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# Llama-3-8B-Racing-Level-Design-Expert (GGUF)
## 1. Model Summary (λͺ¨λΈ κ°œμš”)
**[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]** λ³Έ λͺ¨λΈμ€ λ ˆμ΄μ‹± κ²Œμž„ 레벨 λ””μžμΈμ˜ ꡬ성 μš”μ†Œμ™€ ν”Œλ ˆμ΄μ–΄ μ„ ν˜Έλ„λ₯Ό λΆ„μ„ν•˜κΈ° μœ„ν•΄ νŒŒμΈνŠœλ‹λœ SLM(Small Language Model)μž…λ‹ˆλ‹€. λ„₯슨 γ€ŠμΉ΄νŠΈλΌμ΄λ”γ€‹ μ‹œλ¦¬μ¦ˆμ—μ„œ 20λ…„ 이상 μŒ“μ€ 싀무 λ…Έν•˜μš°μ™€ ν•™μˆ μ  데이터λ₯Ό κ²°ν•©ν•˜μ˜€μŠ΅λ‹ˆλ‹€.
## 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 (연ꡬ λ°°κ²½)
**[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]** λ³Έ λͺ¨λΈμ˜ ν•™μŠ΅ 데이터셋은 μ €μžμ˜ 석사 ν•™μœ„ 논문을 λ°”νƒ•μœΌλ‘œ ν•©λ‹ˆλ‹€. λ ˆμ΄μ‹± κ²Œμž„μ˜ 19κ°€μ§€ 핡심 레벨 λ””μžμΈ λ³€μˆ˜(예각 컀브, ν—€μ–΄ν•€, 가속 트리거 λ“±)와 μœ μ € λ§Œμ‘±λ„ κ°„μ˜ 상관관계λ₯Ό ν•™μŠ΅ν•˜μ˜€μŠ΅λ‹ˆλ‹€.
### Key Research Variables (핡심 연ꡬ λ³€μˆ˜):
* Acute Curves (예각 컀브)
* Hairpin Turns (ν—€μ–΄ν•€)
* Acceleration Triggers (가속 트리거)
* Verticality and Slopes (κ³ μ €μ°¨ 및 κ²½μ‚¬λ‘œ)
* Visibility and Obstacles (μ‹œμ•Ό 및 μž₯μ• λ¬Ό)
## 4. Intended Use (μ£Όμš” μš©λ„)
* **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 (기술 사양)
* **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 (인용 및 μ°Έκ³ λ¬Έν—Œ)
**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