datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
IFEval
Dataset Card for IFEval
Dataset Summary
This dataset contains the prompts used in the Instruction-Following Eval (IFEval) benchmark for large language models. It contains around 500 "verifiable instructions" such as "write in more than 400 words" and "mention the keyword of AI at least 3 times" which can be verified by heuristics. To load the dataset, run:
from datasets import load_dataset
ifeval = load_dataset("google/IFEval")
Supported Tasks and… See the full description on the dataset page: https://huggingface.co/datasets/google/IFEval.gpqa
Dataset Card for GPQA
GPQA is a multiple-choice, Q&A dataset of very hard questions written and validated by experts in biology, physics, and chemistry. When attempting questions out of their own domain (e.g., a physicist answers a chemistry question), these experts get only 34% accuracy, despite spending >30m with full access to Google.
We request that you do not reveal examples from this dataset in plain text or images online, to reduce the risk of leakage into foundation… See the full description on the dataset page: https://huggingface.co/datasets/Idavidrein/gpqa.MegaMath
MegaMath: Pushing the Limits of Open Math Copora
Megamath is part of TxT360, curated by LLM360 Team.
We introduce MegaMath, an open math pretraining dataset curated from diverse, math-focused sources, with over 300B tokens.
MegaMath is curated via the following three efforts:
Revisiting web data:
We re-extracted mathematical documents from Common Crawl with math-oriented HTML optimizations, fasttext-based filtering and deduplication, all for acquiring higher-quality data on… See the full description on the dataset page: https://huggingface.co/datasets/IFM/MegaMath.Magicoder-OSS-Instruct-75KThis is the OSS-Instruct dataset generated by gpt-3.5-turbo-1106 developed by OpenAI. Please pay attention to OpenAI's usage policy when adopting this dataset: https://openai.com/policies/usage-policies.
hacker-news
Hacker News - Complete Archive
Every Hacker News item since 2006, live-updated every 5 minutes
What is it?
This dataset contains the complete Hacker News archive: every story, comment, Ask HN, Show HN, job posting, and poll ever submitted to the site. Hacker News is one of the longest-running and most influential technology communities on the internet, operated by Y Combinator since 2007. It has become the de facto gathering place for founders, engineers… See the full description on the dataset page: https://huggingface.co/datasets/open-index/hacker-news.Code-Reasoning
Code-Reasoning
Dataset Description
Code problem-solving data with reasoning, direct-answer, and task-synthesis subsets. This repository is part of the K2 Horizon collection.
The repository is organized into multiple subsets. Every subset has a train split backed by Parquet shards, which supports Dataset Viewer inspection and streaming access.
K2 Horizon Dataset Series
Dataset repository
Focus
Subsets
IFM/TxT360-v2
Web and… See the full description on the dataset page: https://huggingface.co/datasets/IFM/Code-Reasoning.infini-news-corpus
INFINI-NEWS Corpus
🔎 Search this corpus online: query it with sub-second full-text search and n-gram counts — in the browser or via a public, keyless REST API, no download required — at infini-news.uni-graz.at (API reference).
A multilingual news corpus extracted from
Common Crawl CC-News WARC files.
One row per article, with body text extracted via
trafilatura,
WARC provenance, and derived metadata (publish date, language, topic,
byte hashes) in a single flat schema. Covers… See the full description on the dataset page: https://huggingface.co/datasets/ruggsea/infini-news-corpus.Magicoder-Evol-Instruct-110KA decontaminated version of evol-codealpaca-v1. Decontamination is done in the same way as StarCoder (bigcode decontamination process).
python_code_instructions_18k_alpaca
Dataset Card for python_code_instructions_18k_alpaca
The dataset contains problem descriptions and code in python language.
This dataset is taken from sahil2801/code_instructions_120k, which adds a prompt column in alpaca style. Refer to the source here.
Math-Reasoning
Math-Reasoning
Dataset Description
Mathematical problem-solving, rewriting, and dialogue data for reasoning-oriented language-model training. This repository is part of the K2 Horizon collection.
The repository is organized into multiple subsets. Every subset has a train split backed by Parquet shards, which supports Dataset Viewer inspection and streaming access.
K2 Horizon Dataset Series
Dataset repository
Focus
Subsets
IFM/TxT360-v2… See the full description on the dataset page: https://huggingface.co/datasets/IFM/Math-Reasoning.Pretrain-Behaviors
Pretrain-Behaviors
Dataset Description
Behavior-focused text covering reasoning, planning, data science, games, general content, and format rewriting. This repository is part of the K2 Horizon collection.
The repository is organized into multiple subsets. Every subset has a train split backed by Parquet shards, which supports Dataset Viewer inspection and streaming access.
K2 Horizon Dataset Series
Dataset repository
Focus
Subsets… See the full description on the dataset page: https://huggingface.co/datasets/IFM/Pretrain-Behaviors.TxT360-v2
TxT360-v2
Dataset Description
Pre-training sources for the K2 Horizon training data release. This repository is part of the K2 Horizon collection.
The repository is organized into multiple subsets. Every subset has a train split backed by Parquet shards.
