer-Archive

SakanaAI

{"tags": ["code"], "pretty_name": "The AI CUDA Engineer Archive", "license": "cc-by-4.0", "configs": [{"config_name": "default", "data_files": [{"split": "level_1", "path": "level_1.parquet"}, {"split": "level_2", "path": "level_2.parquet"}, {"split": "level_3", "path": "level_3.parquet"}]}]}

4edbe8d6d0b417e05aaf8ec7e23f78aecdc5516b

The AI CUDA Engineer Archive 👷: Agentic CUDA Kernel Discovery, Optimization & Composition We release The AI CUDA Engineer archive, a dataset consisting of approximately 30,000 CUDA kernels generated by The AI CUDA Engineer. It is released under the CC-By-4.0 license and can be accessed via HuggingFace and interactively visualized here. The dataset is based on the Kernel tasks provided in KernelBench and includes a torch reference implementation, torch, NCU and Clang-tidy profiling… See the full description on the dataset page: https://huggingface.co/datasets/SakanaAI/AI-CUDA-Engineer-Archive.

67b3495a2f3994b7d95dde92

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

Congliu

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

263435dc9a8cc822449b6f3531794486f8141be6

中文基于满血DeepSeek-R1蒸馏数据集（Chinese-Data-Distill-From-R1） 🤗 Hugging Face   |   🤖 ModelScope    |   🚀 Github    |   📑 Blog 注意：该版本为，可以直接SFT使用的版本，将原始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36568个样本， Exam：共计2432个样本， STEM：共计12648个样本，… See the full description on the dataset page: https://huggingface.co/datasets/Congliu/Chinese-DeepSeek-R1-Distill-data-110k-SFT.

66212f29fb07c3e05ad0432e

HuggingFaceFW/fineweb

HuggingFaceFW

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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.

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.

67b10b708191c180b95adadc

smirki/UI_Reasoning_Dataset

smirki

{"license": "mit"}

3341ce16a2d1183312040680689ffcc1763b6f37

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.

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.

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.

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.

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.

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.

67b375650c24a78b687a540f

arcinstitute/opengenome2

arcinstitute

{"license": "apache-2.0"}

dffb01a7fe9f6d1df1d83627d9e97241d72a8b20

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.

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.

