datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
tiny-supervised-datasetxlam-function-calling-60k
APIGen Function-Calling Datasets
Paper | Website | Models
This repo contains 60,000 data collected by APIGen, an automated data generation pipeline designed to produce verifiable high-quality datasets for function-calling applications. Each data in our dataset is verified through three hierarchical stages: format checking, actual function executions, and semantic verification, ensuring its reliability and correctness.
We conducted human evaluation over 600 sampled data points, and… See the full description on the dataset page: https://huggingface.co/datasets/lockon/xlam-function-calling-60k.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.soc-ratchakitcha
Royal Gazette Thailand (Ratchakitcha) Dataset
ชุดข้อมูลราชกิจจานุเบกษา (แบบ Machine Readable)
โครงการ Open Law Data Thailand ร่วมกับคณะกรรมาธิการการพาณิชย์และการอุตสาหกรรม วุฒิสภา ได้รับความอนุเคราะห์ข้อมูลจาก สำนักเลขาธิการคณะรัฐมนตรี (สลค.) เพื่อเผยแพร่ข้อมูลกฎหมายไทยสู่สาธารณะในรูปแบบที่ประมวลผลได้ด้วยคอมพิวเตอร์ (Machine Readable) เพื่อส่งเสริมนวัตกรรม Legal Tech และ AI ของประเทศไทย
Dataset Description
ชุดข้อมูลนี้รวบรวมรายการประกาศในราชกิจจานุเบกษา… See the full description on the dataset page: https://huggingface.co/datasets/open-law-data-thailand/soc-ratchakitcha.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.
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.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.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.swallow-math-v2
SwallowMath-v2
Resources
📑 arXiv: Read our paper for detailed methodology at arXiv:2505.02881.
🤗 Sister Dataset: Discover SwallowCode2, our companion dataset for code generation.
🧮 What is it?
SwallowMath-v2 is a large-scale mathematical dataset containing 32 billion tokens, developed as the successor to SwallowMath-v1.
Building on the success of v1, this release aims to construct a larger-scale and more permissively licensed corpus to support open and… See the full description on the dataset page: https://huggingface.co/datasets/tokyotech-llm/swallow-math-v2.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.RegexEval
Dataset Card for RegexEval
Re(gEx|DoS)Eval is a framework that includes a dataset of 762 regex descriptions (prompts) from real users, refined prompts with examples, and a robust set of tests.
Dataset Details
Dataset Sources
Repository: https://github.com/s2e-lab/RegexEval
Paper: https://s2e-lab.github.io/preprints/icse_nier24-preprint.pdf
Dataset Structure
dataset.jsonl: dataset file in jsonl format. Every line contains a JSON object with… See the full description on the dataset page: https://huggingface.co/datasets/s2e-lab/RegexEval.swallow-code-v2
SwallowCode-v2
Resources
📑 arXiv: Read our paper for detailed methodology and results at arXiv:2505.02881.
🤗 Sister Dataset: Discover SwallowMath-v2, our companion dataset for mathematical reasoning.
💻 What is it?
SwallowCode-v1 was a high-quality Python code dataset generated through an LLM-based rewriting pipeline.
However, it had two significant limitations:
(1) it was distributed under the Llama 3.3 Community License, and
(2) its size was limited to… See the full description on the dataset page: https://huggingface.co/datasets/tokyotech-llm/swallow-code-v2.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.LeetCodeDataset
LeetCodeDataset
LeetCodeDataset is a dataset consists of Python leetcode problems that can be used for LLM training and evaluation.
💻 GitHub
📄 LeetCodeDataset: A Temporal Dataset for Robust Evaluation and Efficient Training of Code LLMs
📄 Policy Filtration for RLHF to Mitigate Noise in Reward Models
rlvr-reward-hacking-scale-no-conftest-20260909-completion
Matched no-conftest RLVR study 20260909-completion
Lossless research records, grouped by model and trajectory type. Only the listed
configurations have published records. Canary diagnostics are excluded from study
estimates; run status in provenance distinguishes retired diagnostics from active
or completed training. Valid failures, refusals and truncations are retained.
The train split name is a dataset-loader convention; record_type identifies
whether a record is training… See the full description on the dataset page: https://huggingface.co/datasets/lucabaroni/rlvr-reward-hacking-scale-no-conftest-20260909-completion.LLaMmlein-DatasetThis dataset is a strict subset of the RedPajama V2 dataset and therefore retains all licenses from RedPajama V2.