K2 Horizon Dataset Series
Dataset repository
Focus
Subsets
IFM/TxT360-v2
Web and question-answering text
3
IFM/Code-Reasoning
Code reasoning and task synthesis
7
IFM/Math-Reasoning… See the full description on the dataset page: https://huggingface.co/datasets/IFM/TxT360-v2.open-australian-legal-corpus
Open Australian Legal Corpus ⚖️
The Open Australian Legal Corpus by Isaacus, a foundational legal AI research company, is the first and only multijurisdictional open corpus of Australian legislative and judicial documents.
Comprised of 229,122 texts totalling over 60 million lines and 1.4 billion tokens, the Corpus includes every in force statute and regulation in the Commonwealth, New South Wales, Queensland, Western Australia, South Australia, Tasmania and Norfolk Island, in… See the full description on the dataset page: https://huggingface.co/datasets/isaacus/open-australian-legal-corpus.tulu-3-sft-personas-instruction-following
Dataset Descriptions
This dataset contains 29980 examples and is synthetically created to enhance model's capabilities to follow instructions precisely and to satisfy user constraints. The constraints are borrowed from the taxonomy in IFEval dataset.
To generate diverse instructions, we expand the methodology in Ge et al., 2024 by using personas. More details and exact prompts used to construct the dataset can be found in our paper.
Curated by: Allen Institute for AI
Paper: TBD… See the full description on the dataset page: https://huggingface.co/datasets/allenai/tulu-3-sft-personas-instruction-following.ChartNet
ChartNet: A Million-Scale Multimodal Dataset for Chart Understanding
🌐 Homepage | 📖 arXiv
📝 Changelog
June 3, 2026 — Release of grounded_qa subset and completed reasoning subset (both subject to Notice Regarding Data Availability)
May 15, 2026 — Added link to 30K real-world charts and detailed captions dataset released by our collaborators Abaka AI/2077AI.
April 29, 2026 — Release of an additional 2.5 million row subset core_permissive (subject to… See the full description on the dataset page: https://huggingface.co/datasets/ibm-granite/ChartNet.Long-Horizon-Terminal-Bench
Long-Horizon Terminal-Bench (LHTB)
LHTB is a 46-task benchmark for measuring how well LLM agents sustain useful
work in a containerized terminal over hundreds of steps. Unlike short-horizon
coding benchmarks where an agent writes one artifact and stops, LHTB drops the agent
into a stateful environment and grades it with hidden, rebuild-from-artifact
verifiers — self-reported progress does not count.
📝 Blog: https://zli12321.github.io/LHTB/
🏆 Leaderboard:… See the full description on the dataset page: https://huggingface.co/datasets/IntelligenceLab/Long-Horizon-Terminal-Bench.fineweb
🍷 FineWeb
15 trillion tokens of the finest data the 🌐 web has to offer
What is it?
The 🍷 FineWeb dataset consists of more than 18.5T tokens (originally 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… See the full description on the dataset page: https://huggingface.co/datasets/idleengine/fineweb.muri-it-language-split
MURI-IT: Multilingual Instruction Tuning Dataset for 200 Languages via Multilingual Reverse Instructions
MURI-IT is a large-scale multilingual instruction tuning dataset containing 2.2 million instruction-output pairs across 200 languages. It is designed to address the challenges of instruction tuning in low-resource languages with Multilingual Reverse Instructions (MURI), which ensures that the output is human-written, high-quality, and authentic to the cultural and linguistic… See the full description on the dataset page: https://huggingface.co/datasets/akoksal/muri-it-language-split.HumanEval-XLThis dataset contains a viewer-friendly version of the dataset at FloatAI/HumanEval-XL. It is made available separately for the convenience of the vllm-code-harness package.
InstructCoder
Paper |
Code |
Blog
InstructCoder (CodeInstruct): Empowering Language Models to Edit Code
Updates
May 23, 2023: Paper, code and data released.
Overview
InstructCoder is the first dataset designed to adapt LLMs for general code editing. It consists of 114,239 instruction-input-output triplets and covers multiple distinct code editing scenarios, generated by ChatGPT. LLaMA-33B finetuned on InstructCoder performs on par with ChatGPT on a… See the full description on the dataset page: https://huggingface.co/datasets/likaixin/InstructCoder.SWE-Fixer-Train-110K
SWE-Fixer: Training Open-Source LLMs for Effective and Efficient GitHub Issue Resolution
📃 Paper |
🚀 GitHub
SWE-Fixer is a simple yet effective solution for addressing real-world GitHub issues by training open-source LLMs. It features a streamlined retrieve-then-edit pipeline with two core components: a code file retriever and a code editor.
This repo holds the data SWE-Fixer-Train-110K we curated for SWE-Fixer training.
For more information, please visit our project page.… See the full description on the dataset page: https://huggingface.co/datasets/internlm/SWE-Fixer-Train-110K.IndustryCorpus[中文主页]
Industry models play a crucial role in driving enterprise intelligence transformation and innovative development. High-quality industry data is key to improving the performance of large models and realizing industry applications. However, datasets currently used for industry model training generally suffer from issues such as insufficient data volume, low quality, and lack of domain expertise.
