| ---
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| language:
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| - en
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| license: other
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| license_name: mixed-per-source
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| license_link: https://huggingface.co/datasets/jncarlo/tinymix#sources-and-licensing
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| pretty_name: tinymix
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| size_categories:
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| - 1M<n<10M
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| task_categories:
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| - text-generation
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| tags:
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| - small-language-models
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| - synthetic-data
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| - curriculum
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| - tinystories
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| - babylm
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| configs:
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| - config_name: 1M
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| data_files:
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| - split: train
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| path: data/1M/train*.parquet
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| - split: validation_dialogue
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| path: data/1M/validation_dialogue.parquet
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| - split: validation_expository
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| path: data/1M/validation_expository.parquet
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| - split: validation_narrative
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| path: data/1M/validation_narrative.parquet
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| - config_name: 10M
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| data_files:
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| - split: train
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| path: data/10M/train*.parquet
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| - split: validation_dialogue
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| path: data/10M/validation_dialogue.parquet
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| - split: validation_expository
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| path: data/10M/validation_expository.parquet
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| - split: validation_narrative
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| path: data/10M/validation_narrative.parquet
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| - config_name: 100M
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| data_files:
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| - split: train
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| path: data/100M/train*.parquet
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| - split: validation_books
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| path: data/100M/validation_books.parquet
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| - split: validation_dialogue
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| path: data/100M/validation_dialogue.parquet
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| - split: validation_expository
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| path: data/100M/validation_expository.parquet
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| - split: validation_narrative
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| path: data/100M/validation_narrative.parquet
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| - split: validation_news
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| path: data/100M/validation_news.parquet
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| - split: validation_poetry
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| path: data/100M/validation_poetry.parquet
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| - config_name: 1000M
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| data_files:
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| - split: train
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| path: data/1000M/train*.parquet
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| - split: validation_books
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| path: data/1000M/validation_books.parquet
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| - split: validation_dialogue
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| path: data/1000M/validation_dialogue.parquet
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| - split: validation_expository
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| path: data/1000M/validation_expository.parquet
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| - split: validation_narrative
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| path: data/1000M/validation_narrative.parquet
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| - split: validation_news
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| path: data/1000M/validation_news.parquet
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| - split: validation_poetry
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| path: data/1000M/validation_poetry.parquet
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| ---
|
|
|
| # tinymix
|
|
|
| Small, **genre-balanced English pretraining corpora** in four sizes (about 1M, 10M, 100M and 1000M words), built for training
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| very small language models. The mix combines synthetic child-level stories and dialogue, simplified real text,
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| encyclopedic and didactic text, books, poetry and news, all filtered toward clear, self-contained documents. It is
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| inspired by TinyStories, SimpleStories, TinyHelen and the BabyLM challenge.
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|
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| What is different from using those datasets directly:
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|
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| - **Several kinds of text in one mix** (narrative, dialogue and chat, expository, books, poetry, news), not only stories or only conversations.
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| - **Four sizes built with the same recipe**, with complexity limits that grow with size.
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| - **No non-commercial sources.** TinyChat (CC-BY-NC) and the BabyLM corpus (no clear license, includes subtitle and child-speech data) were left out on purpose.
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| - **Provenance per document** (`source`, `genre`) and **validation splits per genre**, to see which kind of text a model is weak at.
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| - The whole pipeline is included (`code/`) and every filter decision is documented below.
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|
|
| ## Subsets
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|
|
| | Subset | Train words | Train docs | ~Tokens (BPE) |
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| |---|---|---|---|
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| | `1M` | 1.0M | 4,135 | ~1.3M |
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| | `10M` | 9.96M | 42,117 | ~12.9M |
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| | `100M` | 99.5M | 338,230 | ~129.3M |
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| | `1000M` | 994.92M | 3,040,575 | ~1293.4M |
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|
|
| Words are whitespace-separated words in `train`. Each subset also has about 0.5% of the documents per genre held out as `validation_<genre>` splits
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| (at least one document per genre; the `1M` validation splits are therefore tiny, 4 to 8 documents each, and noisy).
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|
|
| ```python
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| from datasets import load_dataset
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| ds = load_dataset("jncarlo/tinymix", "10M") # splits: train, validation_narrative, validation_dialogue, ...
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| print(ds["train"][0]) # {'text': ..., 'source': 'tinystories', 'genre': 'narrative'}
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| ```
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|
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| Columns: `text` (str), `source` (key of the table below), `genre` (`narrative`, `dialogue`, `expository`, `books`, `poetry`, `news`).
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| Documents are independent; join them with an end-of-text token when training. Dialogues are formatted one turn per line
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| (`Mia: ...`, or `User:` / `Assistant:` for assistant chats).