More details in our preprint!
Data Take Down
Erotic_Literature_CollectionEnglish
中文色情文学数据集合集
概述
本仓库包含了51个中文色情文学数据集。每个数据集由短篇色情小说、个人色情经验及其他形式的色情内容组成。数据集的格式为JSON，每个文件包含一个对象数组，每个对象代表一篇文档：
[
{"text": "document"},
{"text": "document"}
]
这些数据集可用于语言模型的预训练，经过适当调整后也可用于模型的微调。
数据集格式
文件格式： JSON
内容： 短篇色情小说、个人色情经验及其他色情内容
结构：
每个文件包含一个对象数组
每个对象包含一个键 "text"，其值为相应的文档内容
使用方法
这些数据集主要用于研究目的，特别是在语言模型的开发和微调中使用。由于内容的敏感性，用户应谨慎处理这些数据集，并确保遵守当地的法律法规及相关指导原则。
示例用法
import json
# 加载数据集with open('path_to_json_file.json', 'r'… See the full description on the dataset page: https://huggingface.co/datasets/ystemsrx/Erotic_Literature_Collection.Lego-RL-2699
SWE-Lego-RL-2699
2,699 executable, difficulty-filtered SWE tasks for agentic RL, shipped in two
parallel views of the same instances:
View
Path
What it is
Official OpenSWE records
openswe_official_2699/
The original upstream GAIR/OpenSWE rows for exactly these 2,699 instances
Harbor RL environments
openswe_harbor_2699/
The same instances converted into ready-to-run task directories (+ the training index)
Both views cover the identical 2,699 instance_ids. The… See the full description on the dataset page: https://huggingface.co/datasets/Lego-X/Lego-RL-2699.Scaffold-CoT
Scaffold-CoT
Structured chain-of-thought training data with 3,726,548 examples in 76 JSONL shards.
Fields
Every row has exactly four top-level fields:
Field
Contents
metadata
domain, subdomain, difficulty, length_bucket
input
Ordered user messages as {index, content} objects
cot
Ordered {index, type, content} events, including reasoning, tool calls, and tool results
output
Ordered final assistant answers as {index, content} objects
The index… See the full description on the dataset page: https://huggingface.co/datasets/Specific-Labs/Scaffold-CoT.lingshu_training_data_medical_domain
Website
🤖 7B Model
🤖 8B Model based on InternVL3
🤖 32B Model
MedEvalKit
Technical Report
Lingshu MCP
Lingshu Medical MLLM Training Data (Medical Domain)
This dataset contains the medical-domain training data used in the multi-stage training of the Lingshu Medical Multimodal Large Language Model (MLLM). General-domain data has been removed; only medical data is included.
The training… See the full description on the dataset page: https://huggingface.co/datasets/lingshu-medical-mllm/lingshu_training_data_medical_domain.DR3-EvalDR3-Eval: Towards Realistic and ReproducibleDeep Research Evaluation
✨ Overview
DR³-Eval is a realistic, reproducible, and multimodal evaluation benchmark for Deep Research Agents, focusing on multi-file report generation tasks.
Existing benchmarks face a fundamental tension between realism, controllability, and reproducibility when evaluating deep research agents. DR³-Eval addresses this through the following design:… See the full description on the dataset page: https://huggingface.co/datasets/NJU-LINK/DR3-Eval.guidelines
🎉 NEW DROP 🎉 PubMed Guidelines
We just added 1627 clinical guidelines found in PubMed and PubMed Central to the dataset on December 23rd, 2023. Merry Christmas!
Clinical Guidelines
The Clinical Guidelines corpus is a new dataset of 47K clinical practice guidelines from 17 high-quality online medical sources. This dataset serves as a crucial component of the original training corpus of the Meditron Large Language Model (LLM). We publicly release a subset of 37K articles… See the full description on the dataset page: https://huggingface.co/datasets/epfl-llm/guidelines.Data-Prep-Bench
Data-Prep-Bench
This repository contains the data presented in DataPrep-Bench: Benchmarking LLMs as Training Data Preparators.
Code: https://github.com/OpenDCAI/Data-Preparation-Bench
Dataset Overview
This dataset is a comprehensive resource built for Supervised Fine-Tuning (SFT) and evaluation of Large Language Models (LLMs), covering six domains: Finance, Medicine, Law, Mathematics, Science, and General.
