Instructions to use codebasic/Qwen3-0.6B-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/Qwen3-0.6B-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/Qwen3-0.6B-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf codebasic/Qwen3-0.6B-GGUF:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf codebasic/Qwen3-0.6B-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf codebasic/Qwen3-0.6B-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/Qwen3-0.6B-GGUF:F16
# Run inference directly in the terminal:
./llama-cli -hf codebasic/Qwen3-0.6B-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/Qwen3-0.6B-GGUF:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf codebasic/Qwen3-0.6B-GGUF:F16

Use Docker

docker model run hf.co/codebasic/Qwen3-0.6B-GGUF:F16

LM Studio
Jan
Ollama
How to use codebasic/Qwen3-0.6B-GGUF with Ollama:
```
ollama run hf.co/codebasic/Qwen3-0.6B-GGUF:F16
```

Unsloth Studio

How to use codebasic/Qwen3-0.6B-GGUF 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 codebasic/Qwen3-0.6B-GGUF 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 codebasic/Qwen3-0.6B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for codebasic/Qwen3-0.6B-GGUF to start chatting

How to use codebasic/Qwen3-0.6B-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/Qwen3-0.6B-GGUF:F16

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/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/Qwen3-0.6B-GGUF:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use codebasic/Qwen3-0.6B-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/Qwen3-0.6B-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/Qwen3-0.6B-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"

Docker Model Runner
How to use codebasic/Qwen3-0.6B-GGUF with Docker Model Runner:
```
docker model run hf.co/codebasic/Qwen3-0.6B-GGUF:F16
```

Lemonade

How to use codebasic/Qwen3-0.6B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull codebasic/Qwen3-0.6B-GGUF:F16

Run and chat with the model

lemonade run user.Qwen3-0.6B-GGUF-F16

List all available models

lemonade list

Hermes Agent

How to use codebasic/Qwen3-0.6B-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/Qwen3-0.6B-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/Qwen3-0.6B-GGUF:F16

Run Hermes

hermes

Atomic Chat

Qwen3 0.6B - GGUF 포맷 (float16)

📌 본 모델은 코드베이직 Codebasic에서 GGUF 포맷으로 변환되었습니다.

이 저장소는 Qwen3 0.6B 모델을 llama.cpp의 변환 도구를 사용하여 GGUF 포맷으로 변환한 버전입니다. 이 포맷은 llama.cpp, llamafile, llm, text-generation-webui 등에서 사용할 수 있습니다.

🔧 변환 정보

변환 스크립트: llama.cpp/convert.py
출력 형식: GGUF (--outtype f16)
모델 크기: 0.6B 파라미터
원본 모델: Qwen/Qwen1.5-0.5B

🧠 모델 구조

Qwen3 계열은 Transformer 기반 언어 모델로, 다양한 언어와 작업에 사용할 수 있습니다. 본 모델은 한국어 지원 여부에 따라 사용자가 실험적으로 확인해 보시는 것을 권장합니다.

🧪 예시 사용법 (`llama.cpp`)

./main -m qwen3-0.6b.gguf -p "안녕하세요. 자기소개를 해 주세요."

Downloads last month: 6

GGUF

Model size

0.8B params

Architecture

qwen3

Hardware compatibility

16-bit

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Qwen3 0.6B - GGUF 포맷 (float16)

🔧 변환 정보

🧠 모델 구조

🧪 예시 사용법 (llama.cpp)

🧪 예시 사용법 (`llama.cpp`)