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

6649d353babc0b33565e1a4a

HumanLLMs/Human-Like-DPO-Dataset

HumanLLMs

{"language": ["en"], "license": "llama3", "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data.json"}]}]}

dd82ab6a284a15765964149e6a6603ff8ed7d672

Enhancing Human-Like Responses in Large Language Models 🤗 Models | 📊 Dataset | 📄 Paper Human-Like-DPO-Dataset This dataset was created as part of research aimed at improving conversational fluency and engagement in large language models. It is suitable for formats like Direct Preference Optimization (DPO) to guide models toward generating more human-like responses. The dataset includes 10,884 samples across 256 topics, including: Technology Daily Life Science… See the full description on the dataset page: https://huggingface.co/datasets/HumanLLMs/Human-Like-DPO-Dataset.

678f6b0c2705196b8a1c6c86

bespokelabs/Bespoke-Stratos-17k

bespokelabs

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

ef97f8f2ec2bc16fa5a7d50fc9514b67e0ed45b2

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.

67903c8e66393ec4510de14f

yale-nlp/MMVU

yale-nlp

c83339b82df1f21b1ceaf5f0870360043d699604

MMVU: Measuring Expert-Level Multi-Discipline Video Understanding 🌐 Homepage • 🥇 Leaderboard • 📖 Paper • 🤗 Data 📰 News 2025-01-21: We are excited to release the MMVU paper, dataset, and evaluation code! 👋 Overview Why MMVU Benchmark? Despite the rapid progress of foundation models in both text-based and image-based expert reasoning, there is a clear gap in evaluating these models’ capabilities in specialized-domain video understanding.… See the full description on the dataset page: https://huggingface.co/datasets/yale-nlp/MMVU.

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.

6782cb3d244c0e06b1362fed

NovaSky-AI/Sky-T1_data_17k

NovaSky-AI

{"size_categories": ["10K<n<100K"], "license": "apache-2.0"}

3e260822dae5d833d9b040e34265d5f9a2b8a6a5

Sky-T1_data_17k.json: The 17k training data used to train Sky-T1-32B-Preview. The final data contains 5k coding data from APPs and TACO, and 10k math data from AIME, MATH, and Olympiads subsets of the NuminaMATH dataset. In addition, we maintain 1k science and puzzle data from STILL-2.

67750882633d421965733171

DAMO-NLP-SG/multimodal_textbook

DAMO-NLP-SG

{"license": "apache-2.0", "task_categories": ["text-generation", "summarization"], "language": ["en"], "tags": ["Pretraining", "Interleaved", "Reasoning"], "size_categories": ["1M<n<10M"]}

b83d307b2682d6b12420f5b93f4360880ea89df4

Multimodal-Textbook-6.5M Overview This dataset is for "2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining", containing 6.5M images interleaving with 0.8B text from instructional videos. It contains pre-training corpus using interleaved image-text format. Specifically, our multimodal-textbook includes 6.5M keyframesextracted from instructional videos, interleaving with 0.8B ASR texts. All the images and text are extracted from online… See the full description on the dataset page: https://huggingface.co/datasets/DAMO-NLP-SG/multimodal_textbook.

678a1234fa5b1f867f89534d

prithivMLmods/Math-Solve

prithivMLmods

{"license": "apache-2.0", "task_categories": ["text-generation", "question-answering", "summarization"], "language": ["en"], "size_categories": ["10K<n<100K"], "tags": ["math", "math-solve"]}

f395081ef3945f9b8ee8b6257a5649b5a53be764

Overview The Math-Solve dataset is a collection of math problems and their solutions, designed to facilitate training and evaluation of models for tasks such as text generation, question answering, and summarization. The dataset contains nearly 25k rows of math-related problems, each paired with a detailed solution. This dataset is particularly useful for researchers and developers working on AI models that require mathematical reasoning and problem-solving capabilities.… See the full description on the dataset page: https://huggingface.co/datasets/prithivMLmods/Math-Solve.

