6763e94724dee5a47c7c77f7

agibot-world/AgiBotWorld-Alpha

agibot-world

{"pretty_name": "AgiBot World", "size_categories": ["n>1T"], "task_categories": ["other"], "language": ["en"], "tags": ["real-world", "dual-arm", "Robotics manipulation"], "extra_gated_prompt": "### AgiBot World COMMUNITY LICENSE AGREEMENT\nAgiBot World Alpha Release Date: December 30, 2024 All the data and code within this repo are under [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/).", "extra_gated_fields": {"First Name": "text", "Last Name": "text", "Email": "text", "Country": "country", "Affiliation": "text", "Phone": "text", "Job title": {"type": "select", "options": ["Student", "Research Graduate", "AI researcher", "AI developer/engineer", "Reporter", "Other"]}, "Research interest": "text", "geo": "ip_location", "By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the AgiBot Privacy Policy": "checkbox"}, "extra_gated_description": "The information you provide will be collected, stored, processed and shared in accordance with the AgiBot Privacy Policy.", "extra_gated_button_content": "Submit"}

958989ee5a97e932bdb4bb64ca5d4610b1838293

Key Features 🔑 1 million+ trajectories from 100 robots. 100+ real-world scenarios across 5 target domains. Cutting-edge hardware: visual tactile sensors / 6-DoF dexterous hand / mobile dual-arm robots Tasks involving: Contact-rich manipulation Long-horizon planning Multi-robot collaboration Your browser does not support the video tag. Your browser does not support the video tag.… See the full description on the dataset page: https://huggingface.co/datasets/agibot-world/AgiBotWorld-Alpha.

63990f21cc50af73d29ecfa3

fka/awesome-chatgpt-prompts

fka

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

459a66186f8f83020117b8acc5ff5af69fc95b45

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

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.

673e9e53cdad8a9744b0bf1b

O1-OPEN/OpenO1-SFT

O1-OPEN

{"license": "apache-2.0", "task_categories": ["question-answering"], "language": ["en", "zh"], "size_categories": ["10K<n<100K"]}

63112de109aa755e9cdfad63a13f08a92dd7df36

SFT Data for CoT Activation 🎉🎉🎉This repository contains the dataset used for fine-tuning a language model using SFT for Chain-of-Thought Activation. 🌈🌈🌈The dataset is designed to enhance the model's ability to generate coherent and logical reasoning sequences. ☄☄☄By using this dataset, the model can learn to produce detailed and structured reasoning steps, enhancing its performance on complex reasoning tasks. Statistics 1️⃣Total Records: 77,685… See the full description on the dataset page: https://huggingface.co/datasets/O1-OPEN/OpenO1-SFT.

67449661149efb6edaa63b98

HuggingFaceTB/finemath

HuggingFaceTB

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8f233cf84cff0b817b3ffb26d5be7370990dd557

📐 FineMath What is it? 📐 FineMath consists of 34B tokens (FineMath-3+) and 54B tokens (FineMath-3+ with InfiMM-WebMath-3+) of mathematical educational content filtered from CommonCrawl. To curate this dataset, we trained a mathematical content classifier using annotations generated by LLama-3.1-70B-Instruct. We used the classifier to retain only the most educational mathematics content, focusing on clear explanations and step-by-step problem solving rather than… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceTB/finemath.

66cbf7ef92e9f5b19fcd65aa

cfahlgren1/react-code-instructions

cfahlgren1

{"license": "mit", "pretty_name": "React Code Instructions"}

e8b8355ce7be41a3e1b0405be2f11934f6d5880c

React Code Instructions Dataset of Claude Artifact esque React Apps generated by Llama 3.1 70B, Llama 3.1 405B, and Deepseek Chat V3. Examples Virtual Fitness Trainer Website LinkedIn Clone iPhone Calculator Chipotle Waitlist Apple Store