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|
|
| ## Composition
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|
|
| Share of training words by genre:
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|
|
| | Genre | 1M | 10M | 100M | 1000M |
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| |---|---|---|---|---|
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| | narrative | 38.0% | 35.0% | 21.0% | 15.0% |
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| | dialogue | 21.9% | 25.0% | 21.0% | 19.0% |
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| | expository | 40.1% | 40.0% | 48.0% | 56.0% |
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| | books | 0% | 0% | 8.0% | 7.0% |
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| | poetry | 0% | 0% | 1.0% | 1.0% |
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| | news | 0% | 0% | 1.0% | 2.0% |
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|
|
| Million words per source:
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|
|
| | Source | 1M (M words) | 10M (M words) | 100M (M words) | 1000M (M words) |
|
| |---|---|---|---|---|
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| | TinyStories V2 (GPT-4 split) | 0.189 | 1.494 | 7.959 | 39.793 |
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| | SimpleStories | 0.189 | 1.99 | 7.964 | 59.698 |
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| | Cosmopedia v0.1, `stories` | 0 | 0 | 4.972 | 49.763 |
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| | TinyDialogues | 0.12 | 1.494 | 6.969 | 19.655 |
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| | SODA | 0.099 | 0.994 | 8.955 | 83.828 |
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| | UltraChat 200k | 0 | 0 | 3.974 | 72.915 |
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| | SmolTalk (everyday-conversations, smol-summarize, smol-rewrite) | 0 | 0 | 0.994 | 12.66 |
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| | TinyHelen Leaner-Pretrain (book, conversation, textbook, web, wiki) | 0.15 | 1.499 | 9.946 | 33.921 |
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| | Simple English Wikipedia (20231101.simple) | 0.1 | 0.996 | 9.951 | 21.201 |
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| | English Wikipedia, lead paragraphs | 0 | 0 | 0 | 32.885 |
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| | Cosmopedia v2 (SmolLM-Corpus) | 0.1 | 0.996 | 11.944 | 195.975 |
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| | Cosmopedia v0.1 (wikihow, khanacademy, openstax) | 0.05 | 0.497 | 2.984 | 29.767 |
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| | tiny-textbooks (`textbook` column) | 0 | 0 | 3.98 | 64.294 |
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| | FineWeb-Edu (sample-10BT, score >= 4) | 0 | 0 | 8.96 | 179.075 |
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| | Project Gutenberg (English books) | 0 | 0 | 7.96 | 69.649 |
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| | Gutenberg Poetry Corpus | 0 | 0 | 0.995 | 9.95 |
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| | CNN/DailyMail 3.0.0 | 0 | 0 | 0.995 | 19.897 |
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|
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| The 10M and 100M subsets are within about 1% of their targets (train plus validation splits). In the 10M build,
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| sources that could not fill their quota with the filters (Simple English Wikipedia, Cosmopedia v0.1) were topped up by
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| same-genre substitute sources (tiny-textbooks, Cosmopedia v2), capped per source; that is why `tiny-textbooks` appears
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| there. The 1M subset keeps the 10M proportions except that Cosmopedia stories (not in the 10M mix) were replaced by TinyStories and SimpleStories.
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|
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| The 1000M subset is also within about 1% of its target. Five sources ran out under the filters before their quota and were
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| topped up by same-genre substitutes (million words): TinyDialogues by SODA (9.3), SmolTalk by UltraChat (12.3), TinyHelen by
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| tiny-textbooks (15.9), Simple English Wikipedia by tiny-textbooks (8.7) and English Wikipedia lead paragraphs by Cosmopedia v2 (17.0).
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|
|
| ## How it was built
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|
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| 1. **Normalisation**: `ftfy`, NFKC, ASCII quotes and dashes, whitespace; URLs, e-mails, phone numbers, code, LaTeX and HTML removed. Documents with any character outside ASCII, Latin-1 and Latin Extended-A letters, common punctuation and a few math symbols are rejected whole (reason `charset` in the build stats).
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| 2. **Language**: fastText `lid.176`, English with confidence >= 0.9 (skipped for TinyDialogues, whose one-word toddler turns confuse it).
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| 3. **Quality heuristics**: Gopher-style rules (mean word length, symbol and digit ratio, repeated lines, repeated bigrams, stop words), minimum length, complete ending (drops truncated generations), a short list of LLM tics (`delve`, `tapestry`, ...) and an explicit-content word list.
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| 4. **Anti-template**: no 4-word opening may exceed 0.5% of a source's documents.
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| 5. **Deduplication across all sources**: exact hash and MinHash LSH (5-word shingles, Jaccard >= 0.8).