621ffdd236468d709f184284

wikimedia/wikipedia

wikimedia

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{"config_name": "20231101.zh-classical", "features": [{"name": "id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 14869227, "num_examples": 12708}], "download_size": 10098073, "dataset_size": 14869227}, {"config_name": "20231101.zh-min-nan", "features": [{"name": "id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 153672031, "num_examples": 432798}], "download_size": 37122048, "dataset_size": 153672031}, {"config_name": "20231101.zh-yue", "features": [{"name": "id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 109936351, "num_examples": 134140}], "download_size": 64950815, "dataset_size": 109936351}, {"config_name": "20231101.zu", "features": [{"name": "id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 7088246, "num_examples": 11561}], "download_size": 3792429, "dataset_size": 7088246}], "language_bcp47": ["be-tarask", "en-simple"]}

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.

6731b4fffb568a134d3f2dd6

TIGER-Lab/OmniEdit-Filtered-1.2M

TIGER-Lab

{"language": ["en"], "license": "mit", "size_categories": ["1M<n<10M"], "pretty_name": "OmniEdit", "dataset_info": {"features": [{"name": "omni_edit_id", "dtype": "string"}, {"name": "task", "dtype": "string"}, {"name": "src_img", "dtype": "image"}, {"name": "edited_img", "dtype": "image"}, {"name": "edited_prompt_list", "sequence": "string"}, {"name": "width", "dtype": "int64"}, {"name": "height", "dtype": "int64"}, {"name": "sc_score_1", "dtype": "int64"}, {"name": "sc_score_2", "dtype": "int64"}, {"name": "sc_reasoning", "dtype": "string"}, {"name": "pq_score", "dtype": "int64"}, {"name": "pq_reasoning", "dtype": "string"}, {"name": "o_score", "dtype": "float64"}], "splits": [{"name": "dev", "num_bytes": 1547839078, "num_examples": 700}, {"name": "train", "num_bytes": 2852916299223.88, "num_examples": 1202797}], "download_size": 2978259415518, "dataset_size": 2854464138301.88}, "configs": [{"config_name": "default", "data_files": [{"split": "dev", "path": "data/dev-*"}, {"split": "train", "path": "data/train-*"}]}], "tags": ["image"]}

82455c6cd66db7f0e5bfce8d7a236441af59d6df

OmniEdit In this paper, we present OMNI-EDIT, which is an omnipotent editor to handle seven different image editing tasks with any aspect ratio seamlessly. Our contribution is in four folds: (1) OMNI-EDIT is trained by utilizing the supervision from seven different specialist models to ensure task coverage. (2) we utilize importance sampling based on the scores provided by large multimodal models (like GPT-4o) instead of CLIP-score to improve the data quality. 📃Paper |… See the full description on the dataset page: https://huggingface.co/datasets/TIGER-Lab/OmniEdit-Filtered-1.2M.

6787deee734ac2badb1087a0

nvidia/AceMath-Instruct-Training-Data

nvidia

{"dataset_info": {"features": [{"name": "messages", "list": [{"name": "role", "dtype": "string"}, {"name": "content", "dtype": "string"}]}, {"name": "answer", "dtype": "string"}], "splits": [{"name": "general_sft_stage1"}, {"name": "general_sft_stage2"}, {"name": "math_sft"}]}, "configs": [{"config_name": "default", "data_files": [{"split": "general_sft_stage1", "path": "data/general_sft_stage1.parquet"}, {"split": "general_sft_stage2", "path": "data/general_sft_stage2.parquet"}, {"split": "math_sft", "path": "data/math_sft.parquet"}]}], "license": "cc-by-nc-4.0", "language": ["en"], "pipeline_tag": "text-generation", "tags": ["nvidia", "code", "math", "general_domain", "AceMath", "AceInstruct", "sft_dataset"]}

24e49e73a3ab3aa373e904516838c32a972ba75e

website | paper AceMath-Instruct Training Data Card We release all the datasets to train AceMath-1.5B/7B/72B-Instruct models. These models are built upon the Qwen2.5-Math-Base models through a multi-stage supervised fine-tuning (SFT) process. The fine-tuning begins with general-purpose SFT data (general_sft_stage1.parquet and general_sft_stage2.parquet) and is followed by math-specific SFT data (math_sft.parquet). In our experiments, fine-tuning the Qwen2.5-Math-Base models using… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/AceMath-Instruct-Training-Data.