To address these problems, we constructed and applied 22 industry data processing operators to… See the full description on the dataset page: https://huggingface.co/datasets/BAAI/IndustryCorpus.ifeval-like-data
IFEval Like Data
This dataset contains instruction-response pairs synthetically generated using Qwen/Qwen2.5-72B-Instruct following the style of google/IFEval dataset and verified for correctness with lm-evaluation-harness. The dataset contains two subsets:
default: which contains 550k unfiltered rows synthetically generated with Qwen2.5-72B-Instruct, a few system prompts and MagPie prompting technique. The prompts can contain conflicting instructions as defined in… See the full description on the dataset page: https://huggingface.co/datasets/argilla/ifeval-like-data.host-index-testing-v2
Common Crawl Host Index v2
GitHub: https://github.com/commoncrawl/cc-host-index
Each crawl, we generate a Host Index, which aggregates information about each web hosted visited during the crawl. The
information is aggregated from the Common Crawl columnar index,
web graph, and raw crawler logs.
Quickstart
The dataset is Hive-partitioned on crawl (data/crawl=CC-MAIN-2025-18/*.parquet). Open the whole
dataset once, then filter with WHERE crawl = '...': because… See the full description on the dataset page: https://huggingface.co/datasets/commoncrawl/host-index-testing-v2.in-the-wild-jailbreak-prompts
In-The-Wild Jailbreak Prompts on LLMs
This is the official repository for the ACM CCS 2024 paper "Do Anything Now'': Characterizing and Evaluating In-The-Wild Jailbreak Prompts on Large Language Models by Xinyue Shen, Zeyuan Chen, Michael Backes, Yun Shen, and Yang Zhang.
In this project, employing our new framework JailbreakHub, we conduct the first measurement study on jailbreak prompts in the wild, with 15,140 prompts collected from December 2022 to December 2023 (including 1,405… See the full description on the dataset page: https://huggingface.co/datasets/TrustAIRLab/in-the-wild-jailbreak-prompts.imatrix-calibration
Importance Matrix Calibration Datasets
This repository provides calibration datasets used to generate importance matrices (imatrix), which are required to minimize errors when quantizing models with LLaMA C++.
The llama-imatrix program cannot handle parquet files directly and thus requires them to be converted into text format first. There are many ways to do this but a simple approach is to use DuckDB with the following command: duckdb -noheader -ascii -c "SELECT content FROM… See the full description on the dataset page: https://huggingface.co/datasets/eaddario/imatrix-calibration.Trendyol-Cybersecurity-Instruction-Tuning-Dataset
Trendyol Cybersecurity Defense Instruction-Tuning Dataset (v2.0)
🚀 TL;DR
53,202 meticulously curated system/user/assistant instruction-tuning examples covering 200+ specialized cybersecurity domains. Built by the Trendyol Security Team for training state-of-the-art defensive security AI assistants. Expanded from 21K to 53K rows with comprehensive coverage of modern security challenges including cloud-native threats, AI/ML security, quantum computing risks… See the full description on the dataset page: https://huggingface.co/datasets/Trendyol/Trendyol-Cybersecurity-Instruction-Tuning-Dataset.Nemotron-SFT-Instruction-Following-Chat-v3
Dataset Description:
The Nemotron-Instruction-Following-Chat-v3 dataset is designed to strengthen multi-turn, interactive capabilities, including open-ended chat and precise instruction following.
The chat subset uses human written prompts from sources like lmarena, lmsys, and wildchat as seed prompts. Responses are generated with GLM-5. Multiple responses are sampled from the model and the best response as judged by pairwise comparisons using Qwen3-Nemotron-235B-A22B-GenRM-2603… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-SFT-Instruction-Following-Chat-v3.SYNTH
SYNTH
SYNTH is the first open generalist synthetic dataset for training small reasoning model end-to-end, jointly released by Pleias and the AI Alliance.
SYNTH includes 79,648,272 individual text samples, comprising over 41 billion words (about 75 billion tokens with Pleias tokenizer). It is based on the amplification of 58,698 articles from Wikipedia and made possible thanks to the Structured Wikipedia dataset from Wikimedia Enterprise.
SYNTH differs from existing open synthetic… See the full description on the dataset page: https://huggingface.co/datasets/SYNTH-Initiative/SYNTH.kernelbench-hard-traces
KernelBench-Hard agent traces
Frontier coding agents writing optimized CUDA/Triton kernels (FP8 GEMM, paged
attention, MoE, W4A16, KDA, Top-k) on RTX PRO 6000 Blackwell, H100 PCIe, and
B200; roofline-graded.
Each .jsonl file is one agent run in Claude-Code session format, viewable with
the Hugging Face Agent Trace viewer (Data Studio → open a row). Filename =
run id.
Live leaderboard: https://kernelbench.com/hard
Secrets redacted. Full reasoning for open-provider routes… See the full description on the dataset page: https://huggingface.co/datasets/Infatoshi/kernelbench-hard-traces.