File size: 5,199 Bytes
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license: cc-by-sa-4.0
language: en
multilinguality: monolingual
size_categories: n<1K
source_datasets:
- original
annotations_creators:
- machine-generated
language_creators:
- machine-generated
tags:
- rpg
- game-ai
- tactical-decision-making
- fantasy
- mud
- llm
- conversations
- turn-based-combat
- multi-turn
- simulation
- synthetic
- dialogue
- conversational-ai
- large-language-models
- instruction
- llm-training
- conversational-ai
- training-data
- retrieval-augmented-generation
- agentic
task_categories:
- text-generation
task_ids:
- dialogue-generation
- text-simplification
- language-modeling
---
name: RPG_DM_Simulation_Combat_LLM_Training
pretty_name: Magician MUD Conversations
description:
20 turn-by-turn gameplay conversations from a text-based dungeon crawler RPG (MUD style).
Each conversation captures strategic decision-making in fantasy combat, including player status,
enemy encounters, resource management, and combat outcomes. Ideal for fine-tuning language models
for RPG dialogue generation, tactical decision-making, and game state understanding.
Get the full 30K record dataset on Gumroad at https://datadeveloper1.gumroad.com/l/lmfhbg
The full CJ Jones' synthetic dataset catalog is available at: https://datadeveloper1.gumroad.com
<a href="https://datadeveloper1.gumroad.com/l/dxxja" style="display: inline-block; background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); color: white; font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif; font-weight: 600; font-size: 16px; padding: 14px 28px; border-radius: 50px; text-decoration: none; box-shadow: 0 4px 15px rgba(0,0,0,0.2); transition: transform 0.2s, box-shadow 0.2s; border: 1px solid rgba(255,255,255,0.2);" onmouseover="this.style.transform='translateY(-2px)'; this.style.boxShadow='0 6px 20px rgba(0,0,0,0.3)';" onmouseout="this.style.transform='translateY(0)'; this.style.boxShadow='0 4px 15px rgba(0,0,0,0.2)';">Want more? 🚀 Get the AI Startup Bundle from Gumroad.</a>
dataset_size: 500
splits:
train: 400
validation: 50
test: 50
dataset_structure:
description: Each instance represents a single conversation turn.
fields:
conversation_id: string
game_id: string
turn_number: int
speaker: string
message: string
game_state: dict
selected_choice: string
choice_number: int
choice_reason: string
attacked_entities: list
combat_outcome: dict
game_outcome: string
considerations:
social_impact: Fantasy violence only; no real-world sensitive content.
bias: >
Contains combat-focused scenarios in a Western fantasy RPG setting.
AI choices may not reflect human player behavior.
limitations: >
Small dataset (500 conversations). Synthetic data, domain-specific. Not suitable for large-scale pre-training.
recommended_use_cases:
- Fine-tuning small to medium language models (≤7B parameters)
- Training supervised game-playing agents
- RPG dialogue systems
- Tactical AI research
- Game design education
not_recommended_use_cases:
- Large-scale pre-training
- Real-world decision-making systems
- Medical, financial, or safety-critical applications
citation:
bibtex: |
@dataset{magician-mud-conversations-2025,
title = {Magician MUD Conversations: A Dataset of 500 Tactical RPG Dialogues},
author = {Magician MUD Simulator Team},
year = {2025},
publisher = {Hugging Face},
version = {1.0.0},
url = {https://huggingface.co/datasets/RPG_DM_Simulation_Combat_LLM_Training}
}
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