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.

6797e648de960c48ff034e54

open-thoughts/OpenThoughts-114k

open-thoughts

{"dataset_info": {"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}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "tags": ["curator"]}

132040bb65d4490699a4f904db632f3f0d8af765

Open-Thoughts-114k Open synthetic reasoning dataset with 114k high-quality examples covering math, science, code, and puzzles! This data was used to train the OpenThinker-7B model, whose results are below. The numbers reported in the table below are evaluated with our open-source tool Evalchemy. AIME24 MATH500 GPQA-Diamond LCBv2 Easy LCBv2 Medium LCBv2 Hard LCBv2 All OpenThinker-7B 43.3 83.0 42.4 75.3 28.6 6.5 39.9 Bespoke-Stratos-7B 16.6 79.6 38.9 71.4… See the full description on the dataset page: https://huggingface.co/datasets/open-thoughts/OpenThoughts-114k.

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

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}]}

726423ab034680a26722ddbf043306db86a63629

🔉 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.

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.

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": "raw_subject", "dtype": "string"}, {"name": "category", "dtype": "string"}, {"name": "canary", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 170938175, "num_examples": 3000}], "download_size": 162844787, "dataset_size": 170938175}, "configs": [{"config_name": "default", "data_files": [{"split": "test", "path": "data/test-*"}]}]}

cc2f3e746c4ac9e1a6404203bdfd156df9d76e70

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 3,000 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.

679a781f49cc9e35d3247442

open-r1/OpenThoughts-114k-math

open-r1

{"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"}]}, {"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": 2333690726, "num_examples": 89120}], "download_size": 980472605, "dataset_size": 2333690726}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

2db609d3287c00483b52211c156c57367a3603fb

This is a filtered and metadata enriched version of open-thoughts/OpenThoughts-114k. While the original dataset is a valuable resource containing DeepSeek-R1 outputs, it has very little metadata (only 2 fields: system and conversations). It does not contain, for instance, the original solution label, which means that we can not verify the model answers. What we did filtered the dataset for math content (math questions were prefixed by "Return your final response within… See the full description on the dataset page: https://huggingface.co/datasets/open-r1/OpenThoughts-114k-math.

672068775ec8b84bfac97554

hamim-87/Ashrafur_bangla_math

hamim-87

87730f5ee633909b28b4ba64ea6f999c793b3751

679ad9cde1eea46ab4429ef4

TIGER-Lab/WebInstruct-CFT

TIGER-Lab

{"dataset_info": [{"config_name": "WebInstruct-CFT-4K", "features": [{"name": "instruction", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 17964182, "num_examples": 4000}], "download_size": 7440270, "dataset_size": 17964182}, {"config_name": "WebInstruct-CFT-50K", "features": [{"name": "instruction", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 224310117, "num_examples": 50000}], "download_size": 93221720, "dataset_size": 224310117}, {"config_name": "WebInstruct-CFT-600K", "features": [{"name": "instruction", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1546662303, "num_examples": 600000}], "download_size": 702269217, "dataset_size": 1546662303}], "configs": [{"config_name": "WebInstruct-CFT-4K", "data_files": [{"split": "train", "path": "WebInstruct-CFT-4K/train-*"}]}, {"config_name": "WebInstruct-CFT-50K", "data_files": [{"split": "train", "path": "WebInstruct-CFT-50K/train-*"}]}, {"config_name": "WebInstruct-CFT-600K", "data_files": [{"split": "train", "path": "WebInstruct-CFT-600K/train-*"}]}], "license": "apache-2.0", "task_categories": ["text-generation"], "language": ["en"], "tags": ["tigerlab", "math", "cft"]}

66826614531a7281e727d260d993d90e13e0bbcf

WebInstruct-CFT Dataset This dataset is introduced in our paper Critique Fine-Tuning: Learning to Critique is More Effective than Learning to Imitate. | 🚀Project Page | 📖Paper | 🔗Github | 🤗7B Model | 🤗32B Model | Overview WebInstruct-CFT is a critique-based instruction dataset derived from WebInstruct. Unlike traditional instruction datasets that focus on correct answers, our dataset includes critiques of responses, enabling models to learn through critical… See the full description on the dataset page: https://huggingface.co/datasets/TIGER-Lab/WebInstruct-CFT.

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-*"}]}]}

2ce9387281b0791990fa4e57c1b66ed2e61dff7c

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.

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… See the full description on the dataset page: https://huggingface.co/datasets/FreedomIntelligence/medical-o1-reasoning-SFT.

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.

621ffdd236468d709f184284

wikimedia/wikipedia

wikimedia

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b04c8d1ceb2f5cd4588862100d08de323dccfbaa

Dataset Card for Wikimedia Wikipedia Dataset Summary Wikipedia dataset containing cleaned articles of all languages. The dataset is built from the Wikipedia dumps (https://dumps.wikimedia.org/) with one subset per language, each containing a single train split. Each example contains the content of one full Wikipedia article with cleaning to strip markdown and unwanted sections (references, etc.). All language subsets have already been processed for recent dump… See the full description on the dataset page: https://huggingface.co/datasets/wikimedia/wikipedia.

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

654d98090011607c311c924a

MLCommons/unsupervised_peoples_speech

MLCommons

{"language": ["eng"], "pretty_name": "Unsupervised Peoples Speech", "tags": ["audio", "unsupervised"], "task_categories": ["automatic-speech-recognition", "audio-classification"], "task_ids": ["audio-language-identification"], "viewer": false}

4d314fce0b6e5057caa093b89140bf7a607a1494

Dataset Card for Unsupervised Peoples Speech Dataset Description Dataset Summary The Unsupervised Peoples Speech Dataset is a compilation of audiofiles extracted from Archive.org that is licensed for academic and commercial usage under CC-BY and CC-BY-SA licenses. It includes more than one million hours of audio with a diverse set of speakers. Point of Contact: MLCommons Datasets Discord Dataset Structure This dataset is a collection of audio… See the full description on the dataset page: https://huggingface.co/datasets/MLCommons/unsupervised_peoples_speech.

6795e2882ec68b4193d4dbf2

EricLu/SCP-116K

EricLu

{"license": "mit", "task_categories": ["text-generation", "question-answering"], "language": ["en"], "size_categories": ["100K<n<1M"], "tags": ["chemistry", "biology", "medical"]}

0f56afbbe44090c5808a013af714e4773dd06311

 Dataset Card for SCP-116K  Dataset Description Paper SCP-116K: A High-Quality Problem-Solution Dataset and a Generalized Pipeline for Automated Extraction in the Higher Education Science Domain  Dataset Summary  SCP-116K is a large-scale dataset containing 116,756 high-quality scientific problem-solution pairs, automatically extracted from web crawled documents. The dataset covers multiple scientific disciplines including physics, chemistry… See the full description on the dataset page: https://huggingface.co/datasets/EricLu/SCP-116K.

6798f01093acb5b36b8fed4d

promptfoo/CCP-sensitive-prompts

promptfoo

{"license": "cc-by-nc-4.0", "language": ["en"]}

f6fc9a004916913a84883591a96a6709feb02992

CCP Sensitive Prompts These prompts cover sensitive topics in China, and are likely to be censored by Chinese models.