66a6da71f0dc7c8df2e0f979

OpenLeecher/lmsys_chat_1m_clean

OpenLeecher

{"language": ["en"], "size_categories": ["100K<n<1M"], "pretty_name": "Cleaned LMSYS dataset", "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "conversations", "list": [{"name": "from", "dtype": "string"}, {"name": "value", "dtype": "string"}]}, {"name": "category", "dtype": "string"}, {"name": "grounded", "dtype": "bool"}, {"name": "deepseek_response", "struct": [{"name": "moralization", "dtype": "int64"}, {"name": "reward", "dtype": "float64"}, {"name": "value", "dtype": "string"}]}, {"name": "phi-3-mini_response", "struct": [{"name": "moralization", "dtype": "int64"}, {"name": "reward", "dtype": "float64"}, {"name": "value", "dtype": "string"}]}, {"name": "flaw", "dtype": "string"}, {"name": "agreement", "dtype": "bool"}], "splits": [{"name": "train", "num_bytes": 1673196622, "num_examples": 273402}], "download_size": 906472159, "dataset_size": 1673196622}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}

e9f2f6838a2dbba87c216bb6bc406e8d7ce0f389

Cleaning and Categorizing A few weeks ago, I had the itch to do some data crunching, so I began this project - to clean and classify lmsys-chat-1m. The process was somewhat long and tedious, but here is the quick overview: 1. Removing Pure Duplicate Instructions The first step was to eliminate pure duplicate instructions. This involved: Removing whitespace and punctuation. Ensuring that if two instructions matched after that, only one was retained. This step… See the full description on the dataset page: https://huggingface.co/datasets/OpenLeecher/lmsys_chat_1m_clean.

676f70846bf205795346d2be

FreedomIntelligence/medical-o1-reasoning-SFT

FreedomIntelligence

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

04c3d3370e6dc73f7773ddf373d1ac86596dded5

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.

625552d2b339bb03abe3432d

openai/gsm8k

openai

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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

66212f29fb07c3e05ad0432e

HuggingFaceFW/fineweb

HuggingFaceFW

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e31fdfd3918d4b48e837d69d274e624a067d7091

🍷 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… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceFW/fineweb.

67734d5c7ec2413faa8d3c85

PowerInfer/LONGCOT-Refine-500K

PowerInfer

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

88bf8410db01197006e572a46c88311720a23577

This repository contains approximately 500,000 instances of responses generated using Qwen2.5-72B-Instruct. 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. Bias, Risks, and Limitations This dataset is mainly in English. The dataset inherits the biases, errors, and omissions known to exist in data used for seed sources and models used for data generation.… See the full description on the dataset page: https://huggingface.co/datasets/PowerInfer/LONGCOT-Refine-500K.

67514cb8ff3dfacd1b313a33

amphora/QwQ-LongCoT-130K

amphora

{"dataset_info": {"features": [{"name": "problem", "dtype": "string"}, {"name": "qwq", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "split", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 969051509, "num_examples": 133102}], "download_size": 420996585, "dataset_size": 969051509}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "license": "apache-2.0", "task_categories": ["text-generation"], "language": ["en"]}

cb5624e9a538259c5f5ed9d5869f7a2565606e38

Also have a look on the second version here => QwQ-LongCoT-2 Figure 1: Just a cute picture generate with [Flux](https://huggingface.co/Shakker-Labs/FLUX.1-dev-LoRA-Logo-Design) Today, I’m excited to release QwQ-LongCoT-130K, a SFT dataset designed for training O1-like large language models (LLMs). This dataset includes about 130k instances, each with responses generated using QwQ-32B-Preview. The dataset is available under the Apache 2.0 license, so feel free to use it as you like.… See the full description on the dataset page: https://huggingface.co/datasets/amphora/QwQ-LongCoT-130K.

6765c1e881a1f37bc67ec56d

OpenGVLab/MMPR-v1.1

OpenGVLab

{"license": "mit", "task_categories": ["visual-question-answering"], "language": ["en"], "pretty_name": "MMPR", "dataset_info": {"features": [{"name": "image", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "chosen", "dtype": "string"}, {"name": "rejected", "dtype": "string"}]}, "size_categories": ["1M<n<10M"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "annotations.zip"}]}]}

f4f3c430f6b37a1d8406d08336a4d9bcf64ace1a

MMPR-v1.1 [📂 GitHub] [🆕 Blog] [📜 Paper] [📖 Documents] This is a newer version of MMPR, which includes additional data sources to enhance the data diversity and improves the performance of InternVL2.5 by an average of 2 points across all scales on the OpenCompass leaderboard. To unzip the archive of images, please first run cat images.zip_* > images.zip and then run unzip images.zip. Introduction MMPR is a large-scale and high-quality multimodal reasoning… See the full description on the dataset page: https://huggingface.co/datasets/OpenGVLab/MMPR-v1.1.