6758176e04e2f15d7bfacd54

PowerInfer/QWQ-LONGCOT-500K

PowerInfer

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

10a787d967281599e9be6761717147817c018424

This repository contains approximately 500,000 instances of responses generated using QwQ-32B-Preview language model. The dataset combines prompts from multiple high-quality sources to create diverse and comprehensive training data. The dataset is available under the Apache 2.0 license. Over 75% of the responses exceed 8,000 tokens in length. The majority of prompts were carefully created using persona-based methods to create challenging instructions. Bias, Risks, and Limitations… See the full description on the dataset page: https://huggingface.co/datasets/PowerInfer/QWQ-LONGCOT-500K.

667ee649a7d8b1deba8d4f4c

proj-persona/PersonaHub

proj-persona

{"license": "cc-by-nc-sa-4.0", "task_categories": ["text-generation", "text-classification", "token-classification", "fill-mask", "table-question-answering", "text2text-generation"], "language": ["en", "zh"], "tags": ["synthetic", "text", "math", "reasoning", "instruction", "tool"], "size_categories": ["100K<n<1M"], "configs": [{"config_name": "math", "data_files": "math.jsonl"}, {"config_name": "instruction", "data_files": "instruction.jsonl"}, {"config_name": "reasoning", "data_files": "reasoning.jsonl"}, {"config_name": "knowledge", "data_files": "knowledge.jsonl"}, {"config_name": "npc", "data_files": "npc.jsonl"}, {"config_name": "tool", "data_files": "tool.jsonl"}, {"config_name": "persona", "data_files": "persona.jsonl"}]}

c91f99f3efd4d0977e338f3b77abd251653cd405

Scaling Synthetic Data Creation with 1,000,000,000 Personas This repo releases data introduced in our paper Scaling Synthetic Data Creation with 1,000,000,000 Personas: We propose a novel persona-driven data synthesis methodology that leverages various perspectives within a large language model (LLM) to create diverse synthetic data. To fully exploit this methodology at scale, we introduce PERSONA HUB – a collection of 1 billion diverse personas automatically curated from web… See the full description on the dataset page: https://huggingface.co/datasets/proj-persona/PersonaHub.

6745db412fd6d8c0251091c5

5CD-AI/LLaVA-CoT-o1-Instruct

5CD-AI

{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "image", "dtype": "image"}, {"name": "question", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "ground_truth", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4601941604.092, "num_examples": 58468}], "download_size": 3936031095, "dataset_size": 4601941604.092}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

f27312b2b25602b17222798b11837237eb8be4b7

Example1 Input: Please answer the question below, explaining your reasoning step by step before providing the final answer. Question: Are there enough straws for every cup ? A. yes B. no Output: <SUMMARY/>The question asks whether there are enough straws to provide one for each cup depicted in an image. To answer, we need to count the number of straws and cups separately and then compare those quantities.</SUMMARY> <CAPTION>The image shows… See the full description on the dataset page: https://huggingface.co/datasets/5CD-AI/LLaVA-CoT-o1-Instruct.

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.

664a1c1f4fa4afb446afa8f7

openbmb/RLAIF-V-Dataset

openbmb

{"license": "cc-by-nc-4.0", "task_categories": ["visual-question-answering"], "language": ["en"], "pretty_name": "RLAIF-V-Dataset", "dataset_info": {"features": [{"name": "ds_name", "dtype": "string"}, {"name": "image", "dtype": "image"}, {"name": "question", "dtype": "string"}, {"name": "chosen", "dtype": "string"}, {"name": "rejected", "dtype": "string"}, {"name": "origin_dataset", "dtype": "string"}, {"name": "origin_split", "dtype": "string"}, {"name": "idx", "dtype": "string"}, {"name": "image_path", "dtype": "string"}]}, "size_categories": ["10K<n<100K"]}

