| --- |
| 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 |
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| 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> |
|
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|
|
| 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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|
|
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