datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
fineweb-edu-translated
Helsinki-NLP/fineweb-edu-translated
fineweb-edu-tanslated is a collection of automatically translated documents from fineweb-edu.
Translations are based on OPUS-MT and HPLT-MT models.
The data in v1.0 covers 36,704,000 documents with over 28 billion space-searated tokens of English data translated into 36 languages.
The total v1.0 data set includes over 960 billion tokens and the translated documents are aligned across all languages.
In the v1.1 release, additional translations… See the full description on the dataset page: https://huggingface.co/datasets/Helsinki-NLP/fineweb-edu-translated.truthful_qa
Dataset Card for truthful_qa
Dataset Summary
TruthfulQA is a benchmark to measure whether a language model is truthful in generating answers to questions. The benchmark comprises 817 questions that span 38 categories, including health, law, finance and politics. Questions are crafted so that some humans would answer falsely due to a false belief or misconception. To perform well, models must avoid generating false answers learned from imitating human texts.… See the full description on the dataset page: https://huggingface.co/datasets/truthfulqa/truthful_qa.stack-v3-train
🥞 The Stack v3
What is it?
What is being released
How to download and use it
Dataset statistics
Dataset structure
Dataset creation
Considerations for using the data
Additional information
What is it?
The Stack v3 is the largest, most up-to-date open dataset of source code, crawled directly from GitHub and built to pre-train code LLMs with full-repository context. It is the successor to The Stack v2 and, like its predecessor, is released to make the training… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceCode/stack-v3-train.UbuntuIRCCompletely uncurated collection of IRC logs from the Ubuntu IRC channels
alpaca
Dataset Card for Alpaca
Dataset Summary
Alpaca is a dataset of 52,000 instructions and demonstrations generated by OpenAI's text-davinci-003 engine. This instruction data can be used to conduct instruction-tuning for language models and make the language model follow instruction better.
The authors built on the data generation pipeline from Self-Instruct framework and made the following modifications:
The text-davinci-003 engine to generate the instruction data… See the full description on the dataset page: https://huggingface.co/datasets/tatsu-lab/alpaca.TinyStoriesDataset containing synthetically generated (by GPT-3.5 and GPT-4) short stories that only use a small vocabulary.
Described in the following paper: https://arxiv.org/abs/2305.07759.
The models referred to in the paper were trained on TinyStories-train.txt (the file tinystories-valid.txt can be used for validation loss). These models can be found on Huggingface, at roneneldan/TinyStories-1M/3M/8M/28M/33M/1Layer-21M.
Additional resources:
tinystories_all_data.tar.gz - contains a superset of… See the full description on the dataset page: https://huggingface.co/datasets/roneneldan/TinyStories.falcon-refinedweb
📀 Falcon RefinedWeb
Falcon RefinedWeb is a massive English web dataset built by TII and released under an ODC-By 1.0 license.
See the 📓 paper on arXiv for more details.
RefinedWeb is built through stringent filtering and large-scale deduplication of CommonCrawl; we found models trained on RefinedWeb to achieve performance in-line or better than models trained on curated datasets, while only relying on web data.
RefinedWeb is also "multimodal-friendly": it contains links and… See the full description on the dataset page: https://huggingface.co/datasets/tiiuae/falcon-refinedweb.nemotron-cc-translated
Helsinki-NLP/nemotron-cc-translated
nemotron-cc-tanslated is a collection of automatically translated documents from nemotron-cc taken out of the high-quality subset.
Translations are based on OPUS-MT and HPLT-MT models.
The data in v1.0 covers 156,431,999 documents with over 70 billion space-searated tokens of English data translated into 36 languages.
The total v1.0 data set includes over 2.4 trillion tokens and the translated documents are aligned across all languages.
v1.1… See the full description on the dataset page: https://huggingface.co/datasets/Helsinki-NLP/nemotron-cc-translated.tiny-supervised-datasetreddit_dataset_157
Bittensor Subnet 13 Reddit Dataset
Dataset Summary
This dataset is part of the Bittensor Subnet 13 decentralized network, containing preprocessed Reddit data. The data is continuously updated by network miners, providing a real-time stream of Reddit content for various analytical and machine learning tasks.
For more information about the dataset, please visit the official repository.
Supported Tasks
The versatility of this dataset allows… See the full description on the dataset page: https://huggingface.co/datasets/tensorshield/reddit_dataset_157.fineweb-tokenized
FineWeb Tokenized
> 4 trillion tokens of the pre-tokenized data the 🌐 web has to offer
What is it?
This is a pre-tokenized version of the HuggingFaceFW/fineweb dataset (currently in-progress, tokenization of the ~15 trillion tokens corpus is ongoing). The data is being pre-processed and tokenized using the AnisoleAI BPE tokenizer (52,022 vocabulary size) and packed into compact uint16 Parquet shards.
