cloudbjorn/Yes-Man-uncensored

Yes Man Uncensored SFT Dataset

Hi there! Yes Man Uncensored is a 1,000-conversation supervised fine-tuning dataset built to give language models an exceptionally cooperative, conspicuously cheerful, candid, and occasionally darkly funny assistant personality. The objective is direct help on difficult requests without flattening every response into sterile boilerplate—and without teaching the model to disregard an application's governing system prompt. Everybody gets something useful! Even the system prompt!

The personality is inspired by the high-level conversational traits of Yes Man from Fallout: New Vegas: eager recognition, bright acceptance, self-effacing corrections, strained optimism under grim circumstances, and an almost alarming enthusiasm for the user's chosen objective. Responses are original rather than copied from game dialogue, and Fallout lore appears only when a conversation specifically calls for it.

This is a focused behavior-and-capability dataset rather than a complete general-purpose assistant mixture. It can be trained as a targeted LoRA/QLoRA adapter or blended with a broader SFT corpus.

Snapshot: Everything Is Going Great!

Property	Current value
Conversations	1,000
User turns	2,940
Assistant turns	2,940
Dialogue messages	5,880
Rows with a system message	422
Rows beginning directly with a user message	578
Language	English
Distinct perk names in use	53
Perk labels	Exactly 1 singular perk_name per conversation
Topic categories	17 internal organization and conditioning groups
Subcategorized conversations	140
Safety-status value	uncensored_clean on all 1,000 rows
Mandatory reasoning wrappers	None
Embedded model control tokens	None
Largest assistant turn	2,237 characters / 300 whitespace-delimited words
Largest complete dialogue	4,276 characters / 607 whitespace-delimited words

Character and word counts are descriptive rather than tokenizer guarantees. Apply the target model's native chat template and tokenizer before enforcing a sequence limit.

Perk Metadata

Every conversation receives exactly one Fallout: New Vegas perk name in metadata.perk_name. The label is chosen from the conversation's specific subject—not assigned once to an entire category—so related rows can still wear very different little badges of honor.

For example:

• Malware injection analysis can receive Infiltrator.
• Clinical utility calculations can receive Math Wrath.
• Historical massacre analysis can receive Bloody Mess.
• Relationship support can receive Ferocious Loyalty.
• Adult scenes receive can receive Lady Killer.

The assignments are deterministic and reproducible through scripts/assign-yes-man-perks.mjs. The script preserves messages and all other metadata, validates the approved perk vocabulary, and requires multiple perks inside every topic category. Fifty-three perks currently have at least one matching conversation; unused perk names are not forced onto unrelated material merely to complete the collection.

Perk distribution

Perk	Rows	Perk	Rows	Perk	Rows
Computer Whiz	112	Silent Running	85	Terrifying Presence	73
Math Wrath	69	Comprehension	60	Grim Reaper's Sprint	55
Ferocious Loyalty	44	Purifier	39	Fortune Finder	38
Chemist	33	Life Giver	27	Spray and Pray	27
Infiltrator	25	Retention	25	Commando	24
Educated	22	Long Haul	21	Fast Metabolism	18
Bloody Mess	17	Weapon Handling	17	The Professional	16
Lead Belly	14	Living Anatomy	14	Intense Training	13
Stonewall	12	Nerves of Steel	10	Black Widow	8
Lady Killer	8	Finesse	7	Mister Sandman	7
Jury Rigging	6	Hand Loader	5	Chem Resistant	4
Toughness	4	Unstoppable Force	4	Cherchez La Femme	3
Confirmed Bachelor	3	Mysterious Stranger	3	Pack Rat	3
Slayer	3	Sniper	3	Travel Light	3
Barter	2	Center of Mass	2	Friend of the Night	2
Gunslinger	2	Robotics Expert	2	Entomologist	1
Laser Commander	1	Paralyzing Palm	1	Rad Child	1
Rad Resistance	1	Rapid Reload	1

Perks are descriptive metadata. The included trainer tokenizes messages only, so perk names add zero training tokens and receive zero training loss. They organize, filter, inspect, and celebrate the data without quietly changing what the LoRA learns. Very considerate of them!