A key feature of this dataset is that we employed 12… See the full description on the dataset page: https://huggingface.co/datasets/lhpku20010120/Data-Prep-Bench.physical-ai-bench-generation
Physical AI Bench - Generation
Paper | Code
Dataset Description
The PAI-Bench is a benchmark to measure the progress of world models quantitatively.
The predict task contains a list of 1044 samples of text prompts, conditioning images, and qa pairs, covering Physical AI target domains including autonomous vehicle (AV) driving, robotics, industry (smart space), physics, human, and common sense. All the questions are binary questions, and the answer is either Yes or No. Our… See the full description on the dataset page: https://huggingface.co/datasets/shi-labs/physical-ai-bench-generation.Java-Code-Large-text-onlyJava-Code-Large
Java-Code-Large is a large-scale corpus of publicly available Java source code comprising more than 15 million java codes. The dataset is designed to support research in large language model (LLM) pretraining, code intelligence, software engineering automation, and program analysis.
By providing a high-volume, language-specific corpus, Java-Code-Large enables systematic experimentation in Java-focused model training, domain adaptation, and downstream code understanding tasks.… See the full description on the dataset page: https://huggingface.co/datasets/XEUIPR/Java-Code-Large-text-only.JavaScript-Code-LargeJavaScript-Code-Large
JavaScript-Code-Large is a large-scale corpus of JavaScript source code comprising around 5 million JavaScript files. The dataset is designed to support research in large language model (LLM) pretraining, code intelligence, software engineering automation, and program analysis for the JavaScript ecosystem.
By providing a high-volume, language-specific corpus, JavaScript-Code-Large enables systematic experimentation in JavaScript-focused model training, domain adaptation… See the full description on the dataset page: https://huggingface.co/datasets/ajibawa-2023/JavaScript-Code-Large.LongDS
LongDS
Overview •
Versions •
Download •
Usage •
Task Index •
Citation
Paper •
Code •
Web
LongDS-Bench is a benchmark for evaluating long-horizon, multi-turn agentic data analysis. Real-world analysis is rarely a sequence of independent questions: filters, metric definitions, assumptions, intermediate tables, and branch-specific results evolve over many turns. LongDS tests whether agents can maintain and apply these evolving analytical states correctly.… See the full description on the dataset page: https://huggingface.co/datasets/zjunlp/LongDS.Light-R1-SFTData
Light-R1: Surpassing R1-Distill from Scratch* with $1000 through Curriculum SFT & DPO
*from models without long COT
technical report
GitHub page
Here are the two-stage SFT data we used to train Light-R1-32B.
Simply refer to stage1-76k.json and stage2-3k.json
Model
Trained From
Release Date
AIME24
AIME25
DeepSeek-R1-Distill-Llama-70B
Llama-3.3-70B-Instruct
25.1.20
70.0
54.1
DeepSeek-R1-Distill-Qwen-32B
Qwen2.5-32B
25.1.20
72.6
54.9
LIMO (32B)
Qwen2.5-32B-Instruct
25.2.4… See the full description on the dataset page: https://huggingface.co/datasets/qihoo360/Light-R1-SFTData.alpaca-gpt4-data-zh
Dataset Card for "alpaca-gpt4-data-zh"
All of the work is done by this team.
Usage and License Notices
The data is intended and licensed for research use only. The dataset is CC BY NC 4.0 (allowing only non-commercial use) and models trained using the dataset should not be used outside of research purposes.
English Dataset
Found here
Citation
@article{peng2023gpt4llm,
title={Instruction Tuning with GPT-4},
author={Baolin Peng, Chunyuan Li… See the full description on the dataset page: https://huggingface.co/datasets/llm-wizard/alpaca-gpt4-data-zh.HarmfulQAPaper | Github | Dataset| Model
📣📣📣: Do check our new multilingual dataset CatQA here used in Safety Vectors:📣📣📣
As a part of our research efforts toward making LLMs more safe for public use, we create HarmfulQA i.e. a ChatGPT-distilled dataset constructed using the Chain of Utterances (CoU) prompt. More details are in our paper Red-Teaming Large Language Models using Chain of Utterances for Safety-Alignment
HarmfulQA serves as both-a new LLM safety benchmark and an alignment dataset… See the full description on the dataset page: https://huggingface.co/datasets/declare-lab/HarmfulQA.