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license: cc-by-4.0
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# Llama-3-8B-Racing-Level-Design-Expert (GGUF)
## 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.
## 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.
## 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.
### 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)
## 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.
## 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
## 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 |