| --- |
| language: |
| - en |
| license: cc-by-nc-4.0 |
| pretty_name: Complexity Atlas Posttrain |
| task_categories: |
| - text-generation |
| tags: |
| - supervised-fine-tuning |
| - post-training |
| - card-based-generation |
| - semantic-audit |
| configs: |
| - config_name: chat |
| data_files: |
| - split: train |
| path: data/sft-v13/chat/train-*.parquet |
| - config_name: instruct |
| data_files: |
| - split: train |
| path: data/sft-v13/instruct/train-*.parquet |
| - split: validation |
| path: data/sft-v13/instruct/validation.parquet |
| - split: diagnostic |
| path: data/sft-v13/instruct/diagnostic.parquet |
| --- |
| |
| # Complexity Atlas Posttrain |
|
|
| **Dataset release: v1.0.13** |
|
|
| Complexity Atlas Posttrain is an original English post-training corpus built |
| from linked semantic cards, intent-specific completion contracts, |
| family-specific answer decks and auditable composition rules. The released |
| prompts and responses use Complexity-authored material. No third-party |
| conversation rows and no model-generated dialogue are included. |
|
|
| ## Model-facing SFT projections |
|
|
| The `chat` and `instruct` configurations expose the canonical v1.0.13 |
| model-facing projection by interaction mode. Each row is stored once and |
| belongs to exactly one configuration. |
|
|
| The projection removes exact prompt and response conflicts, selects compatible |
| surface cards and applies the `complexity-chat-v1` serialization contract. |
| Historical source projections are excluded. Native 32k token shards remain |
| available as derived training artifacts but are not Dataset Viewer inputs. |
|
|
| | Split | Examples | Purpose | |
| |---|---:|---| |
| | train | 396,138 | supervised fine-tuning | |
| | validation | 28 | separately authored held-out exchanges | |
| | diagnostic | 672 | deterministic family-coverage checks | |
| | **total** | **396,838** | | |
|
|
| | Configuration | Train examples | Additional splits | |
| |---|---:|---| |
| | `chat` | 203,899 | — | |
| | `instruct` | 192,239 | validation, diagnostic | |
|
|
| The complete native-32k projection contains **94,815,656 serialized tokens**, |
| including **31,390,124 supervised assistant tokens**. The training split |
| contains 31,366,545 supervised tokens. Exact projected prompts and responses |
| are unique. |
|
|
| The training data is stored in nine Parquet shards of at most 50,000 rows. |
| Every file uses 5,000-row groups, Zstandard compression and a page index so the |
| Hugging Face Dataset Viewer can read bounded previews without scanning a large |
| monolithic file. |
|
|
| ## What changed in v1.0.13 |
|
|
| The v1.0.13 projection adds an independent casual-conversation source instead |
| of converting task instructions into artificial dialogue. It contributes 398 |
| training conversations built from 420 original topic/context pairs, with four- |
| and six-turn exchanges covering everyday observations, preferences, hobbies, |
| small decisions and natural topic shifts. Source-pair groups cannot cross the |
| train/validation boundary. The existing fourteen assistant families remain |
| available alongside this additive source. |
|
|
| The casual-conversation source audit reports: |
|
|
| - 100% exact conversation and final-response uniqueness; |
| - zero source-pair overlap between train and validation; |
| - no surface hand or response structure above 5%; |
| - no four-word phrase above 5% of messages; |
| - 3.10% / 0% / 0% MiniLM semantic-neighbor ratios for prompts, responses and |
| complete conversations at cosine 0.98; |
| - 0.5% for all three views with Mixedbread at the same threshold. |
|
|
| ## Tokenized native 32k shards |
|
|
| `tokenized/32k-v13/` contains the exact v1.0.13 projection for the native 32k |
| tokenizer used by the 306.5M checkpoint: |
|
|
| - little-endian `uint32` input IDs; |
| - aligned little-endian `int32` labels; |
| - `-100` for system and user positions; |
| - assistant-only causal loss and an EOS target; |
| - per-example offsets in `examples.jsonl` and `sft.idx.json`; |
| - the required `complexity-chat-v1` template in `chat_template.json`. |
|
|
| Parquet remains the canonical readable form. The 28-row validation split is |
| independently authored. The 672-row diagnostic split is deterministic and must |
| not be presented as independent human evaluation. |
|
|
| ## Limitations |
|
|
| - The corpus is experimental and English-only. |
| - The held-out evaluation set is small and is not a comprehensive benchmark. |
| - Automated structural checks do not replace downstream model evaluation. |
| - Casual dialogue remains a small additive source rather than the majority of |
| the full projection. |
| - Embedding similarity is a diagnostic and not proof of conversational |
| correctness or independence. |
|
|
| ## Intended use |
|
|
| This dataset is intended for experimental supervised post-training of small |
| English language models. Evaluate general-language regression, repetition, |
| held-out behavior and safety before deployment. |
|
|
| ## Provenance and license |
|
|
| The original cards, schemas, compositions, curation and released dataset |
| artifacts are licensed under **CC BY-NC 4.0**. |
|
|
| Attribution: **Complexity — Complexity Atlas Posttrain** |
|
|
| The Apache-2.0 builder and audit code is maintained at |
| <https://github.com/Complexity-ML/complexity-card-corpus>. |
|
|
