Instructions to use codebasic/Qwen3-8B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use codebasic/Qwen3-8B-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf codebasic/Qwen3-8B-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf codebasic/Qwen3-8B-GGUF:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf codebasic/Qwen3-8B-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf codebasic/Qwen3-8B-GGUF:F16

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 codebasic/Qwen3-8B-GGUF:F16
# Run inference directly in the terminal:
./llama-cli -hf codebasic/Qwen3-8B-GGUF:F16

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 codebasic/Qwen3-8B-GGUF:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf codebasic/Qwen3-8B-GGUF:F16

Use Docker

docker model run hf.co/codebasic/Qwen3-8B-GGUF:F16

LM Studio
Jan
Ollama
How to use codebasic/Qwen3-8B-GGUF with Ollama:
```
ollama run hf.co/codebasic/Qwen3-8B-GGUF:F16
```
Unsloth Desktop

How to use codebasic/Qwen3-8B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf codebasic/Qwen3-8B-GGUF:F16

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "codebasic/Qwen3-8B-GGUF:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use codebasic/Qwen3-8B-GGUF with Docker Model Runner:
```
docker model run hf.co/codebasic/Qwen3-8B-GGUF:F16
```

Lemonade

How to use codebasic/Qwen3-8B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull codebasic/Qwen3-8B-GGUF:F16

Run and chat with the model

lemonade run user.Qwen3-8B-GGUF-F16

List all available models

lemonade list

Hermes Agent

How to use codebasic/Qwen3-8B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf codebasic/Qwen3-8B-GGUF:F16

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default codebasic/Qwen3-8B-GGUF:F16

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use codebasic/Qwen3-8B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf codebasic/Qwen3-8B-GGUF:F16

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "codebasic/Qwen3-8B-GGUF:F16" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Qwen3-8B-GGUF

🤖 코드베이직 제공

이 모델은 **코드베이직(codebasic)**에서 GGUF 포맷으로 변환·배포하였습니다.

이 리포지토리는 Qwen3-8B 모델을 여러 GGUF 양자화 버전으로 제공합니다.
llama.cpp, text-generation-webui, koboldcpp 등 GGUF 포맷을 지원하는 다양한 환경에서 사용할 수 있습니다.

📂 제공 파일

파일명	양자화 방식	메모리 요구량(대략)	설명
`Qwen3-8B-F16.gguf`	FP16 (비양자화)	~16GB	원본 FP16 가중치 (GPU/고사양 환경)
`Qwen3-8B-Q8_0.gguf`	Q8_0	~9GB	고품질 양자화, 거의 FP16 수준의 정확도

💡 메모리 요구량은 추정치이며, 환경에 따라 다를 수 있습니다.

🚀 사용 방법

1. Docker (llama.cpp Q8_0 예시)

docker run -v /path/to/models:/models \
    ghcr.io/ggml-org/llama.cpp:full \
    --run -m /models/Qwen3-8B/Qwen3-8B-Q8_0.gguf \
    -p "언어 모델 소개"

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GGUF

Model size

8B params

Architecture

qwen3

Hardware compatibility

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