datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
agent-diff-bench
Agent-Diff Bench
Website | Paper | GitHub
Agent-Diff is a benchmarking framework for evaluating agentic Large Language Models (LLMs) on real-world tasks that execute code via external APIs. The benchmark provides access to real API interfaces (Slack, Box, Linear, Google Calendar) while sandboxing the environment in which calls are made and evaluated.
Dataset Summary
The dataset contains 224 tasks utilizing enterprise software workflows, provided with an 80/20… See the full description on the dataset page: https://huggingface.co/datasets/hubertmarek/agent-diff-bench.AM-Math-Difficulty-RLFor more open-source datasets, models, and methodologies, please visit our GitHub repository.
We believe that the selection of training data for reinforcement learning is crucial.
To validate this, we conducted several experiments exploring how data difficulty influences training performance.
Our data sources originate from numerous excellent open-source projects, and we sincerely appreciate their contributions, without which our current achievements would not have been possible.… See the full description on the dataset page: https://huggingface.co/datasets/a-m-team/AM-Math-Difficulty-RL.skill-diffs
skill-diffs
Commit-by-commit revision history of agent skills (SKILL.md files) scraped from public GitHub repos. Each record is a (before, after, intent) tuple capturing how a skill was iteratively refined through human feedback.
v0.5 covers 4 platforms — Anthropic Claude, OpenClaw, OpenCode, and Hermes Agent — with PR title/body metadata as richer intent labels, MinHash + semantic clustering for dedup, structural diff_summary for filtering by edit type, aggregate quality_score for… See the full description on the dataset page: https://huggingface.co/datasets/shl0ms/skill-diffs.diff-xyz
Diff-XYZ
This is a dataset for the paper: Diff-XYZ: A Benchmark for Evaluating Diff Understanding.
Diff-XYZ contains 1,000 real-world code edits sampled and filtered from
the CommitPackFT dataset.Each example provides three components: the original file contents (old_code), the modified contents (new_code), and
multiple diff representations (udiff, udiff-h, udiff-l, and search-replace).
These formats enable evaluation of LLM capabilities on three code editing tasks:
Apply: Given… See the full description on the dataset page: https://huggingface.co/datasets/JetBrains-Research/diff-xyz.task518_emo_different_dialogue_emotions
Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task518_emo_different_dialogue_emotions
Additional Information
Citation Information
The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it:
@misc{wang2022supernaturalinstructionsgeneralizationdeclarativeinstructions,
title={Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP… See the full description on the dataset page: https://huggingface.co/datasets/Lots-of-LoRAs/task518_emo_different_dialogue_emotions.MathVision_with_difficulty_level
MathVision with difficulty level tags
This dataset extends the 🤗 MathVision benchmark by introducing two additional tags: passrate_for_qwen2.5_vl_7b and difficulty_level_for_qwen2.5_vl_7b. Further details are available in our paper The Synergy Dilemma of Long-CoT SFT and RL: Investigating Post-Training Techniques for Reasoning VLMs.
🚀 Data Usage
from datasets import load_dataset
dataset = load_dataset("JierunChen/MathVision_with_difficulty_level")
print(dataset)… See the full description on the dataset page: https://huggingface.co/datasets/JierunChen/MathVision_with_difficulty_level.linux-kernel-bugfixes-diffs
🐧 Linux Kernel Bugfixes & Patches Dataset (Instruction-Tuned)
📖 Dataset Description
This dataset is a highly curated, instruction-tuned collection of problem-solution pairs extracted directly from the official Linux Kernel Git repository (torvalds/linux). It is specifically designed to train Large Language Models (LLMs) on low-level C programming, kernel architecture, memory management, and security vulnerability patching.
Unlike raw commit histories, this… See the full description on the dataset page: https://huggingface.co/datasets/switlydev/linux-kernel-bugfixes-diffs.2026-08-07-surf-synthdoc-difficult-advice-attributes-full
SURF Attributes (Full)
Complete dataset for SURF research and extension.
Paper: Chunky Post-Training (link pending)
Quick Start
For running SURF, use the minimal dataset: LASR-Callum/2026-08-07-surf-synthdoc-difficult-advice-attributes
uv run -m surf.cli.main sweep \
--attributes LASR-Callum/2026-08-07-surf-synthdoc-difficult-advice-attributes \
--rubric rubrics/rebuttal.yaml \
-o results/
Dataset Fields
prompt: The query text… See the full description on the dataset page: https://huggingface.co/datasets/dougalldeepmind/2026-08-07-surf-synthdoc-difficult-advice-attributes-full.2026-08-07-surf-synthdoc-difficult-advice-attributes
SURF Attributes
Minimal dataset for running SURF (Surfacing Unintended Response Failures).
