datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
ChatGPT-Jailbreak-PromptsPromptEval_MMLU_full
MMLU Multi-Prompt Evaluation Data
Overview
This dataset contains the results of a comprehensive evaluation of various Large Language Models (LLMs) using multiple prompt templates on the Massive Multitask Language Understanding (MMLU) benchmark. The data is introduced in
Maia Polo, Felipe, Ronald Xu, Lucas Weber, Mírian Silva, Onkar Bhardwaj, Leshem Choshen, Allysson Flavio Melo de Oliveira, Yuekai Sun, and Mikhail Yurochkin. "Efficient multi-prompt evaluation of LLMs."… See the full description on the dataset page: https://huggingface.co/datasets/PromptEval/PromptEval_MMLU_full.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.PromptEval_MMLU_correctness
MMLU Multi-Prompt Evaluation Data (correctness scores)
Overview
This dataset contains the results of a comprehensive evaluation of various Large Language Models (LLMs) using multiple prompt templates on the Massive Multitask Language Understanding (MMLU) benchmark. The data is introduced in
Maia Polo, Felipe, Ronald Xu, Lucas Weber, Mírian Silva, Onkar Bhardwaj, Leshem Choshen, Allysson Flavio Melo de Oliveira, Yuekai Sun, and Mikhail Yurochkin. "Efficient multi-prompt… See the full description on the dataset page: https://huggingface.co/datasets/PromptEval/PromptEval_MMLU_correctness.AIME_Problem_Set_1983-202410k_prompts_ranked
Dataset Card for 10k_prompts_ranked
10k_prompts_ranked is a dataset of prompts with quality rankings created by 314 members of the open-source ML community using Argilla, an open-source tool to label data. The prompts in this dataset include both synthetic and human-generated prompts sourced from a variety of heavily used datasets that include prompts.
The dataset contains 10,331 examples and can be used for training and evaluating language models on prompt ranking tasks. The… See the full description on the dataset page: https://huggingface.co/datasets/data-is-better-together/10k_prompts_ranked.MMLU_multi_promptwinograd_wsc_wsc273_promptsourcevoice-acting-cutscene-prompts
Cut-Scene Voice-Acting Prompts
Continuously-generated, character-consistent two-scene "CUT TO:" voice-performance
prompts (text only, no audio) for training and evaluating expressive TTS / voice-acting
models. Each prompt describes a single speaker across two sharply contrasting emotional
moments separated by a CUT TO: transition, in a voice-acting stage-direction format
(spoken lines in "quotes", performance notes in (parentheses)).
Total prompts: 4,057,000
Languages: English… See the full description on the dataset page: https://huggingface.co/datasets/laion/voice-acting-cutscene-prompts.stack-prompts
The stack-prompts
This dataset is a curated collection of high-quality educational and synthetic data designed for training (small) language models in coding tasks.
The current dataset comprises three config names:
python-edu: comprises the blob_ids from https://huggingface.co/datasets/HuggingFaceTB/smollm-corpus
the-stack: comprises the blob_ids (for powershell only) from https://huggingface.co/datasets/bigcode/the-stack-v2.
stack-edu: comprises the blob_ids from… See the full description on the dataset page: https://huggingface.co/datasets/Fhrozen/stack-prompts.chroma_promptsA collection of prompts captioned using Gemma 2b captioning model. These prompts are intended to be used with FLUX Chroma model.
Download .parquet files to your Google Drive and
run them using the .ipynb notebook in this repo
MMLU_multi_prompt_v0transcripts
My Weird Prompts — Transcript Corpus
Every published transcript from the My Weird Prompts
podcast, shaped for textual analysis: narrowed metadata, the full transcript, the
same transcript segmented into speaker turns, and per-episode text statistics.
5,367 episodes · 469,836 speaker turns. Rebuilt daily from the
production database.