67afd31dba726eda5c0846dc

google/smol

google

{"license": "cc-by-4.0", "task_categories": ["translation"], "pretty_name": "Smol", "size_categories": ["10K<n<100K"], "language": ["aa", "ab", "ace", "ach", "ady", "aeb", "af", "ahr", "aii", "ak", "alz", "am", "apc", "apd", "ar", "arn", "arz", "as", "av", "awa", "ay", "ayl", "ba", "bal", "ban", "bbc", "bci", "bem", "ber", "bew", "bfq", "bfy", "bgq", "bho", "bik", "bjn", "bm", "bns", "bo", "br", "bra", "brx", "bts", "btx", "bua", "bug", "ccp", "ce", "cgg", "ch", "chk", "chm", "ckb", "cnh", "crh", "crs", "ctg", "cv", "dhd", "din", "doi", "dov", "dv", "dyu", "dz", "ee", "efi", "en", "es", "fa", "ff", "fj", "fo", "fon", "fr", "fur", "gaa", "gn", "gom", "grt", "ha", "hi", "hil", "hne", "hoc", "hrx", "iba", "ig", "ilo", "iso", "iu", "jam", "kaa", "kac", "kbd", "kek", "kfy", "kg", "kha", "ki", "kl", "kr", "kri", "kru", "ks", "ktu", "kv", "lep", "lg", "li", "lif", "lij", "lmo", "ln", "ltg", "lu", "lua", "luo", "lus", "mad", "mag", "mai", "mak", "mam", "meo", "mfe", "mg", "mh", "min", "mjl", "mni", "mos", "ms", "mtr", "mwr", "nd", "ndc", "ne", "new", "nhe", "noe", "nr", "nso", "nus", "nv", "ny", "oc", "om", "os", "pa", "pag", "pam", "pap", "pcm", "pt", "qu", "quc", "rhg", "rn", "rom", "rw", "sa", "sah", "sat", "scl", "scn", "sd", "se", "sg", "sgj", "shn", "sjp", "skr", "sn", "so", "spv", "ss", "st", "sus", "sw", "syl", "szl", "tcy", "tet", "ti", "tiv", "tn", "to", "tpi", "trp", "ts", "tum", "ty", "tyv", "udm", "unr", "ve", "vec", "war", "wbr", "wo", "xh", "xnr", "xsr", "yo", "yua", "yue", "zap", "zh", "zu", "zza"], "configs": [{"config_name": "smolsent__en_es", "data_files": [{"split": "train", "path": "smolsent/en_es.jsonl"}]}, {"config_name": "smolsent__en_qu", "data_files": [{"split": "train", "path": "smolsent/en_qu.jsonl"}]}, {"config_name": "smolsent__en_ay", "data_files": [{"split": "train", "path": "smolsent/en_ay.jsonl"}]}, {"config_name": "smolsent__en_gn", "data_files": [{"split": "train", "path": "smolsent/en_gn.jsonl"}]}, {"config_name": "smolsent__en_bo", "data_files": [{"split": "train", "path": "smolsent/en_bo.jsonl"}]}, {"config_name": "smolsent__en_brx", "data_files": [{"split": "train", "path": "smolsent/en_brx.jsonl"}]}, {"config_name": "smolsent__en_pa-Arab", "data_files": [{"split": "train", "path": "smolsent/en_pa-Arab.jsonl"}]}, {"config_name": "smolsent__en_sat-Latn", "data_files": [{"split": "train", "path": "smolsent/en_sat-Latn.jsonl"}]}, {"config_name": "smolsent__en_sat", "data_files": [{"split": "train", "path": "smolsent/en_sat.jsonl"}]}, {"config_name": "smolsent__en_sa", "data_files": [{"split": "train", "path": "smolsent/en_sa.jsonl"}]}, {"config_name": "smolsent__en_ks", "data_files": [{"split": "train", "path": "smolsent/en_ks.jsonl"}]}, {"config_name": "smolsent__en_ks-Deva", "data_files": [{"split": "train", "path": "smolsent/en_ks-Deva.jsonl"}]}, {"config_name": "smolsent__en_yue", "data_files": [{"split": "train", "path": "smolsent/en_yue.jsonl"}]}, {"config_name": "smolsent__en_lus", "data_files": [{"split": 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d05b2e227adc45eaebdf62afef6dc618fe07a924

SMOL SMOL (Set for Maximal Overall Leverage) is a collection professional translations into 221 Low-Resource Languages, for the purpose of training translation models, and otherwise increasing the representations of said languages in NLP and technology. Please read the SMOL Paper and the GATITOS Paper for a much more thorough description! There are four resources in this directory: SmolDoc: document-level translations into 100 languages SmolSent: sentence-level translations into 81… See the full description on the dataset page: https://huggingface.co/datasets/google/smol.

67b2407eba726eda5ca0657a

Selvakumarduraipandian/Thirukural

Selvakumarduraipandian

{"license": "mit", "dataset_info": {"features": [{"name": "ID", "dtype": "int64"}, {"name": "Adhigaram_ID", "dtype": "int64"}, {"name": "Paal", "dtype": "string"}, {"name": "Iyal", "dtype": "string"}, {"name": "Adhigaram", "dtype": "string"}, {"name": "Kural", "dtype": "string"}, {"name": "Transliteration", "dtype": "string"}, {"name": "Vilakam", "dtype": "string"}, {"name": "Couplet", "dtype": "string"}, {"name": "Chapter", "dtype": "string"}, {"name": "Section", "dtype": "string"}, {"name": "Athigaram", "dtype": "string"}, {"name": "Kalaingar_Urai", "dtype": "string"}, {"name": "Parimezhalagar_Urai", "dtype": "string"}, {"name": "M_Varadharajanar", "dtype": "string"}, {"name": "Solomon_Pappaiya", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4216819, "num_examples": 1330}], "download_size": 1519400, "dataset_size": 4216819}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "language": ["ta", "en"], "tags": ["thirukural", "tamil"], "pretty_name": "Thiruvalluvar", "size_categories": ["n<1K"]}