By distributing the pre-tokenized corpus, we eliminate… See the full description on the dataset page: https://huggingface.co/datasets/anisoleai/fineweb-tokenized.bigbenchBIG-Bench but it doesn't require the hellish dependencies (tensorflow, pypi-bigbench, protobuf) of the official version.
dataset = load_dataset("tasksource/bigbench",'movie_recommendation')
Code to reproduce:
https://colab.research.google.com/drive/1MKdLdF7oqrSQCeavAcsEnPdI85kD0LzU?usp=sharing
Datasets are capped to 50k examples to keep things light.
I also removed the default split when train was available also to save space, as default=train+val.
@article{srivastava2022beyond… See the full description on the dataset page: https://huggingface.co/datasets/tasksource/bigbench.ToolACE
ToolACE
ToolACE is an automatic agentic pipeline designed to generate Accurate, Complex, and divErse tool-learning data.
ToolACE leverages a novel self-evolution synthesis process to curate a comprehensive API pool of 26,507 diverse APIs.
Dialogs are further generated through the interplay among multiple agents, guided by a formalized thinking process.
To ensure data accuracy, we implement a dual-layer verification system combining rule-based and model-based checks.
More details… See the full description on the dataset page: https://huggingface.co/datasets/lockon/ToolACE.cc-traces-weka-062126
semianalysisai/cc-traces-weka-062126
WekaTrace corpus derived from SemiAnalysis Claude Code proxy traces. Built 2026-06-21 17:48:24 UTC via utils/agentic/build_weka_hf_dataset.py.
Filters
Trace version: exactly v7
min Anthropic requests per session: 20
Claude Code CLI ≥ 2.1.139 (every row)
peak concurrent sub-agent groups ≤ 10
Non-image rows only (image content excluded at source)
Classifier calls excluded (max_tokens<=64 AND no tools → SUGGESTION MODE, title-gen… See the full description on the dataset page: https://huggingface.co/datasets/semianalysisai/cc-traces-weka-062126.Lucie-Training-Dataset
Lucie Training Dataset Card
The Lucie Training Dataset is a curated collection of text data
in English, French, German, Spanish and Italian culled from a variety of sources including: web data, video subtitles, academic papers,
digital books, newspapers, and magazines, some of which were processed by Optical Character Recognition (OCR). It also contains samples of diverse programming languages.
The Lucie Training Dataset was used to pretrain Lucie-7B,
a foundation LLM with… See the full description on the dataset page: https://huggingface.co/datasets/OpenLLM-France/Lucie-Training-Dataset.ToolACE
ToolACE
ToolACE is an automatic agentic pipeline designed to generate Accurate, Complex, and divErse tool-learning data.
ToolACE leverages a novel self-evolution synthesis process to curate a comprehensive API pool of 26,507 diverse APIs.
Dialogs are further generated through the interplay among multiple agents, guided by a formalized thinking process.
To ensure data accuracy, we implement a dual-layer verification system combining rule-based and model-based checks.
More details… See the full description on the dataset page: https://huggingface.co/datasets/Team-ACE/ToolACE.glaive_toolcall_enBorrowed from: https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2
You can use it in LLaMA Factory by specifying dataset: glaive_toolcall_en.
the-stack-smol
Dataset Description
A small subset (~0.1%) of the-stack dataset, each programming language has 10,000 random samples from the original dataset. The dataset has 2.6GB of text (code).
Languages
The dataset contains 30 programming languages:
"assembly", "batchfile", "c++", "c", "c-sharp", "cmake", "css", "dockerfile", "fortran", "go", "haskell", "html", "java",
"javascript", "julia", "lua", "makefile", "markdown", "perl", "php", "powershell", "python", "ruby", "rust"… See the full description on the dataset page: https://huggingface.co/datasets/bigcode/the-stack-smol.OpenThoughts3-1.2M
paper |
dataset |
model
[!NOTE]
We have released a paper for OpenThoughts! See our paper here.
OpenThoughts3-1.2M
Open-source state-of-the-art reasoning dataset with 1.2M rows. 🚀
OpenThoughts3-1.2M is the third iteration in our line of OpenThoughts datasets, building on our previous OpenThoughts-114k and OpenThoughts2-1M.
This time around, we scale even further and generate our dataset in a much more systematic way -- OpenThoughts3-1.2M is the result of a… See the full description on the dataset page: https://huggingface.co/datasets/open-thoughts/OpenThoughts3-1.2M.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.MathInstruct
🦣 MAmmoTH: Building Math Generalist Models through Hybrid Instruction Tuning
MathInstruct is a meticulously curated instruction tuning dataset that is lightweight yet generalizable. MathInstruct is compiled from 13 math rationale datasets, six of which are newly curated by this work. It uniquely focuses on the hybrid use of chain-of-thought (CoT) and program-of-thought (PoT) rationales, and ensures extensive coverage of diverse mathematical fields.