586aff0ea91b485a73fe99f65570f054c10c79d9

Dataset Card for RLAIF-V-Dataset GitHub | Paper News: [2024.05.28] 📃 Our paper is accesible at arxiv now! [2024.05.20] 🔥 Our data is used in MiniCPM-Llama3-V 2.5, which represents the first end-side MLLM achieving GPT-4V level performance! Dataset Summary RLAIF-V-Dataset is a large-scale multimodal feedback dataset. The dataset provides high-quality feedback with a total number of 83,132 preference pairs, where the instructions are collected… See the full description on the dataset page: https://huggingface.co/datasets/openbmb/RLAIF-V-Dataset.

6790e7b33f5d2b5e5b9e70a5

Rapidata/sora-video-generation-style-likert-scoring

Rapidata

{"dataset_info": {"features": [{"name": "Prompt", "dtype": "string"}, {"name": "Video", "dtype": "string"}, {"name": "LikertScore", "dtype": "float64"}, {"name": "LikertScoreNormalized", "dtype": "float64"}, {"name": "DetailedResults", "list": [{"name": "selectedCategory", "dtype": "string"}, {"name": "userDetails", "struct": [{"name": "age", "dtype": "string"}, {"name": "country", "dtype": "string"}, {"name": "gender", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "occupation", "dtype": "string"}, {"name": "userScore", "dtype": "float64"}]}]}, {"name": "FileName", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 435142, "num_examples": 198}], "download_size": 59159, "dataset_size": 435142}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "license": "apache-2.0", "task_categories": ["video-classification", "text-to-video"], "language": ["en"], "tags": ["t2v", "text2video", "texttovideo", "t2i", "likert", "scale", "human", "preference"], "pretty_name": "t2v Sora Style Likert Scores", "size_categories": ["1K<n<10K"]}

82c61ac450a434e38ddbe399ac8cf3e29813e54d

Rapidata Video Generation Preference Dataset If you get value from this dataset and would like to see more in the future, please consider liking it. This dataset was collected in ~1 hour using the Rapidata Python API, accessible to anyone and ideal for large scale data annotation. Overview In this dataset, ~6000 human evaluators were asked to rate AI-generated videos based on their visual appeal, without seeing the prompts used to generate them. The specific… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/sora-video-generation-style-likert-scoring.

67374c18c32c765810f748f6

HuggingFaceH4/MATH-500

HuggingFaceH4

{"task_categories": ["text-generation"], "language": ["en"], "pretty_name": "MATH-500"}

ff5b20257d8185524591543f8ff5993951537bb8

Dataset Card for MATH-500 This dataset contains a subset of 500 problems from the MATH benchmark that OpenAI created in their Let's Verify Step by Step paper. See their GitHub repo for the source file: https://github.com/openai/prm800k/tree/main?tab=readme-ov-file#math-splits

67903960be8d5adf69448b24

PrimeIntellect/NuminaMath-QwQ-CoT-5M

PrimeIntellect

{"dataset_info": {"features": [{"name": "problem_id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "ground_truth", "dtype": "string"}, {"name": "correct", "dtype": "bool"}], "splits": [{"name": "train", "num_bytes": 43783462002, "num_examples": 5138102}], "download_size": 18047330866, "dataset_size": 43783462002}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "license": "mit"}

ba3b9e4083982f67a2140bddb098d9e6678cbf77

INTELLECT-MATH: Frontier Mathematical Reasoning through Better Initializations for Reinforcement Learning INTELLECT-MATH is a 7B parameter model optimized for mathematical reasoning. It was trained in two stages, an SFT stage, in which the model was fine-tuned on verified QwQ outputs, and an RL stage, in which the model was trained using the PRIME-RL recipe. We demonstrate that the quality of our SFT data can impact the performance and training speed of the RL stage: Due to its… See the full description on the dataset page: https://huggingface.co/datasets/PrimeIntellect/NuminaMath-QwQ-CoT-5M.