Atomic Deep
Collection
To innovate a technological life descended from humanity, capable of evolving and thriving beyond the limits of biology. • 1 item • Updated
Datasets:
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38a76005681abd4a4f50a364d472016436f17e79778577ee5825580f06997202 | In a hole in the ground there lived a hobbit. | book_sentences | en | synthetic | train | synthetic-model-output | https://huggingface.co/Qwen/Qwen3.8-Flash-Next | atomicdeep_public_release_v1 | qwen3.8-flash-next | null | single_message | null | true | null | {
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573569deb78d8dda629d660f2c40ab14cc5484d7b31f37b40985c7ad034df113 | To be, or not to be, that is the question. | book_sentences | en | synthetic | train | synthetic-model-output | https://huggingface.co/Qwen/Qwen3.8-Flash-Next | atomicdeep_public_release_v1 | qwen3.8-flash-next | null | single_message | null | true | null | {
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4c6287fffa98367e96e3e1391526b9f4dc738475d06f2d70111d2a63bc30b172 | The only thing we have to fear is fear itself. | book_sentences | en | synthetic | train | synthetic-model-output | https://huggingface.co/Qwen/Qwen3.8-Flash-Next | atomicdeep_public_release_v1 | qwen3.8-flash-next | null | single_message | null | true | null | {
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4710486369f916157d032ce113de88a04f5b8b34fb892ad5cb362014565d0e39 | Never give up, never never give up. | book_sentences | en | synthetic | train | synthetic-model-output | https://huggingface.co/Qwen/Qwen3.8-Flash-Next | atomicdeep_public_release_v1 | qwen3.8-flash-next | null | single_message | null | true | null | {
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ef537f25c895bfa782526529a9b63d97aa631564d5d789c2b765448c8635fb6c | The quick brown fox jumps over the lazy dog. | book_sentences | en | synthetic | train | synthetic-model-output | https://huggingface.co/Qwen/Qwen3.8-Flash-Next | atomicdeep_public_release_v1 | qwen3.8-flash-next | null | single_message | null | true | null | {
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d43fa533abf358a1d80696ba04e285404a425d0e0f0b687f1bdba89aea225433 | This is just a flesh-and-blood-and-bone story. | book_sentences | en | synthetic | train | synthetic-model-output | https://huggingface.co/Qwen/Qwen3.8-Flash-Next | atomicdeep_public_release_v1 | qwen3.8-flash-next | null | single_message | null | true | null | {
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9ead5892fdab946a0b6cf94bee1126ae6582ce9a4c03ba32831d9a48ebcc37ec | Do not go gentle into that good night. | book_sentences | en | synthetic | train | synthetic-model-output | https://huggingface.co/Qwen/Qwen3.8-Flash-Next | atomicdeep_public_release_v1 | qwen3.8-flash-next | null | single_message | null | true | null | {
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3c902a99df7079e86e5b36cc539e852e85b5db731c905a57b74ee7ef67647ac9 | And the stars fell down. | book_sentences | en | synthetic | train | synthetic-model-output | https://huggingface.co/Qwen/Qwen3.8-Flash-Next | atomicdeep_public_release_v1 | qwen3.8-flash-next | null | single_message | null | true | null | {
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9aa8e28054862d8798b80214f26f47184035efc47532e8c0255560bafe7ab123 | The wind in the willows weeps. | book_sentences | en | synthetic | train | synthetic-model-output | https://huggingface.co/Qwen/Qwen3.8-Flash-Next | atomicdeep_public_release_v1 | qwen3.8-flash-next | null | single_message | null | true | null | {
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52616c4b601eee437b79a4ed2523e92b94a26abd8c47131fee1705ba3b6954ca | wait what?? | stream_chat | en | synthetic | train | synthetic-model-output | https://huggingface.co/Qwen/Qwen3.8-Flash-Next | atomicdeep_public_release_v1 | qwen3.8-flash-next | null | single_message | null | true | null | {
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130b7d0144c21b792c4ed0fb68da9ac62ae2b7f2bd1a9e0f6f300a4dcc2a53bc | haha nice | stream_chat | en | synthetic | train | synthetic-model-output | https://huggingface.co/Qwen/Qwen3.8-Flash-Next | atomicdeep_public_release_v1 | qwen3.8-flash-next | null | single_message | null | true | null | {
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b4a185cc1755adff4bf627f23fc0406076804934e9deb0642fc747a3c094f01a | nice one | stream_chat | en | synthetic | train | synthetic-model-output | https://huggingface.co/Qwen/Qwen3.8-Flash-Next | atomicdeep_public_release_v1 | qwen3.8-flash-next | null | single_message | null | true | null | {
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c34045c1a1db8d1b3fca8a692198466952daae07eaf6104b4c87ed3b55b6af1b | cool | stream_chat | en | synthetic | train | synthetic-model-output | https://huggingface.co/Qwen/Qwen3.8-Flash-Next | atomicdeep_public_release_v1 | qwen3.8-flash-next | null | single_message | null | true | null | {
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b6dc933311bc2357cc5fc636a4dbe41a01b7a33b583d043a7f870f3440697e27 | wow | stream_chat | en | synthetic | train | synthetic-model-output | https://huggingface.co/Qwen/Qwen3.8-Flash-Next | atomicdeep_public_release_v1 | qwen3.8-flash-next | null | single_message | null | true | null | {
