README.md

---
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>.