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| 6. **Complexity control by size** (vocabulary coverage = share of lowercase tokens inside the top-N English words of `wordfreq`):
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|
|
| | | 10M | 100M | 1000M |
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| |---|---|---|---|
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| | Reference vocabulary | top 8k | top 15k | top 30k |
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| | Min vocabulary coverage | 90% | 90% | 90% |
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| | Max Flesch-Kincaid grade (narrative, news, books) | 5 | 8 | 12 |
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| | Max Flesch-Kincaid grade (expository) | 9 | 10 | 12 |
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| | Document length | 80 to 600 words | 80 to 1200 words | 80 to 2000 words |
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| | Dialogue: mean words per turn | <= 25 | <= 40 | <= 80 |
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|
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| Assistant chats (`User:` / `Assistant:`) may average up to 4 times the per-turn cap and need at least 2 turns (named dialogues need 4),
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| because summarisation and rewrite tasks have 2 turns and assistant answers are long.
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|
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| Documents longer than the maximum are split at paragraph borders. The 1M subset is a prefix slice of the 10M build; 10M, 100M and 1000M were built separately with the same recipe, so they are **not** guaranteed to be subsets of each other (a later fix to the end-of-document check, which wrongly rejected Wikipedia articles that end in a heading, was only applied to sources rebuilt for the 100M run).
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| 7. **Run safety nets** (used to build the larger subsets without manual tuning): a probe per source picks a starting filter strictness, strictness relaxes in up to three logged steps when yield is too low, and shortfalls go to substitute sources.
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|
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| Thresholds were calibrated on real data: the first values (95% coverage in the top 5k words, Flesch-Kincaid <= 5 for everything)
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| accepted only 3% of SimpleStories and 0.6% of Cosmopedia v2, so they were relaxed as described above.
|
|
|
| ## Sources and licensing
|
|
|
| | Source | License | Role |
|
| |---|---|---|
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| | [TinyStories V2 (GPT-4 split)](https://huggingface.co/datasets/roneneldan/TinyStories) | CDLA-Sharing-1.0 | child-level stories |
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| | [SimpleStories](https://huggingface.co/datasets/SimpleStories/SimpleStories) | MIT | varied simple stories |
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| | [Cosmopedia v0.1, `stories`](https://huggingface.co/datasets/HuggingFaceTB/cosmopedia) | Apache-2.0 | everyday stories |
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| | [TinyDialogues](https://huggingface.co/datasets/styfeng/TinyDialogues) | MIT | child-directed dialogue |
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| | [SODA](https://huggingface.co/datasets/allenai/soda) | CC-BY-4.0 | social dialogue |
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| | [UltraChat 200k](https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k) | MIT | user/assistant chat |
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| | [SmolTalk (everyday-conversations, smol-summarize, smol-rewrite)](https://huggingface.co/datasets/HuggingFaceTB/smoltalk) | Apache-2.0 | chat, summaries, rewrites |
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| | [TinyHelen Leaner-Pretrain (book, conversation, textbook, web, wiki)](https://huggingface.co/datasets/EmpathYang/TinyHelen) | MIT | simplified real text |
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| | [Simple English Wikipedia (20231101.simple)](https://huggingface.co/datasets/wikimedia/wikipedia) | CC-BY-SA-3.0 / GFDL | encyclopedic, simple |
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| | [English Wikipedia, lead paragraphs](https://huggingface.co/datasets/wikimedia/wikipedia) | CC-BY-SA-3.0 / GFDL | encyclopedic |
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| | [Cosmopedia v2 (SmolLM-Corpus)](https://huggingface.co/datasets/HuggingFaceTB/smollm-corpus) | Apache-2.0 (corpus: ODC-By) | synthetic textbooks, blogs, stories |
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| | [Cosmopedia v0.1 (wikihow, khanacademy, openstax)](https://huggingface.co/datasets/HuggingFaceTB/cosmopedia) | Apache-2.0 | procedural and didactic |
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| | [tiny-textbooks (`textbook` column)](https://huggingface.co/datasets/nampdn-ai/tiny-textbooks) | Apache-2.0 | short synthetic lessons |
|
| | [FineWeb-Edu (sample-10BT, score >= 4)](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu) | ODC-By-1.0 | educational web |
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| | [Project Gutenberg (English books)](https://huggingface.co/datasets/manu/project_gutenberg) | Project Gutenberg License (public domain in the US) | classic books |
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| | [Gutenberg Poetry Corpus](https://huggingface.co/datasets/biglam/gutenberg-poetry-corpus) | CC0-1.0 | poetry stanzas |
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| | [CNN/DailyMail 3.0.0](https://huggingface.co/datasets/abisee/cnn_dailymail) | Apache-2.0 (articles are (c) CNN / Daily Mail) | news |
|
|
|
| **Licensing and attribution.** This repository is a derivative compilation of the datasets above; each document keeps the
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| license of its `source`. Obligations that matter when redistributing or reusing it:
|
|
|
| - Attribution is required for CC-BY / ODC-By / CDLA sources (listed above).