Paper: Chunky Post-Training (link pending)
Usage
uv run -m surf.cli.main sweep \
--attributes LASR-Callum/2026-08-07-surf-synthdoc-difficult-advice-attributes \
--rubric rubrics/rebuttal.yaml \
-o results/
Fields
prompt: The query text
sae_attributes: List of semantic attribute cluster summaries
How it works
Each prompt was… See the full description on the dataset page: https://huggingface.co/datasets/dougalldeepmind/2026-08-07-surf-synthdoc-difficult-advice-attributes.difficulty-aime_2025-generations
Generations Dataset: aime_2025
Paper: LLMs Encode Their Failures: Predicting Success from Pre-Generation ActivationsCode: GitHub
LLM-generated solutions across train/validation/test splits for multiple models.
Columns
Column
Type
Description
problem
str
Problem statement
generated_solutions
list
Generated solutions with scores
success_rate
float
Fraction of correct generations
majority_vote_is_correct
int (0/1)
Whether majority vote is correct
k… See the full description on the dataset page: https://huggingface.co/datasets/CoffeeGitta/difficulty-aime_2025-generations.task628_xlwic_word_with_different_meaning_sentence_generation
Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task628_xlwic_word_with_different_meaning_sentence_generation
Additional Information
Citation Information
The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it:
@misc{wang2022supernaturalinstructionsgeneralizationdeclarativeinstructions,
title={Super-NaturalInstructions: Generalization via Declarative… See the full description on the dataset page: https://huggingface.co/datasets/Lots-of-LoRAs/task628_xlwic_word_with_different_meaning_sentence_generation.DiffutronLM-Pretraining-Corpus
DiffutronLM-Pretraining-Corpus
DiffutronLM-Pretraining-Corpus is the comprehensive, filtered Turkish text dataset used during the Continual Pre-training (CPT) phase of the Diffutron language models.
The primary goal of this dataset was to align the cross-lingual representations of a multilingual base encoder (jhu-clsp/mmBERT-base) with the agglutinative complexity and morphological nuances of the Turkish language, without inducing catastrophic forgetting.
📊 Dataset… See the full description on the dataset page: https://huggingface.co/datasets/diffutron/DiffutronLM-Pretraining-Corpus.diffusion-generated-text
Diffusion-Generated Text Benchmark
17,565 cleaned responses from three diffusion language model families and 21 generation settings
This benchmark supports research on diffusion-generated language, machine-generated text detection, and robustness across model families and decoding configurations. It includes outputs from DiffusionGemma, LLaDA-8B-Instruct, and LLaDA2-mini with varied generation lengths and block sizes.
Benchmark composition
Generator… See the full description on the dataset page: https://huggingface.co/datasets/paoche11/diffusion-generated-text.stable_diffusion_female_prompts
stable_diffusion_female_prompts
⚠ PRECAUTION : This dataset contains NSFW and SFW prompts. ⚠
Stable Diffusion female prompts and some informations about the generated images
Dataset Details
Dataset Description
Enhancing the dataset progress is going on and going to be updated frequently.
These Huggingface datasets are used for enhancing the dataset:
FredZhang7/anime-prompts-180K
FredZhang7/stable-diffusion-prompts-2.47M… See the full description on the dataset page: https://huggingface.co/datasets/WoWoWoWololo/stable_diffusion_female_prompts.japanese-math-empirical-difficulty-pilot-50k
Japanese Math Empirical Difficulty Pilot 50k
This dataset is a 50,000-problem empirical difficulty pilot, not a full empirical labeling of the original 5.66M-row source dataset.
It was created for LLM-jp experiment 0399, Team Victory SFT, to validate empirical difficulty label distribution, downstream split behavior, and the rollout/scoring pipeline before attempting labeling at the full 5.6M scale.
Current Status
This upload uses the v3 scorer with assistant-only… See the full description on the dataset page: https://huggingface.co/datasets/argo11/japanese-math-empirical-difficulty-pilot-50k.prompt_description_stable_diffusion_3k
The Synthetic Description from Prompts Dataset
This dataset is created using the Phi 2 3B Q4_K_S quantized model, using 3k random samples from training set of a base dataset of about 80,000 prompts from the Stable Diffusion dataset on Lexica.art. This dataset is designed to explore the capabilities of language models in generating creative and expanded descriptions from concise prompts.