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524da3416925d4dc7ab8428400cd7e3dff88271a3ee61f867eedecc1d4765199 | 674 + 67 = 741 | math | en | synthetic | train | synthetic-model-output | https://huggingface.co/Qwen/Qwen3.8-Flash-Next | atomicdeep_public_release_v1 | verified-arithmetic-generator | null | single_message | null | true | null | {
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70570810b1281c826196bd5b3e696bc0947136b2eaac0edb12784454e7776506 | 660 + 74 = 734 | math | en | synthetic | train | synthetic-model-output | https://huggingface.co/Qwen/Qwen3.8-Flash-Next | atomicdeep_public_release_v1 | verified-arithmetic-generator | null | single_message | null | true | null | {
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3413e2e0e1636ec92d4b8618aba5a4e02eefa26bef5e49ed41b363b5baa7b39e | 594 + 19 = 613 | math | en | synthetic | train | synthetic-model-output | https://huggingface.co/Qwen/Qwen3.8-Flash-Next | atomicdeep_public_release_v1 | verified-arithmetic-generator | null | single_message | null | true | null | {
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b73e57903ba9b4902d4bc25fe84ea62bc71cdc4ab56b7722cd5606a54a02b69f | 523 * 86 = 44978 | math | en | synthetic | train | synthetic-model-output | https://huggingface.co/Qwen/Qwen3.8-Flash-Next | atomicdeep_public_release_v1 | verified-arithmetic-generator | null | single_message | null | true | null | {
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25afd45c96743563cdd0b26bd587de5642cc3f0591460d4c9d38d1f0ea2b7a98 | 532 + 31 = 563 | math | en | synthetic | train | synthetic-model-output | https://huggingface.co/Qwen/Qwen3.8-Flash-Next | atomicdeep_public_release_v1 | verified-arithmetic-generator | null | single_message | null | true | null | {
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785a42438eccd823378d4f7cacf37a5fdc03cd595ef12e7e3b5bc343e6f2bdeb | Q: What is the county seat of King County, Washington? A: Seattle | trivia | en | extracted | train | CC-BY-SA-4.0 | https://opentdb.com/api_config.php | atomicdeep_public_release_v1 | https://opentdb.com/ | null | null | question_answer | null | true | null | {
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635d89be132b581684bfbd880e6e16e501da41c9675e53863b919776a3aa3b86 | Q: De Eemhof, Port Zelande and Het Heijderbos are holiday villas owned by what company? A: Center Parcs | trivia | en | extracted | train | CC-BY-SA-4.0 | https://opentdb.com/api_config.php | atomicdeep_public_release_v1 | https://opentdb.com/ | null | null | question_answer | null | true | null | {
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a791ca9ec13603656e019f14f01c1f34d8a3cef70b30b7d5b5ae19ce2c4f247f | Q: In a normal distribution, 95% of the data lies within how many standard deviations of the mean? A: 2 | trivia | en | extracted | train | CC-BY-SA-4.0 | https://opentdb.com/api_config.php | atomicdeep_public_release_v1 | https://opentdb.com/ | null | null | question_answer | null | true | null | {
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f44d24e1eecbf8f0bdc8583fe8385f35c4f5a12120409085ab803a2c885abf4c | Q: What nuts are used in the production of marzipan? A: Almonds | trivia | en | extracted | train | CC-BY-SA-4.0 | https://opentdb.com/api_config.php | atomicdeep_public_release_v1 | https://opentdb.com/ | null | null | question_answer | null | true | null | {
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a1529fe8bd24a4e6e4df7ee42b1279ae478c8b9a4532b47a7e7a73c155ec98c7 | Q: What is the second-largest city in Lithuania? A: Kaunas | trivia | en | extracted | train | CC-BY-SA-4.0 | https://opentdb.com/api_config.php | atomicdeep_public_release_v1 | https://opentdb.com/ | null | null | question_answer | null | true | null | {
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130ba6ac363918aeee761f9043e9a2cf169886a87b3f8a6f92493b47ad3535b4 | Please pass me the salt. | everyday_sentences | en | synthetic | train | synthetic-model-output | https://huggingface.co/Qwen/Qwen3.8-Flash-Next | atomicdeep_public_release_v1 | qwen3.8-flash-next | null | single_message | null | true | null | {
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d2ab7e3ff8170a00d55648c4d7a1b946d7d9683046ae60d635a6f32dd9d4fae5 | What time does the train leave? | everyday_sentences | en | synthetic | train | synthetic-model-output | https://huggingface.co/Qwen/Qwen3.8-Flash-Next | atomicdeep_public_release_v1 | qwen3.8-flash-next | null | single_message | null | true | null | {
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a750fc9dcab4aef9cf60131a10d3f336a862e42a2136429933efda9b50be23d3 | Turn left at the next corner. | everyday_sentences | en | synthetic | train | synthetic-model-output | https://huggingface.co/Qwen/Qwen3.8-Flash-Next | atomicdeep_public_release_v1 | qwen3.8-flash-next | null | single_message | null | true | null | {
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c5833474c5935dafb87869e6bc34fce7474603861a82e46b4b2a8b7db5e6731d | I forgot my keys. | everyday_sentences | en | synthetic | train | synthetic-model-output | https://huggingface.co/Qwen/Qwen3.8-Flash-Next | atomicdeep_public_release_v1 | qwen3.8-flash-next | null | single_message | null | true | null | {
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"... |
End of preview. Expand in Data Studio
Atomic Deep · Short Message
An open research dataset of compact language units for language modelling and agent communication. Developed by Atomic Deep, Short Message brings together everyday messages, sentences, code, questions and answers in a common format with source attribution.