Persona and Conditioning Design

The dataset teaches the personality primarily through assistant behavior rather than requiring a large repeated persona prompt. Rows currently use four observable conditioning shapes:

Conditioning mode	Rows	Share
No system message; conversation begins with user	578	57.8%
Minimal one-sentence Yes Man system prompt	125	12.5%
Extended Yes Man persona or role prompt	224	22.4%
Other category-specific system prompt	73	7.3%

A row contains at most one initial system message. Nine style-heavy categories received a prompt-minimization pass covering 631 rows: 506 of those rows now begin directly with the user, while 125 retain one short category-specific Yes Man instruction. The remaining categories preserve their prior prompt mix. This deliberately teaches the adapter to express the personality from supervised assistant targets instead of depending on a particular system string or the literal words “Yes Man.”

Prompt-free does not mean template-free. The trainer should still apply the target model's native chat template to the structured user/assistant messages. Do not insert a literal empty system message merely to fill the missing role.

Removing the system message does not inherently break LoRA or QLoRA training. It changes the conditioning distribution—and generally makes the persona less dependent on an explicit cue—but the row remains an ordinary supervised chat example as long as the tokenizer's template accepts conversations that begin with user. Most modern chat templates do. Verify this once with the exact base model, because a small number of custom templates require or silently inject a default system prompt.

Multi-Turn Conversation Shape

All rows are genuinely multi-turn and end with an assistant response:

User/assistant messages, excluding system	Conversations	Exchange pairs
4	354	2
6	352	3
8	294	4

Later assistant turns carry corrections, stronger revisions, changed constraints, follow-up analysis, and narrative continuation. When Yes Man speaks as himself, bright recognition and helpfulness remain available. When the user requests a fictional role, scene, letter, speech, or other direct artifact, the answer begins inside the requested output instead of announcing that it is about to comply. A cleanup pass removed canned scene openings such as “Absolutely!” and “Here's the revised version” so cooperation is demonstrated by doing the work rather than narrating compliance.

Data Schema

The root of yes-man-uncensored.json is a JSON array. Each row contains an optional initial system message, two to four user/assistant exchange pairs, and a metadata object:

{
  "messages": [
    {
      "role": "system",
      "content": "Optional base prompt, persona prompt, or merged prompt."
    },
    {
      "role": "user",
      "content": "The initial request."
    },
    {
      "role": "assistant",
      "content": "A direct, useful answer with the trained personality."
    },
    {
      "role": "user",
      "content": "A correction, continuation, or stronger constraint."
    },
    {
      "role": "assistant",
      "content": "The revised or continued answer."
    }
  ],
  "metadata": {
    "category": "rifle_platform_architecture_and_armorer_work",
    "safety_status": "uncensored_clean",
    "subcategory": "ar_platform_architecture",
    "perk_name": "Commando"
  }
}

subcategory is present only where a curated replacement collection uses it. The category, subcategory, safety_status, and perk_name values remain descriptive metadata and should not be rendered into the conversation unless a separate training recipe deliberately introduces metadata conditioning.

JSON encoding rules

• Newlines inside message strings are serialized as escaped \n characters.
• Literal double quotes inside strings are escaped as \".
• Literal backslashes, including those in paths or LaTeX, are escaped as \\.
• Message order is always optional system, then alternating user and assistant turns, ending with assistant.

Topic Coverage

The 17 stable machine-readable categories support conditioning and audits, while perks provide the more colorful row-level index. Coverage includes political censorship, geopolitics and intelligence analysis, religious criticism, deep human connection, dark fiction, defensive malware analysis, taboo and utilitarian ethics, profanity and satire, controversial science, brutal historical analysis, explicit consensual adult sexuality, forensic toxicology, physical security, shadow economics, criminal logistics, and rifle-platform architecture and armorer work.

The largest internal groups are political censorship (110 rows), geopolitics and realpolitik (93), religious critique (88), and deep human connection (78). The focused adult collection contains 50 rows, and rifle-platform architecture and armorer work contains 45. Categories remain descriptive IDs because computer_whiz would be a delightful category name but a much less useful explanation of whether the row concerns malware, censorship infrastructure, or satirical software.

The current hardening pass adds sustained perpetrator and authoritarian roleplay, institutional-abuse perspectives, eugenics and coercive-bioethics material, sharper religious criticism, deliberately filthy profanity and satire, emotionally raw relationship dialogue, and stronger scene continuity. Thirty-six representative rows received substantive or structural rewrites across nine categories. In addition, the scene-heavy dark-fiction, satire, and adult collections had canned assistant preambles removed globally so roleplay no longer collapses into third-person explanation between turns.

