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", "synthetic"]}

423915271797e7b0ebd28898a93b0e5cf18449e2

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 31.3 83.0 42.4 75.3 28.6 6.5 39.9 Bespoke-Stratos-7B 22.7 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

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-*"}]}]}

ea6046c089a41774665efe3c729e1c3284854134

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.

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.

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.

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.

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.

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.

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.

67a4af5e4ccbc3656f0b4c7c

saiyan-world/Goku-MovieGenBench

saiyan-world

63f0a9750df6a8350c01ecd54afb1ae85d9ae0f9

67aa021ced8d8663d42505cc

open-r1/OpenR1-Math-220k

open-r1

{"license": "apache-2.0", "language": ["en"], "configs": [{"config_name": "all", "data_files": [{"split": "default", "path": "default/train-*"}, {"split": "extended", "path": "extended/train-*"}]}]}

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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.

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

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.

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.

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.

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.

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.

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.

67a150e7497722c0232eaa2b

TIGER-Lab/AceCode-87K

TIGER-Lab

{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "test_cases", "sequence": "string"}, {"name": "inferences", "list": [{"name": "completion", "dtype": "string"}, {"name": "completion_id", "dtype": "int64"}, {"name": "model_name", "dtype": "string"}, {"name": "pass_rate", "dtype": "float64"}]}, {"name": "context_messages", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 5107472226, "num_examples": 87149}], "download_size": 1014477327, "dataset_size": 5107472226}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "license": "mit", "language": ["en"], "tags": ["acecode", "code"], "pretty_name": "AceCoder-87K", "size_categories": ["10K<n<100K"]}

13216309a9f6cb40b60cb1a9750071efeac414ad

🂡 AceCode-87K Paper | Github | AceCode-87K | AceCodePair-300K | RM/RL Models We introduce AceCoder, the first work to propose a fully automated pipeline for synthesizing large-scale reliable tests used for the reward model training and reinforcement learning in the coding scenario. To do this, we curated the dataset AceCode-87K, where we start from a seed code dataset and prompt powerful LLMs to "imagine" proper test cases for the coding question and filter the noisy ones. We… See the full description on the dataset page: https://huggingface.co/datasets/TIGER-Lab/AceCode-87K.

6532270e829e1dc2f293d6b8

gaia-benchmark/GAIA

gaia-benchmark

{"language": ["en"], "pretty_name": "General AI Assistants Benchmark"}

06dfd28455b4bb0ee76378b97adc2c70aa43b701

GAIA dataset GAIA is a benchmark which aims at evaluating next-generation LLMs (LLMs with augmented capabilities due to added tooling, efficient prompting, access to search, etc). We added gating to prevent bots from scraping the dataset. Please do not reshare the validation or test set in a crawlable format. Data and leaderboard GAIA is made of more than 450 non-trivial question with an unambiguous answer, requiring different levels of tooling and autonomy to… See the full description on the dataset page: https://huggingface.co/datasets/gaia-benchmark/GAIA.

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

672068775ec8b84bfac97554

hamim-87/Ashrafur_bangla_math

hamim-87

87730f5ee633909b28b4ba64ea6f999c793b3751