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license: cc-by-4.0 |
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# Llama-3-8B-Racing-Level-Design-Expert (GGUF) |
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## 1. Model Summary (λͺ¨λΈ κ°μ) |
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**[EN]** This model is a specialized Small Language Model (SLM) fine-tuned for analyzing racing game level design components and player preferences. It integrates 20+ years of industry expertise from Nexon (KartRider series) with academic research data. |
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**[KR]** λ³Έ λͺ¨λΈμ λ μ΄μ± κ²μ λ 벨 λμμΈμ κ΅¬μ± μμμ νλ μ΄μ΄ μ νΈλλ₯Ό λΆμνκΈ° μν΄ νμΈνλλ SLM(Small Language Model)μ
λλ€. λ₯μ¨ γμΉ΄νΈλΌμ΄λγ μ리μ¦μμ 20λ
μ΄μ μμ μ€λ¬΄ λ
Ένμ°μ νμ μ λ°μ΄ν°λ₯Ό κ²°ν©νμμ΅λλ€. |
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## 2. About the Author |
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### Kim Tae-Wan |
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* **Current Role**: Game Developer & Researcher at NEXON (20+ years of experience) |
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* **Academic Background**: |
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* Ph.D. Student in Technology at Sogang University Graduate School of Metaverse |
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* M.S. in Game Design from Gachon University |
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* B.F.A. from Pusan National University |
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* **Expertise**: Level Design for the *KartRider* series, World Building Systems, and LLM-based Content Pipelines. |
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## 3. Research Context (μ°κ΅¬ λ°°κ²½) |
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**[EN]** The training dataset is based on the author's Master's thesis, which identifies 19 key level design variables and their impact on player satisfaction. |
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**[KR]** λ³Έ λͺ¨λΈμ νμ΅ λ°μ΄ν°μ
μ μ μμ μμ¬ νμ λ
Όλ¬Έμ λ°νμΌλ‘ ν©λλ€. λ μ΄μ± κ²μμ 19κ°μ§ ν΅μ¬ λ 벨 λμμΈ λ³μ(μκ° μ»€λΈ, ν€μ΄ν, κ°μ νΈλ¦¬κ±° λ±)μ μ μ λ§μ‘±λ κ°μ μκ΄κ΄κ³λ₯Ό νμ΅νμμ΅λλ€. |
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### Key Research Variables (ν΅μ¬ μ°κ΅¬ λ³μ): |
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* Acute Curves (μκ° μ»€λΈ) |
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* Hairpin Turns (ν€μ΄ν) |
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* Acceleration Triggers (κ°μ νΈλ¦¬κ±°) |
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* Verticality and Slopes (κ³ μ μ°¨ λ° κ²½μ¬λ‘) |
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* Visibility and Obstacles (μμΌ λ° μ₯μ λ¬Ό) |
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## 4. Intended Use (μ£Όμ μ©λ) |
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* **Design Automation**: Automated analysis of track structures during the planning stage. |
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* **Preference Prediction**: Evaluating the potential success of a track based on player preference data. |
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* **Research Integration**: Part of the "VN Studio" and "Persona AI System" projects for automated game content generation. |
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## 5. Technical Details (κΈ°μ μ¬μ) |
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* **Base Model**: Llama-3-8B (4-bit quantized) |
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* **Format**: GGUF (Optimized for local inference via LM Studio/Ollama) |
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* **Training Method**: Supervised Fine-Tuning (SFT) using Unsloth |
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## 6. Reference & Citation (μΈμ© λ° μ°Έκ³ λ¬Έν) |
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**Thesis**: *A Study on Level Design Components and Player Preferences in Racing Game Content* (Gachon Univ.) |
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* **Link**: https://www.dbpia.co.kr/journal/detail?nodeId=T14760144 |
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**Contact**: https://github.com/Taewan627 |