001231c8ad9c1e08ca71b836559754aedbb0b983

📖 திருக்குறள் Dataset 🔹 Introduction இந்த dataset-ல் திருக்குறள், அதன் விளக்கம், பாடப்பிரிவுகள் மற்றும் பல உரைகள் உள்ளன. Selvakumar Duraipandian, one of the developers of thirukural.ai, has contributed to this dataset, making it a valuable resource for various language models and chatbot applications. இதை Natural Language Processing (NLP) மற்றும் Chatbot Fine-tuning போன்ற Machine Learning வேலைகளுக்கு பயன்படுத்தலாம். 📂 Dataset Structure இந்த dataset… See the full description on the dataset page: https://huggingface.co/datasets/Selvakumarduraipandian/Thirukural.

6695831f2d25bd04e969b0a2

AI-MO/NuminaMath-CoT

AI-MO

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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.

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.

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.

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.

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.

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

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.

67b1c1c6626cd81034787da1

bethgelab/CuratedThoughts

bethgelab

{"dataset_info": [{"config_name": "OpenR1-Math-220k-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": 4695959427.499899, "num_examples": 88662}], "download_size": 1998085149, "dataset_size": 4695959427.499899}, {"config_name": "OpenThoughts-114k-math-default", "features": [{"name": "source", "dtype": "string"}, {"name": "problem", "dtype": "string"}, {"name": "solution", "dtype": "string"}, {"name": "messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "system", "dtype": "string"}, {"name": "conversations", "list": [{"name": "from", "dtype": "string"}, {"name": "value", "dtype": "string"}]}, {"name": "generated_token_count", "dtype": "int64"}, {"name": "correct", "dtype": "bool"}], "splits": [{"name": "train", "num_bytes": 1730254099.6693895, "num_examples": 66076}], "download_size": 641561198, "dataset_size": 1730254099.6693895}, {"config_name": "OpenThoughts-114k-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": 3939535262.6895933, "num_examples": 89909}], "download_size": 2029522975, "dataset_size": 3939535262.6895933}, {"config_name": "OpenThoughts-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": 3939535262.6895933, "num_examples": 89909}], "download_size": 2029539149, "dataset_size": 3939535262.6895933}], "configs": [{"config_name": "OpenR1-Math-220k-default", "data_files": [{"split": "train", "path": "OpenR1-Math-220k-default/train-*"}]}, {"config_name": "OpenThoughts-114k-math-default", "data_files": [{"split": "train", "path": "OpenThoughts-114k-math-default/train-*"}]}, {"config_name": "OpenThoughts-114k-metadata", "data_files": [{"split": "train", "path": "OpenThoughts-114k-metadata/train-*"}]}]}

1ee49035a0e31e432e40f23d56961dc794becde2

CuratedThoughts: Data Curation for RL Training Datasets Andreas Hochlehnert¹*, Hardik Bhatnagar¹*, Vishaal Udandarao¹, Ameya Prabhu¹, Matthias Bethge¹ * - shared first authors, ¹ - Tübingen AI Center, University of Tübingen Since the release of DeepSeek-R1 and its reinforcement learning-based reasoning approach, new datasets such as Open-R1 and OpenThoughts have been introduced. These datasets are used for various post-training approaches like supervised fine-tuning (SFT) and Group… See the full description on the dataset page: https://huggingface.co/datasets/bethgelab/CuratedThoughts.

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.

67a47037749ea2c4b9fafd4b

agents-course/certificates

agents-course

{"license": "apache-2.0"}

b5ae880c6524770585a6e9db8c2c0791972ba569

67b1ea86ce03f9a057f98b26

GAIR/LIMR

GAIR

1832f825ee992a86a5be6d5b17d1e1a8c925df53

LIMR: Less is More for RL Scaling 📄 Paper   |   🌐 Dataset   |   📘 Model Releases [2025/02/17] We're releasing the following components: 🛠️ LIM Tools: Implementation of our Learning Impact Measurement methodology 🚀 Training & Evaluation: Complete implementation of our training pipeline and evaluation scripts 🔥 LIMR Dataset: Our curated dataset of 1,389 mathematical questions 🤖 LIMR Model: Model training on the LIMR dataset. Overview… See the full description on the dataset page: https://huggingface.co/datasets/GAIR/LIMR.