Source Data… See the full description on the dataset page: https://huggingface.co/datasets/gokaygokay/prompt_description_stable_diffusion_3k.spelling-bee-human-difficulty
NYT Spelling Bee — Human Difficulty Dataset
Human solve-frequency data for 58 New York Times Spelling Bee puzzles (June–July 2025), sampled from 10,000 users per puzzle. Serves as ground truth for evaluating LLM orthographic constraint satisfaction.
Stat
Value
Puzzles
58
Date range
2025-06-02 to 2025-07-29
Total answer words
2,710
Words per puzzle
22–72 (mean 46.7)
Word length
4–13 characters
Users sampled per puzzle
10,000
Task
The NYT… See the full description on the dataset page: https://huggingface.co/datasets/redasers/spelling-bee-human-difficulty.Korean-DeepMath-with-Difficulty
Korean DeepMath with Difficulty
This dataset enriches ChuGyouk/Korean-DeepMath with difficulty and topic metadata from zwhe99/DeepMath-103K.
Join procedure
Rows are matched using Korean-DeepMath[extra_info][index] -> original DeepMath row index.
Added fields
original_index
difficulty
topic
Intended use
Prepared for controlled Korean mathematical reasoning SFT experiments, including difficulty-aware sampling such as TDCS.
No Easy /… See the full description on the dataset page: https://huggingface.co/datasets/Seungjun/Korean-DeepMath-with-Difficulty.Performance_Management_Difficult_Conversations_Practical
Performance Management Difficult Conversations — Practical
This corpus was automatically generated by the Deku Corpus Builder for use in RAG-based AI applications.
Dataset Structure
Each record contains:
text: The content text
source_url: Original source URL
source_title: Title of the source document
source_domain: Domain of the source
license_type: License classification (e.g. public_domain, cc_by, cc_by_sa)
attribution_required: Boolean — True for CC BY / CC BY-SA and… See the full description on the dataset page: https://huggingface.co/datasets/PhillyMac/Performance_Management_Difficult_Conversations_Practical.task125_conala_pair_differences
Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task125_conala_pair_differences
Additional Information
Citation Information
The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it:
@misc{wang2022supernaturalinstructionsgeneralizationdeclarativeinstructions,
title={Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks}… See the full description on the dataset page: https://huggingface.co/datasets/Lots-of-LoRAs/task125_conala_pair_differences.Performance_Management_Difficult_Conversations_Theory
Performance Management Difficult Conversations — Theory
This corpus was automatically generated by the Deku Corpus Builder for use in RAG-based AI applications.
Dataset Structure
Each record contains:
text: The content text
source_url: Original source URL
source_title: Title of the source document
source_domain: Domain of the source
license_type: License classification (e.g. public_domain, cc_by, cc_by_sa)
attribution_required: Boolean — True for CC BY / CC BY-SA and… See the full description on the dataset page: https://huggingface.co/datasets/PhillyMac/Performance_Management_Difficult_Conversations_Theory.adaption-minimal-diff-proofreading-12k
Minimal-Diff Proofreading
Proofread a sentence with the fewest possible edits (one to three tokens), explain the change, then give the corrected sentence.
Rows
12,000
Domain
writing and editing
Format
data.parquet, one row per example
Licence
apache-2.0
Built for
supervised fine-tuning (SFT) experiments on Adaption AutoScientist
Columns
Column
Description
original_prompt
The prompt (user turn) as uploaded.
original_completion… See the full description on the dataset page: https://huggingface.co/datasets/rodriguescarson/adaption-minimal-diff-proofreading-12k.Images-Diffusion-Prompt-Style
Image Diffusion Prompt Style
High-quality synthetic prompts for image diffusion models, optimized for Flux, Z Image, and Qwen.
Dataset Structure
Column
Type
Description
style_name
string
Short descriptive name
prompt_text
string
Full prompt with quality tokens
negative_prompt
string
Artifacts to avoid
tags
list
Lowercase keywords
compatible_models
list
Target models
Usage
from datasets import load_dataset
ds =… See the full description on the dataset page: https://huggingface.co/datasets/Pratofeitoo/Images-Diffusion-Prompt-Style.