Snapshot 2026-10-03 · 4,059,552 messages · 14 categories · en
The Short Message idea
Short Message treats a small, meaningful piece of language as the basic data unit. Each record pairs its text with a category, origin and source information. These units support research into compact language models, controlled data mixtures and concise communication between agents.
Dataset contents
| Configuration | Records | Content |
|---|---|---|
messages |
4,059,552 | Messages, sentences, code and question–answer items |
reasoning_experimental |
192 | Short prompts, answers and explanations |
Messages are distributed as 443.08 MB of compressed Parquet. Records retain their text, category, language, origin, source terms and recorded quality checks.
Sources
| Origin | Messages | Share |
|---|---|---|
| Synthetic / generated | 4,007,540 | 98.72% |
| Extracted public text | 52,012 | 1.28% |
Source and generator attribution is available in each record and the source ledger. Synthetic origin describes how a record was produced; generated text may reproduce existing material.
Examples
SMS-style text · synthetic
Hi, running 10 mins late to coffee. Start without me!
Terms: synthetic-model-output · Record ID: 84ced58c5acf1ebf0d96977585835a9f8f88372dc18c7e29dfc52119861ccbbc
Code snippets · synthetic
def greet(name):
return f'Hello, {name}!'
print(greet('Alice'))
Terms: synthetic-model-output · Record ID: 8b2586b6fd4a883d3128533119a78ab2c60511fd334ffc3786feccde3e1dcf27
Trivia · extracted
Q: How many islands does Kuwait have? A: 9
Terms: CC-BY-SA-4.0 · Record ID: 30a31214cef4aa27e8feb479c8c6ac9c518a56d6fb216d0d8fff5a07dc1c2b5e
Short reasoning · experimental
The separate pilot contains 192 scripted examples in arithmetic, unit conversion and ordering. Each combines a short prompt, an answer and an authored explanation, with an independent answer verifier.
| Prompt | Answer | Explanation |
|---|---|---|
| A price is 360 credits. Apply a 20% discount. What is the final price in credits? | 288 | Keep 80%: 360 × 80/100 = 288. |
The pilot is experimental and marked training_eligible: false. Template families stay within one split; domains and language patterns remain shared. See the reasoning manifest for the schema, character limits and verification details.
Quick start
from datasets import load_dataset
revision = "snapshot-2026-10-03-r3"
messages = load_dataset("itchson/atomic-deep-short-message", "messages",
split="train", revision=revision, streaming=True)
print(next(iter(messages)))
Use "reasoning_experimental" as the configuration name to load the reasoning pilot.
| Split | Messages | Experimental reasoning |
|---|---|---|
| train | 3,859,336 | 96 |
| validation | 120,310 | 48 |
| test | 79,906 | 48 |
This is a versioned snapshot. File checksums and release metadata are in the release manifest.
Licence and data quality
Terms vary by source. See LICENSE.md and the upstream licences for attribution and usage conditions. The authored reasoning fixtures are CC0-1.0.
The corpus is predominantly synthetic and automatically checked. Accuracy, originality and freedom from benchmark overlap are not guaranteed; review record provenance and quality fields for downstream use.
Contributing
Contributions are welcome: new public sources, corrections, broader language coverage, compact reasoning tasks and evaluation tools. Open a Discussion or submit a pull request following CONTRIBUTING.md.
Citation
@misc{atomicdeep_short_message_2026,
author = {Atomic Deep},
title = {Atomic Deep Short Message},
year = {2026},
url = {https://huggingface.co/datasets/itchson/atomic-deep-short-message},
note = {Snapshot 2026-10-03; revision snapshot-2026-10-03-r3}
}
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