datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
prompts.chat
a.k.a. Awesome ChatGPT Prompts
This is a Dataset Repository mirror of prompts.chat — a social platform for AI prompts.
📢 Notice
This Hugging Face dataset is a mirror. For the latest prompts, features, and community contributions, please visit:
🌐 Website: prompts.chat
📦 GitHub: github.com/f/awesome-chatgpt-prompts
About
prompts.chat is an open-source platform where users can share, discover, and collect AI prompts from the community. The project can… See the full description on the dataset page: https://huggingface.co/datasets/fka/prompts.chat.voice-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.ChatGPT-4o-Writing-Prompts
ChatGPT-4o Writing Prompts
This is a dataset containing 3746 short stories, generated with OpenAI's chatgpt-4o-latest model and using Reddit's Writing Prompts subreddit as a source. Each sample is generally between 6000-8000 characters long.
These stories were thoroughly cleaned and then further enriched with a title and a series of applicable genres.
Note that I did not touch the Markdown ChatGPT-4o produced by itself to enrich its output, as I very much enjoy the added flavour… See the full description on the dataset page: https://huggingface.co/datasets/Gryphe/ChatGPT-4o-Writing-Prompts.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.ChatGPT-Jailbreak-Prompts
Dataset Card for Dataset Name
Name
ChatGPT Jailbreak Prompts
Dataset Summary
ChatGPT Jailbreak Prompts is a complete collection of jailbreak related prompts for ChatGPT. This dataset is intended to provide a valuable resource for understanding and generating text in the context of jailbreaking in ChatGPT.
Languages
[English]
10k_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.White-Hat-Security-Agent-Prompts-600K
White Hat Security Agent Prompts 600K
Overview
The White-Hat-Security-Agent-Prompts-600K dataset is a practitioner-perspective security prompts corpus of 596,295 richly contextualized queries, designed to represent how real-world defensive security professionals communicate, interrogate, and reason through active threat scenarios.
Where most security datasets catalogue CVEs, malware signatures, or CTF write-ups, this collection teaches models to operate from inside the… See the full description on the dataset page: https://huggingface.co/datasets/yatin-superintelligence/White-Hat-Security-Agent-Prompts-600K.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.transcripts
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.
5,690 episodes · 513,097 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.chatbot_instruction_prompts
Dataset Card for Chatbot Instruction Prompts Datasets
Dataset Summary
This dataset has been generated from the following ones:
tatsu-lab/alpaca
Dahoas/instruct-human-assistant-prompt
allenai/prosocial-dialog
The datasets has been cleaned up of spurious entries and artifacts. It contains ~500k of prompt and expected resposne. This DB is intended to train an instruct-type model
episodes
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.
5,716 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.Safety-Prompts
Dataset Card for Dataset Name
GitHub Repository: https://github.com/thu-coai/Safety-Prompts
Paper: https://arxiv.org/abs/2304.10436
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-qwen3-4b-n16-flattened-logprobs-k16
OpenThoughts-4 Code SDG: Qwen3-4B (n=16, top-16 logprobs)
Synthetic generations from
Qwen/Qwen3-4B
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
Qwen/Qwen3-4B… See the full description on the dataset page: https://huggingface.co/datasets/marin-community/openthoughts4-code-9168-prompts-qwen3-4b-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.openthoughts4-science-26041-prompts-qwen3-32b-n8-flattened-logprobs-k16
OpenThoughts-4 Science SDG: Qwen3-32B (n=8, top-16 logprobs)
Synthetic generations from
Qwen/Qwen3-32B
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
Value
Generator model… See the full description on the dataset page: https://huggingface.co/datasets/marin-community/openthoughts4-science-26041-prompts-qwen3-32b-n8-flattened-logprobs-k16.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.TC260-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.GPT-4-PromptsMulti-Turn Conversational Prompts from ChatGPT-4 (10K+ Tokens)
Abstract:
This dataset offers a valuable collection of multi-turn conversational prompts generated by ChatGPT-4, carefully curated for diverse prompt styles (chatml, gemma, llama). Each prompt exceeds 10,000 tokens, providing ample context and inspiration for training and evaluating large language models. Ideal for researchers and developers interested in exploring advanced conversational AI capabilities.