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| - The Wikipedia parts (`simplewiki`, `enwiki_intro`) are **CC-BY-SA-3.0 / GFDL (share-alike)**. If you need a share-alike-free corpus, filter out those two sources.
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| - Cosmopedia, SmolTalk, UltraChat, TinyStories, SimpleStories, TinyHelen and tiny-textbooks contain **model-generated text**; check the terms of the generating model (GPT-4, GPT-4o-mini, Mixtral and others) for your use case.
|
| - CNN/DailyMail articles and FineWeb-Edu web pages are third-party content; they are included under the dataset licenses above, which do not transfer rights from the original publishers.
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| - Project Gutenberg texts had the Project Gutenberg header and footer removed; they are public domain in the United States, check your jurisdiction.
|
|
|
| No personal data was added; e-mails, phone numbers and URLs were removed, but residual personal information in web-derived sources cannot be ruled out.
|
|
|
| ## Sanity-check training runs
|
|
|
| Small GPTs (nanoGPT-style, own byte-level BPE trained on each training set). Each row shows its own parameter count and training tokens, so model sizes differ between rows. Scored in **bits per byte** (lower is better, comparable across tokenizers) on the held-out validation splits of **this** dataset. Baselines take the first 10M / 100M words of a single source with only `ftfy` cleaning. They are out of distribution on the genres they do not cover, so the table shows how much genre coverage matters, not a leaderboard. One seed, no tuning. Rows of different sizes are not comparable with each other. The 1M and 10M sets have no books, news or poetry split, hence n/a.
|
|
|
| | Training data | Params | Train tokens | narrative | dialogue | expository | books | news | poetry |
|
| |---|---|---|---|---|---|---|---|---|
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| | tinymix 1M | 9.59M | 1.71M | 1.683 | 1.830 | 2.276 | n/a | n/a | n/a |
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| | tinymix 10M | 9.59M | 17.18M | 0.883 | 0.875 | 1.470 | n/a | n/a | n/a |
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| | TinyStories only, 10M words | 17.41M | 12.05M | 1.111 | 2.512 | 2.708 | n/a | n/a | n/a |
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| | FineWeb-Edu only, 10M words | 17.41M | 15.71M | 1.639 | 1.769 | 1.565 | n/a | n/a | n/a |
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| | tinymix 100M | 46.43M | 136.82M | 0.697 | 0.697 | 0.987 | 1.349 | 1.368 | 1.819 |
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| | FineWeb-Edu only, 100M words | 46.29M | 142.51M | 1.331 | 1.312 | 1.215 | 1.726 | 1.670 | 2.306 |
|
|
|
| ## Limitations
|
|
|
| - **Not done**: perplexity filtering with an n-gram LM, LLM-judge coherence scoring, semantic deduplication, cluster caps and benchmark decontamination. Expect some residual near-duplicates and some low-quality documents.
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| - Some `tinyhelen` chunks start mid-thought, because the simplified web and book documents are chunks of longer texts.
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| - Synthetic sources share model-specific style and topics (names, morals, classroom settings). The anti-template cap reduces this but does not remove it.
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| - The toxicity filter is a short word list, not a classifier. Chat and web sources can still contain mildly sensitive content.
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| - English only, mostly simple modern language; it is a poor fit for models meant to handle technical, legal or archaic text.
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| - Word counts are whitespace words; token counts depend on your tokenizer.
|
|
|
| ## Reproduce
|
|
|
| `code/` contains the build scripts (`mix.py` quotas and tiers, `sources.py` adapters, `build.py` pipeline, `build_pool.py` for
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| 1000M, `assemble.py`, `publish.py`, `train.py` for the sanity-check runs) and `requirements.txt`. Building a subset:
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| `python build.py 10M` (or `python build_pool.py 1000M`), then `python assemble.py 10M`. The build streams the source datasets from the Hub.
|
|
|
| ## Acknowledgements
|
|
|
| Built on the work behind TinyStories, SimpleStories, TinyDialogues, SODA, UltraChat, SmolTalk and SmolLM-Corpus
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| (Cosmopedia, FineWeb-Edu), TinyHelen, tiny-textbooks, Project Gutenberg, the Gutenberg Poetry Corpus, Wikipedia and
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| CNN/DailyMail. Please cite the original datasets when you use this one.
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|
|