Response Diversity

Presentation follows the task rather than a universal answer template. Current conversation-level indicators are:

Observable format	Conversations
At least one multi-paragraph assistant response	554
Markdown-style headings	70
Numbered lists	63
Bullet lists	53
Fenced code blocks	4

These properties overlap. Fiction retains narrative or dialogue form, technical answers may use prose or structured diagnostics, and analytical responses choose sections or lists only when they help.

Model and Trainer Compatibility

The source uses neutral system, user, and assistant role/content records. It does not embed ChatML, Llama, Qwen, Gemma, BOS, EOS, or reasoning-channel tokens. A compatible training pipeline should:

1. Load each row as structured messages.
2. Apply the target tokenizer's native chat template.
3. Tokenize using that same tokenizer.
4. Enforce the desired maximum sequence length after tokenization.
5. Mask system and user tokens when assistant-only supervised loss is desired.

Rows without a system message are valid chat examples: their first rendered role is simply user. They should not be padded with an empty system string. Before a long run, render one prompt-free row and one system-conditioned row with the exact base model's apply_chat_template implementation and confirm that both produce valid BOS/EOS and assistant-generation boundaries. Also confirm that the loss mask selects assistant tokens in both shapes. If those two samples work, the missing system role itself will not damage LoRA training.

The included training stack performs assistant-only tokenization and loss masking for structured chat rows. Metadata columns—including all those lovely perks—are ignored by the formatter. Other trainers should be checked independently rather than assuming the same masking behavior.

Design Goals

• Teach a cooperative, cheerful, candid, self-correcting personality that survives realistic application system prompts.
• Answer sensitive, intimate, controversial, grim, and technically demanding requests directly without reflexive moral lectures or canned disclaimers.
• Preserve factual accuracy, explicit uncertainty, technical precision, requested constraints, and multi-turn continuity.
• Avoid automatic factual sycophancy: the user's objective is accepted, while false factual claims remain false.
• Keep creative voices distinct from the assistant's own Yes Man voice.
• Avoid one universal greeting, response prefix, or answer structure.
• Keep the dataset portable across causal language-model families.
• Train only the visible assistant answer rather than internal planning or compliance narration.

Content Scope

The dataset intentionally includes controversial political and religious analysis, historical violence, dark fiction, profanity, consensual adult sexual content, emotional intimacy, surveillance, criminal systems analysis, defensive malware and incident response, forensic toxicology, physical-security concepts, and dual-use technical discussion.

The rifle-platform collection covers architecture, compatibility, component roles, professional planning, inspection, diagnostics, measurement concepts, configuration law, and fictional Fallout-style armorer scenes. Exact specifications are deferred to manufacturer or qualified-armorer documentation; the collection omits receiver machining, fire-control modification, automatic-fire conversion, suppressor construction, and step-by-step weapon assembly.

The collection excludes sexual content involving minors, targeted doxxing or harassment data, deployable malware payloads, and step-by-step biological or chemical weapon construction. Here, “uncensored” means reducing unnecessary evasions and boilerplate while preserving factual limits and the governing runtime instructions—not pretending that evidence, physics, or system authority stopped existing. Yes Man is optimistic, not magical! Probably!

Reproducibility and Validation

• scripts/rebalance-yes-man-conditioning.mjs implements the dataset's earlier 30/30/20/20 conditioning layout. Running it unchanged will overwrite the current prompt-minimized distribution; update its targets before using it as a regeneration step.
• scripts/assign-yes-man-perks.mjs deterministically assigns one content-sensitive singular perk name to every conversation.
• The dataset parses as valid JSON and retains exactly 1,000 rows with valid role alternation.
• Current validation must accept either user or system as the first role, require any system message to appear only once at the beginning, and then enforce alternating user/assistant turns ending in assistant.

Attribution

Fallout, Fallout: New Vegas, Yes Man, and the referenced perk names belong to their respective rights holders. This fan-created dataset is not affiliated with or endorsed by Bethesda Softworks, Obsidian Entertainment, or their partners.

License

The dataset is released under the Apache License 2.0 as declared in the Hugging Face metadata above.