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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 (๋ชจ๋ธ ๊ฐ์) |
| | **[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. |
| | **[KR]** ๋ณธ ๋ชจ๋ธ์ ๋ ์ด์ฑ ๊ฒ์ ๋ ๋ฒจ ๋์์ธ์ ๊ตฌ์ฑ ์์์ ํ๋ ์ด์ด ์ ํธ๋๋ฅผ ๋ถ์ํ๊ธฐ ์ํด ํ์ธํ๋๋ SLM(Small Language Model)์
๋๋ค. ๋ฅ์จ ใ์นดํธ๋ผ์ด๋ใ ์๋ฆฌ์ฆ์์ 20๋
์ด์ ์์ ์ค๋ฌด ๋
ธํ์ฐ์ ํ์ ์ ๋ฐ์ดํฐ๋ฅผ ๊ฒฐํฉํ์์ต๋๋ค. |
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| | ## 2. About the Author |
| | ### Kim Tae-Wan |
| | * **Current Role**: Game Developer & Researcher at NEXON (20+ years of experience) |
| | * **Academic Background**: |
| | * Ph.D. Student in Technology at Sogang University Graduate School of Metaverse |
| | * M.S. in Game Design from Gachon University |
| | * B.F.A. from Pusan National University |
| | * **Expertise**: Level Design for the *KartRider* series, World Building Systems, and LLM-based Content Pipelines. |
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| | ## 3. Research Context (์ฐ๊ตฌ ๋ฐฐ๊ฒฝ) |
| | **[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. |
| | **[KR]** ๋ณธ ๋ชจ๋ธ์ ํ์ต ๋ฐ์ดํฐ์
์ ์ ์์ ์์ฌ ํ์ ๋
ผ๋ฌธ์ ๋ฐํ์ผ๋ก ํฉ๋๋ค. ๋ ์ด์ฑ ๊ฒ์์ 19๊ฐ์ง ํต์ฌ ๋ ๋ฒจ ๋์์ธ ๋ณ์(์๊ฐ ์ปค๋ธ, ํค์ดํ, ๊ฐ์ ํธ๋ฆฌ๊ฑฐ ๋ฑ)์ ์ ์ ๋ง์กฑ๋ ๊ฐ์ ์๊ด๊ด๊ณ๋ฅผ ํ์ตํ์์ต๋๋ค. |
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| | ### Key Research Variables (ํต์ฌ ์ฐ๊ตฌ ๋ณ์): |
| | * Acute Curves (์๊ฐ ์ปค๋ธ) |
| | * Hairpin Turns (ํค์ดํ) |
| | * Acceleration Triggers (๊ฐ์ ํธ๋ฆฌ๊ฑฐ) |
| | * Verticality and Slopes (๊ณ ์ ์ฐจ ๋ฐ ๊ฒฝ์ฌ๋ก) |
| | * Visibility and Obstacles (์์ผ ๋ฐ ์ฅ์ ๋ฌผ) |
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| | ## 4. Intended Use (์ฃผ์ ์ฉ๋) |
| | * **Design Automation**: Automated analysis of track structures during the planning stage. |
| | * **Preference Prediction**: Evaluating the potential success of a track based on player preference data. |
| | * **Research Integration**: Part of the "VN Studio" and "Persona AI System" projects for automated game content generation. |
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| | ## 5. Technical Details (๊ธฐ์ ์ฌ์) |
| | * **Base Model**: Llama-3-8B (4-bit quantized) |
| | * **Format**: GGUF (Optimized for local inference via LM Studio/Ollama) |
| | * **Training Method**: Supervised Fine-Tuning (SFT) using Unsloth |
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| | ## 6. Reference & Citation (์ธ์ฉ ๋ฐ ์ฐธ๊ณ ๋ฌธํ) |
| | **Thesis**: *A Study on Level Design Components and Player Preferences in Racing Game Content* (Gachon Univ.) |
| | * **Link**: https://www.dbpia.co.kr/journal/detail?nodeId=T14760144 |
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| | **Contact**: https://github.com/Taewan627 |