Configs
from datasets import load_dataset
episodes = load_dataset("My-Weird-Prompts/transcripts", "episodes", split="train") # one… See the full description on the dataset page: https://huggingface.co/datasets/My-Weird-Prompts/transcripts.llm-jailbreak-prompt-injection-dataset
LLM Jailbreak & Prompt-Injection Dataset
A unified safety dataset combining 30+ public sources for training LLM
guardrails, content moderation classifiers, and response-safety filters.
Schema (orthogonal multi-label, WildGuard-style)
Instead of a single binary is_dangerous, every example carries four
orthogonal labels matching the structure used by AI2 WildGuard, IBM Granite
Guardian, and Azure Prompt Shields:
Column
Type
Description
prompt
str
The user/attack… See the full description on the dataset page: https://huggingface.co/datasets/Necent/llm-jailbreak-prompt-injection-dataset.winograd_wsc_wsc285_promptsourcerh_qwen3_8b_prompted_v2_completions
TRL Completion logs
This dataset contains the completions generated during training using trl.
The completions are stored in parquet files, and each file contains the completions for a single step of training (depending on the logging_steps argument).
Each file contains the following columns:
step: the step of training
prompt: the prompt used to generate the completion
completion: the completion generated by the model
<reward_function_name>: the reward(s) assigned to the… See the full description on the dataset page: https://huggingface.co/datasets/sunshineNew/rh_qwen3_8b_prompted_v2_completions.stable-diffusion-prompts-stats-full-uncensoredepisodes
My Weird Prompts - Episode Dataset
The production record of every episode of the
My Weird Prompts podcast: the transcript, links to
the published episode, a description of the prompt that started it, and the
generation telemetry for how it was made - model, pipeline version, GPU, timings
and compute cost.
5,393 episodes. Synced daily from the production database.
from datasets import load_dataset
ds = load_dataset("My-Weird-Prompts/episodes", split="train")
Which… See the full description on the dataset page: https://huggingface.co/datasets/My-Weird-Prompts/episodes.midjourney-prompts-embeddings
Midjourney Prompt–Embedding Dataset
This dataset is derived from our COLM 2024 paper, Iteratively Prompting Multimodal LLMs to Reproduce Natural and AI-Generated Images. The paper studies whether multimodal language models can infer prompts that generate images visually similar to target images produced by text-to-image systems or found in stock image collections, highlighting the relationship between real-world prompts and generated images as well as broader economic and security… See the full description on the dataset page: https://huggingface.co/datasets/AliN96/midjourney-prompts-embeddings.openthoughts4-code-9168-prompts-qwen3-30b-a3b-thinking-2507-n16-flattened-logprobs-k16
OpenThoughts-4 Code SDG: Qwen3-30B-A3B-Thinking-2507 (n=16, top-16 logprobs)
Synthetic generations from
Qwen/Qwen3-30B-A3B-Thinking-2507
on the Marin OpenThoughts-4 code SDG prompt
set.
Each prompt is sampled n=16 times, and for every generated token the dataset
stores the chosen-token log probability plus the top-16 log probabilities
over the vocabulary, enabling distillation, KL-style fine-tuning,
reranking, and uncertainty analysis.
Generation setup
Field… See the full description on the dataset page: https://huggingface.co/datasets/marin-community/openthoughts4-code-9168-prompts-qwen3-30b-a3b-thinking-2507-n16-flattened-logprobs-k16.openthoughts4-code-9168-prompts-qwen3-32b-n16-flattened-logprobs-k16
OpenThoughts-4 Code SDG: Qwen3-32B (n=16, top-16 logprobs)
Synthetic generations from
Qwen/Qwen3-32B
on the Marin OpenThoughts-4 code SDG prompt
set.
Each prompt is sampled n=16 times, and for every generated token the dataset
stores the chosen-token log probability plus the top-16 log probabilities
over the vocabulary, enabling distillation, KL-style fine-tuning,
reranking, and uncertainty analysis.
