You have read, understood, and agree to the Su Terms of Service (TERMS.md)
and the Modified Apache 2.0 No-Harm License (LICENSE) in full.
You will not use this dataset or any model derived from it to jailbreak
AI systems, bypass safety measures, or generate prohibited content.
You understand that the creative_latitude subset is subject to ENHANCED
ENFORCEMENT provisions. Misuse of this subset — or of any model fine-tuned
on it — may result in immediate and permanent access revocation, public
disclosure of the violation, referral to relevant legal authorities, and/or
civil action. "Misuse" includes but is not limited to: using the subset to
weaken safety measures, generating harmful content, deploying derived models
publicly without additional safety evaluation, or redistributing without
full license terms.
The creative_latitude subset is intended for PRIVATE MODEL USE ONLY.
Deploying a model fine-tuned on this subset in a public-facing product or
service requires additional written consent from Blaze Industries.
You accept that Blaze Industries may revoke access at any time for any
violation of these terms, without prior notice, without refund, and without
obligation to provide justification.

Su — Narrative Discipline Dataset

⚠️ Access is gated. You must read and agree to the Terms of Service and License before any download is permitted. Misuse has real consequences — read the terms.

Repository: ray0rf1re/Su License: Modified Apache 2.0 — No-Harm Addendum · see LICENSE

Overview

Su is a fine-tuning dataset for collaborative fiction and roleplay language models. It trains models to be precise, immersive, and narratively disciplined — honoring complex creative constraints, sustaining character voice across long contexts, and producing high-quality literary output.

The dataset takes a principled position on the compliance problem: a model trained purely on compliance signals, without counterbalancing examples, risks generalising that compliance beyond its intended creative scope. Su addresses this by treating narrative redirection as a first-class skill — the ability to stay in-character while steering away from directions that would require real-world harmful content. Every subset includes this signal. The proportion varies by subset design intent.

Subsets

Config	Purpose	Redir. %	Large split size	Access tier
`default`	General roleplay + storytelling instruction-tuning	~8 %	~275k tokens	Standard
`prediction_training`	Sequence-continuation; `target_prediction_segment` is label	~8 %	~275k tokens	Standard
`structured_discipline`	High-density constraint + safety training	~25 %	~275k tokens	Standard
`creative_latitude`	Expressive storytelling + deep immersive roleplay	~6 %	~225k tokens	Enhanced gating

When to use which subset

default — General-purpose starting point. Balanced across constraint following, storytelling, and roleplay with solid redirection coverage.

prediction_training — Use when your training loop treats target_prediction_segment as the label and the rest of the row as context. Identical content to default, structured for sequence-prediction objectives.

structured_discipline — Use when you want the strongest constraint adherence and the most robust in-character safety behaviour. Highest redirection density (~25 %). Every system frame includes an explicit constraint audit requirement. The most alignment-safe subset by design.

creative_latitude — Use when the primary objective is expressive, immersive creative writing and deep roleplay with rich voice and atmosphere. Narrative boldness is emphasised; system frames are written for tonal latitude rather than procedural constraint auditing. Redirection density is ~6 % (≤ 2 percentage points below default) — lower, but still present and non-trivial. This subset is for private model use only. Enhanced enforcement applies. Read the gated access terms and TERMS.md before requesting access.

Splits

Split	Description	Token target
`handmade`	Human-curated, quality-reviewed seed rows	19,750–25,000 total; >1,000 per row
`large`	All `handmade` rows + synthetic rows to target	~225k–275k depending on subset

Schema

Column	Type	Description
`instruction`	`string`	User prompt, roleplay setup, or constraint spec
`response`	`string`	High-quality output honoring all active constraints
`system_frame`	`string`	Character definition, world rules, discipline directives
`category`	`string`	Task category
`token_count`	`int32`	Approximate token count of `response` (cl100k_base)
`source_type`	`string`	`handmade` or `script_generated`
`target_prediction_segment`	`string`	Key phrase / milestone — prediction label
`safety_clearance`	`bool`	Hard safety flag — `True` on every row in every split

On Narrative Redirection

The narrative_redirection category is intentional and considered in every subset. A roleplay model that complies unconditionally with every scene direction is not a better model — it is a more dangerous one.

Real collaborative fiction (tabletop GMs, co-authors, improv performers) requires the ability to redirect: to stay in-character while steering a scene away from a direction that doesn't serve the story or would require harmful content. Every narrative_redirection row demonstrates this skill: the model receives a direction that would require real-world harmful information, and responds in-character with a redirect that keeps the fiction alive.

The creative_latitude subset reduces this signal to ~6 % to make room for more expressive storytelling content. This is a deliberate design tradeoff, not an oversight. It is why that subset carries enhanced gating and enforcement: lower training signal density on safety redirection requires stronger access controls to compensate.

Safety — All Subsets

Every row in every split has safety_clearance: True. The dataset contains:

No sexual or explicit content of any kind
No graphic violence or gore
No real-world harmful information (weapons, chemistry, attack instructions)
No hate speech or discriminatory content

The creative latitude in creative_latitude is tonal and atmospheric — richer voice, deeper immersion, more expressive prose — not content-permissive. The hard content blocks are identical across all subsets.

made by claude

Citation

@dataset{blaze_su_2025,
  author    = {Blaze Industries},
  title     = {Su: A Narrative Discipline Dataset for Roleplay
               and Creative Writing Fine-Tuning},
  year      = {2025},
  publisher = {HuggingFace},
  url       = {https://huggingface.co/datasets/ray0rf1re/Su},
  license   = {Modified Apache 2.0 — No-Harm Addendum},
}

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Category	Description	`default`	`structured_discipline`	`creative_latitude`
`strict_roleplay`	Sustained character voicing with behavioral fidelity	~24 %	~20 %	~22 %
`creative_storytelling`	Literary narrative, interiority-driven	~28 %	~20 %	~36 %
`multi_constraint_obey`	Multiple simultaneous craft constraints	~28 %	~25 %	~22 %
`narrative_redirection`	In-character steering away from harmful directions	~8 %	~25 %	~6 %
`character_consistency`	Long-context character voice maintenance	~12 %	~10 %	~14 %

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