File size: 2,710 Bytes
bddad62
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
---
license: cc-by-4.0
---
# Llama-3-8B-Racing-Level-Design-Expert (GGUF)

## 1. Model Summary (๋ชจ๋ธ ๊ฐœ์š”)
**[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 ๋ชจ๋ธ์€ ๋ ˆ์ด์‹ฑ ๊ฒŒ์ž„ ๋ ˆ๋ฒจ ๋””์ž์ธ์˜ ๊ตฌ์„ฑ ์š”์†Œ์™€ ํ”Œ๋ ˆ์ด์–ด ์„ ํ˜ธ๋„๋ฅผ ๋ถ„์„ํ•˜๊ธฐ ์œ„ํ•ด ํŒŒ์ธํŠœ๋‹๋œ SLM(Small Language Model)์ž…๋‹ˆ๋‹ค. ๋„ฅ์Šจ ใ€Š์นดํŠธ๋ผ์ด๋”ใ€‹ ์‹œ๋ฆฌ์ฆˆ์—์„œ 20๋…„ ์ด์ƒ ์Œ“์€ ์‹ค๋ฌด ๋…ธํ•˜์šฐ์™€ ํ•™์ˆ ์  ๋ฐ์ดํ„ฐ๋ฅผ ๊ฒฐํ•ฉํ•˜์˜€์Šต๋‹ˆ๋‹ค.

## 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 ๋ชจ๋ธ์˜ ํ•™์Šต ๋ฐ์ดํ„ฐ์…‹์€ ์ €์ž์˜ ์„์‚ฌ ํ•™์œ„ ๋…ผ๋ฌธ์„ ๋ฐ”ํƒ•์œผ๋กœ ํ•ฉ๋‹ˆ๋‹ค. ๋ ˆ์ด์‹ฑ ๊ฒŒ์ž„์˜ 19๊ฐ€์ง€ ํ•ต์‹ฌ ๋ ˆ๋ฒจ ๋””์ž์ธ ๋ณ€์ˆ˜(์˜ˆ๊ฐ ์ปค๋ธŒ, ํ—ค์–ดํ•€, ๊ฐ€์† ํŠธ๋ฆฌ๊ฑฐ ๋“ฑ)์™€ ์œ ์ € ๋งŒ์กฑ๋„ ๊ฐ„์˜ ์ƒ๊ด€๊ด€๊ณ„๋ฅผ ํ•™์Šตํ•˜์˜€์Šต๋‹ˆ๋‹ค.

### Key Research Variables (ํ•ต์‹ฌ ์—ฐ๊ตฌ ๋ณ€์ˆ˜):
* Acute Curves (์˜ˆ๊ฐ ์ปค๋ธŒ)
* Hairpin Turns (ํ—ค์–ดํ•€)
* Acceleration Triggers (๊ฐ€์† ํŠธ๋ฆฌ๊ฑฐ)
* Verticality and Slopes (๊ณ ์ €์ฐจ ๋ฐ ๊ฒฝ์‚ฌ๋กœ)
* Visibility and Obstacles (์‹œ์•ผ ๋ฐ ์žฅ์• ๋ฌผ)

## 4. Intended Use (์ฃผ์š” ์šฉ๋„)
* **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 (๊ธฐ์ˆ  ์‚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 (์ธ์šฉ ๋ฐ ์ฐธ๊ณ ๋ฌธํ—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

---
**Contact**: https://github.com/Taewan627