67946e948c7a5e66d4f3beea

Magpie-Align/Magpie-Reasoning-V2-250K-CoT-Deepseek-R1-Llama-70B

Magpie-Align

{"dataset_info": {"features": [{"name": "conversation_id", "dtype": "string"}, {"name": "instruction", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "conversations", "list": [{"name": "from", "dtype": "string"}, {"name": "value", "dtype": "string"}]}, {"name": "gen_input_configs", "struct": [{"name": "temperature", "dtype": "float64"}, {"name": "top_p", "dtype": "float64"}, {"name": "input_generator", "dtype": "string"}, {"name": "seed", "dtype": "null"}, {"name": "pre_query_template", "dtype": "string"}]}, {"name": "gen_response_configs", "struct": [{"name": "prompt", "dtype": "string"}, {"name": "temperature", "dtype": "int64"}, {"name": "top_p", "dtype": "float64"}, {"name": "repetition_penalty", "dtype": "float64"}, {"name": "max_tokens", "dtype": "int64"}, {"name": "stop_tokens", "sequence": "string"}, {"name": "output_generator", "dtype": "string"}, {"name": "engine", "dtype": "string"}]}, {"name": "intent", "dtype": "string"}, {"name": "knowledge", "dtype": "string"}, {"name": "difficulty", "dtype": "string"}, {"name": "difficulty_generator", "dtype": "string"}, {"name": "input_quality", "dtype": "string"}, {"name": "quality_explanation", "dtype": "string"}, {"name": "quality_generator", "dtype": "string"}, {"name": "task_category", "dtype": "string"}, {"name": "other_task_category", "sequence": "string"}, {"name": "task_category_generator", "dtype": "string"}, {"name": "language", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4065953009, "num_examples": 249922}], "download_size": 1615946128, "dataset_size": 4065953009}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "language": ["en"], "size_categories": ["100K<n<1M"], "license": "llama3.3"}

d78edb811991faba57a3b7226719f3818460e723

Project Web: https://magpie-align.github.io/ Arxiv Technical Report: https://arxiv.org/abs/2406.08464 Codes: https://github.com/magpie-align/magpie Abstract Click Here High-quality instruction data is critical for aligning large language models (LLMs). Although some models, such as Llama-3-Instruct, have open weights, their alignment data remain private, which hinders the democratization of AI. High human labor costs and a limited, predefined scope for prompting prevent… See the full description on the dataset page: https://huggingface.co/datasets/Magpie-Align/Magpie-Reasoning-V2-250K-CoT-Deepseek-R1-Llama-70B.

67ad1d5c100c4bfc7203184e

jonathan-roberts1/zerobench

jonathan-roberts1

{"pretty_name": "ZeroBench", "dataset_info": {"features": [{"name": "question_id", "dtype": "string"}, {"name": "question_text", "dtype": "string"}, {"name": "question_images_decoded", "sequence": "image"}, {"name": "question_answer", "dtype": "string"}, {"name": "question_images", "sequence": "string"}, {"name": "image_attribution", "dtype": "string"}], "splits": [{"name": "zerobench", "num_bytes": 65839214, "num_examples": 100}, {"name": "zerobench_subquestions", "num_bytes": 238205906, "num_examples": 334}], "download_size": 128199662, "dataset_size": 304045120}, "configs": [{"config_name": "default", "data_files": [{"split": "zerobench", "path": "data/zerobench-*"}, {"split": "zerobench_subquestions", "path": "data/zerobench_subquestions-*"}]}], "task_categories": ["image-text-to-text"]}

c2f28b45bd4092fe9f7fa7a17083a500e2abd93c

ZeroBench: An Impossible* Visual Benchmark for Contemporary Large Multimodal Models 🌐 Project Page | 📄 Paper | GitHub *Given the recent rapid progress on benchmarks, we do not imagine ZeroBench will remain "impossible" for long! ZeroBench is a challenging visual reasoning benchmark for Large Multimodal Models (LMMs). It consists of a main set of 100 high-quality, manually curated questions covering numerous domains, reasoning types and image type. Questions in ZeroBench have been… See the full description on the dataset page: https://huggingface.co/datasets/jonathan-roberts1/zerobench.

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.