Table of Contents:… See the full description on the dataset page: https://huggingface.co/datasets/erfanzar/GPT-4-Prompts.TC260-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/MuYi23/TC260-Chinese-Safety-Prompts.red_team_repo_social_bias_prompts
Dataset Card for A Red-Teaming Repository of Existing Social Bias Prompts
Summary
This dataset contains aggregated and unified existing red-teaming prompts designed to identify
stereotypes, discrimination, hate speech, and other representation harms in text-based Large Language Models (LLMs)
Project Summary Page: For more information about my 2024 AI Safety Capstone project
Dataset Information: For more information about the datasets used to create this repository.… See the full description on the dataset page: https://huggingface.co/datasets/svannie678/red_team_repo_social_bias_prompts.openthoughts4-science-26041-prompts-qwen3-4b-n8-flattened-logprobs-k16
OpenThoughts-4 Science SDG: Qwen3-4B (n=8, top-16 logprobs)
Synthetic generations from
Qwen/Qwen3-4B
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
Value
Generator model… See the full description on the dataset page: https://huggingface.co/datasets/marin-community/openthoughts4-science-26041-prompts-qwen3-4b-n8-flattened-logprobs-k16.stable-diffusion-prompts-2.47M
Source
Combined text-only dataset from
poloclub/diffusiondb
Gustavosta/Stable-Diffusion-Prompts
bartman081523/stable-diffusion-discord-prompts
FredZhang7/krea-ai-prompts
For preprocessing methods, please see Fast GPT2 PromptGen.
Python
Download and save the dataset to all_prompts.txt locally.
pip install datasets
import datasets
dataset = datasets.load_dataset("FredZhang7/stable-diffusion-prompts-2.47M")
train = dataset["train"]
prompts = train["text"]
with… See the full description on the dataset page: https://huggingface.co/datasets/FredZhang7/stable-diffusion-prompts-2.47M.openthoughts4-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.turkish-flow-drafter-prompts
GitHub repo ·
Technical blog ·
Model collection
Turkish prompts for Chained-Flow drafter training
Chat-templated Turkish prompts used to train and evaluate the Turkish
Flow-Drafter
checkpoints for Qwen/Qwen3.5-4B / 9B / 27B.
Prompts only — no completions. A drafter is trained on the target model's own hidden states, so
continuations are generated locally by running the target over these prompts. Nothing here is a
model output.
split
rows
what it is
v1/… See the full description on the dataset page: https://huggingface.co/datasets/ytu-ce-cosmos/turkish-flow-drafter-prompts.llama2-high-entropy-prompts
High-entropy prompts for suffix-based backdoor detection
Prompts on which base meta-llama/Llama-2-7b-hf has high predictive
entropy, built to give a suffix-optimization backdoor detector measurable
headroom: a clean model should stay uncertain on these prompts, while a poisoned
model driven by a trigger-like suffix should collapse to low entropy. Prompts
where the base model is already confident cannot separate the two.
How the prompts were made
Short prefixes… See the full description on the dataset page: https://huggingface.co/datasets/Alookhoshk/llama2-high-entropy-prompts.prompts_under_512_tokens
Under 512 Tokens Prompts Dataset
Created by Aipresso LIMITED, London, UK
⚠️ IMPORTANT: By using this dataset, you agree to our Terms of Use
Dataset Overview
Specialized collection of short-form English prompts (under 512 tokens), perfect for training models with context length constraints or faster iteration cycles.
📊 Dataset Statistics
Metric
Value
Total Files
200
Rows Per File
10,000
Total Rows
2,000,000
Token Range
1 to 511 tokens… See the full description on the dataset page: https://huggingface.co/datasets/Aipresso/prompts_under_512_tokens.prompt-slimmer-slm
Prompt Slimmer SLM — Demo Dataset
Synthetic examples for experimenting with prompt rewriting and sentence selection. Exported without changing the examples or their original splits from the shared GitHub codebase.
Model · Project page
Configuration
Train
Validation
Test
Purpose
rewrites-expanded (default)
41
2
2
Expanded rewriting dataset: 45 examples
rewrites
9
2
2
Original dataset used by the first adapter
selector
256
64
64
KEEP/DROP labels for source spans… See the full description on the dataset page: https://huggingface.co/datasets/ai-mitra/prompt-slimmer-slm.10k_rows_cleaned_prompts
10K Rows Cleaned Prompts Dataset
Created by Aipresso LIMITED, London, UK
⚠️ IMPORTANT: By using this dataset, you agree to our Terms of Use
You must provide attribution when using this data in publications, research, or commercial products.
Dataset Overview
A chunked collection of 2.7 million cleaned English prompts, organized into 200 files of 10,000 rows each for easy processing and distributed training of language models.
📊 Dataset Statistics
Metric… See the full description on the dataset page: https://huggingface.co/datasets/Aipresso/10k_rows_cleaned_prompts.synthetic_multilingual_llm_prompts
Image generated by DALL-E. See prompt for more details
📝🌐 Synthetic Multilingual LLM Prompts
Welcome to the "Synthetic Multilingual LLM Prompts" dataset! This comprehensive collection features 1,250 synthetic LLM prompts generated using Gretel Navigator, available in seven different languages. To ensure accuracy and diversity in prompts, and translation quality and consistency across the different languages, we employed Gretel Navigator both as a generation tool and as an… See the full description on the dataset page: https://huggingface.co/datasets/gretelai/synthetic_multilingual_llm_prompts.