Update README.md

README.md

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-license: cc-by-4.0
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+license: cc-by-4.0
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+# Llama-3-8B-Racing-Level-Design-Expert (GGUF)
+
+## 1. Model Summary (모델 개요)
+**[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]** 본 모델은 레이싱 게임 레벨 디자인의 구성 요소와 플레이어 선호도를 분석하기 위해 파인튜닝된 SLM(Small Language Model)입니다. 넥슨 《카트라이더》 시리즈에서 20년 이상 쌓은 실무 노하우와 학술적 데이터를 결합하였습니다.
+
+## 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 (연구 배경)
+**[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]** 본 모델의 학습 데이터셋은 저자의 석사 학위 논문을 바탕으로 합니다. 레이싱 게임의 19가지 핵심 레벨 디자인 변수(예각 커브, 헤어핀, 가속 트리거 등)와 유저 만족도 간의 상관관계를 학습하였습니다.
+
+### Key Research Variables (핵심 연구 변수):
+* Acute Curves (예각 커브)
+* Hairpin Turns (헤어핀)
+* Acceleration Triggers (가속 트리거)
+* Verticality and Slopes (고저차 및 경사로)
+* Visibility and Obstacles (시야 및 장애물)
+
+## 4. Intended Use (주요 용도)
+* **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 참고문헌)
+**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
+
+---
+**Contact**: https://github.com/Taewan627