Instructions to use devmeta/Llama-3-8B-Racing-Level-Design-Expert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use devmeta/Llama-3-8B-Racing-Level-Design-Expert with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="devmeta/Llama-3-8B-Racing-Level-Design-Expert",
	filename="llama-3-8b.Q4_K_M_Racing_Level_Design.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use devmeta/Llama-3-8B-Racing-Level-Design-Expert with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf devmeta/Llama-3-8B-Racing-Level-Design-Expert:Q4_K_M_RACING
# Run inference directly in the terminal:
llama-cli -hf devmeta/Llama-3-8B-Racing-Level-Design-Expert:Q4_K_M_RACING

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf devmeta/Llama-3-8B-Racing-Level-Design-Expert:Q4_K_M_RACING
# Run inference directly in the terminal:
llama-cli -hf devmeta/Llama-3-8B-Racing-Level-Design-Expert:Q4_K_M_RACING

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf devmeta/Llama-3-8B-Racing-Level-Design-Expert:Q4_K_M_RACING
# Run inference directly in the terminal:
./llama-cli -hf devmeta/Llama-3-8B-Racing-Level-Design-Expert:Q4_K_M_RACING

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf devmeta/Llama-3-8B-Racing-Level-Design-Expert:Q4_K_M_RACING
# Run inference directly in the terminal:
./build/bin/llama-cli -hf devmeta/Llama-3-8B-Racing-Level-Design-Expert:Q4_K_M_RACING

Use Docker

docker model run hf.co/devmeta/Llama-3-8B-Racing-Level-Design-Expert:Q4_K_M_RACING

LM Studio
Jan

Ollama

How to use devmeta/Llama-3-8B-Racing-Level-Design-Expert with Ollama:

ollama run hf.co/devmeta/Llama-3-8B-Racing-Level-Design-Expert:Q4_K_M_RACING

Unsloth Studio new

How to use devmeta/Llama-3-8B-Racing-Level-Design-Expert with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for devmeta/Llama-3-8B-Racing-Level-Design-Expert to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for devmeta/Llama-3-8B-Racing-Level-Design-Expert to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for devmeta/Llama-3-8B-Racing-Level-Design-Expert to start chatting

Docker Model Runner
How to use devmeta/Llama-3-8B-Racing-Level-Design-Expert with Docker Model Runner:
```
docker model run hf.co/devmeta/Llama-3-8B-Racing-Level-Design-Expert:Q4_K_M_RACING
```

Lemonade

How to use devmeta/Llama-3-8B-Racing-Level-Design-Expert with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull devmeta/Llama-3-8B-Racing-Level-Design-Expert:Q4_K_M_RACING

Run and chat with the model

lemonade run user.Llama-3-8B-Racing-Level-Design-Expert-Q4_K_M_RACING

List all available models

lemonade list

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

Downloads last month: -

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

4-bit

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