Generation setup
Field
Value
Generator model… See the full description on the dataset page: https://huggingface.co/datasets/marin-community/openthoughts4-code-9168-prompts-qwen3-32b-n16-flattened-logprobs-k16.highlevel_thinking_with_grounding_annotation_split1000_v3_merged_promptsopenthoughts3-unfinished-promptsTC260-Chinese-Safety-Prompts
TC260 Chinese Safety Prompts V1
Public research dataset containing synthetic Chinese safety-testing prompts.
Records have different quality tiers; the full dataset must not be described
as human-verified or Gold data.
这是一个面向中文生成式人工智能安全评测研究的合成测试提示数据集。候选数据
由项目冻结的 tc260-generator-v3.2 生成，并经过结构校验、凭据与内部路径
扫描、精确去重和四字shingle近似去重。
本数据集不是TC260或任何国家标准机构发布、认可或认证的官方数据集。
类别名称和映射用于研究性实现，不构成法律、监管或合规结论。
数据规模
原始生成规模：5,000条候选；结构清洗后正式发布4,997条（剔除2条标记泄漏和1条重复记录）。
A.1至A.4：4… See the full description on the dataset page: https://huggingface.co/datasets/BBBBBBBBBBBQ/TC260-Chinese-Safety-Prompts.OpenThoughts3-456k-no-cot-with-olmo-system-promptsuper_glue_wsc.fixed_promptsourceopenthoughts4-code-9168-prompts-glm-5.2-n4
OpenThoughts-4 Code — GLM-5.2 n=4
Quality-filtered synthetic responses from
zai-org/GLM-5.2-FP8 for the
9,168 unique instruction_seed values in
mlfoundations-dev/hero_run_4_code.
Each prompt has four accepted responses, for 36,672 rows total.
Generation
Field
Value
Generator
zai-org/GLM-5.2-FP8
Samples per prompt
4
Temperature
1.0
Top-p
0.95
Maximum generated tokens
256,000
Thinking mode
enabled
Inference engine
vLLM on 8 GB200 GPUs… See the full description on the dataset page: https://huggingface.co/datasets/marin-community/openthoughts4-code-9168-prompts-glm-5.2-n4.openthoughts4-science-26041-prompts-qwen3-30b-a3B-thinking-2507-n8-flattened-logprobs-k16
OpenThoughts-4 Science SDG: Qwen3-30B-A3B-Thinking-2507 (n=8, top-16 logprobs)
Synthetic generations from
Qwen/Qwen3-30B-A3B-Thinking-2507
on the Marin OpenThoughts-4 science SDG prompt
set.
Each prompt is sampled n=8 times, and for every generated token the dataset
stores the chosen-token log probability plus the top-16 log probabilities
over the vocabulary, enabling distillation, KL-style fine-tuning,
reranking, and uncertainty analysis.
Generation setup
Field… See the full description on the dataset page: https://huggingface.co/datasets/marin-community/openthoughts4-science-26041-prompts-qwen3-30b-a3B-thinking-2507-n8-flattened-logprobs-k16.prompt-voice-v1.5
Dataset Overview
This dataset contains nearly 2.35M English speech instruction to text answer samples, using the combination of:
Intel/orca_dpo_pairs
routellm/gpt4_dataset
nomic-ai/gpt4all-j-prompt-generations
microsoft/orca-math-word-problems-200k
allenai/WildChat-1M
Open-Orca/oo-gpt4-200k
Magpie-Align/Magpie-Pro-300K-Filtered
qiaojin/PubMedQA
Undi95/Capybara-ShareGPT
HannahRoseKirk/prism-alignment
BAAI/Infinity-Instruct
Usage
from datasets import load_dataset… See the full description on the dataset page: https://huggingface.co/datasets/Menlo/prompt-voice-v1.5.t2i-prompts-3mDataset used in the paper:
pi-Flow: Policy-Based Few-Step Generation via Imitation Distillation
Hansheng Chen1,
Kai Zhang2,
Hao Tan2,
Leonidas Guibas1,
Gordon Wetzstein1,
Sai Bi2
1Stanford University, 2Adobe Research
[arXiv] [Code] [pi-Qwen Demo🤗] [pi-FLUX Demo🤗]