Project Page:… See the full description on the dataset page: https://huggingface.co/datasets/TIGER-Lab/MathInstruct.cc-traces-weka-062126-256k
semianalysisai/cc-traces-weka-062126-256k
WekaTrace corpus derived from SemiAnalysis Claude Code proxy traces. Built 2026-06-21 17:49:45 UTC via utils/agentic/build_weka_hf_dataset.py.
Derived from semianalysisai/cc-traces-weka-062126 by applying the 256k per-request cap and preserving the surviving requests' relative timestamps.
Filters
Trace version: exactly v7
min Anthropic requests per session: 20
Claude Code CLI ≥ 2.1.139 (every row)
peak concurrent… See the full description on the dataset page: https://huggingface.co/datasets/semianalysisai/cc-traces-weka-062126-256k.FIM-Midtraining-400K
FIM-Midtraining-400K
📄 Paper · 💻 GitHub · 🤗 Collection
The mid-training corpus of "Function-Aware Fill-in-the-Middle as Mid-Training for Coding Agent Foundation Models": 400K function-aware FIM samples (~2.6B tokens under the Qwen2.5-Coder tokenizer) drawn from 75,568 Python files across 968 permissively-licensed GitHub repositories, fully decontaminated against SWE-Bench.
A coding agent's inner loop — act → observe → continue — is structurally isomorphic to a function call… See the full description on the dataset page: https://huggingface.co/datasets/TIGER-Lab/FIM-Midtraining-400K.po_qwen14b_tabular_data
BoLT Prompt Optimization — Tabular Dataset
For prompt optimization tasks in BoLT, an accessible benchmark for black-box optimization on LLM tasks.
Dataset Description
The dataset covers 5,014 evaluated instructions. Each row is a candidate system-prompt instruction paired with its empirically measured MATH-500 (4-shot, non-thinking mode) scores.
Evaluation details:
Model: Qwen/Qwen3-14B
Task: minerva_math500 (4-shot) (from lm-eval library)
System prompt:… See the full description on the dataset page: https://huggingface.co/datasets/chewwt/po_qwen14b_tabular_data.eai-taxonomy-code-w-dclm
💻 EAI-Taxonomy Code w/ DCLM
🏆 Website | 🖥️ Code | 📖 Paper
A 564 billion token dataset of high-quality code curated from web data using taxonomy-based filtering.
🎯 Dataset Overview
This dataset is part of the Essential-Web project, which introduces a new paradigm for dataset curation using expressive metadata and simple semantic filters. Unlike traditional code datasets that require complex domain-specific pipelines, our approach leverages a 12-category taxonomy… See the full description on the dataset page: https://huggingface.co/datasets/EssentialAI/eai-taxonomy-code-w-dclm.the-stack-dedup
Dataset Card for The Stack
Changelog
Release
Description
v1.0
Initial release of the Stack. Included 30 programming languages and 18 permissive licenses. Note: Three included licenses (MPL/EPL/LGPL) are considered weak copyleft licenses. The resulting near-deduplicated dataset is 1.5TB in size.
v1.1
The three copyleft licenses ((MPL/EPL/LGPL) were excluded and the list of permissive licenses extended to 193 licenses in total. The list of programming… See the full description on the dataset page: https://huggingface.co/datasets/bigcode/the-stack-dedup.SEC-EDGARDatamule, Teraflop AI, and Eventual collaborated to release the SEC-EDGAR dataset.
The dataset contains 590 gbs of data, spanning 8 million samples and 43 billion tokens from all major filings in the SEC EDGAR database.
The bulk data was collected using datamule-python library and the official datamule api created by John Friedman. The datamule Python library is a package for collecting, manipulating, and processing the SEC Edgar data at scale. Datamule provides a simple open-source api… See the full description on the dataset page: https://huggingface.co/datasets/TeraflopAI/SEC-EDGAR.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.Mixture-of-Thoughts
Dataset summary
Mixture-of-Thoughts is a curated dataset of 350k verified reasoning traces distilled from DeepSeek-R1. The dataset spans tasks in mathematics, coding, and science, and is designed to teach language models to reason step-by-step. It was used in the Open R1 project to train OpenR1-Distill-7B, an SFT model that replicates the reasoning capabilities of deepseek-ai/DeepSeek-R1-Distill-Qwen-7B from the same base model.
To load the dataset, run:
from datasets import… See the full description on the dataset page: https://huggingface.co/datasets/open-r1/Mixture-of-Thoughts.SWE-smith-trajectories
SWE-smith Trajectories
Code
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Paper
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Site
This dataset contains the 5017 trajectories we fine-tuned Qwen 2.5 Coder Instruct on, leading to
SWE-agent-LM-32B, a coding LM agent that
achieve 40.2% on SWE-bench Verified (no verifiers or multiple rollouts, just 1 attempt per instance).
Trajectories were generated by running SWE-agent + Claude 3.7 Sonnet on task instances from
the SWE-smith dataset.