Instructions to use appvoid/arco-3-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 appvoid/arco-3-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 appvoid/arco-3-gguf:Q8_0
# Run inference directly in the terminal:
llama cli -hf appvoid/arco-3-gguf:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf appvoid/arco-3-gguf:Q8_0
# Run inference directly in the terminal:
llama cli -hf appvoid/arco-3-gguf:Q8_0

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 appvoid/arco-3-gguf:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf appvoid/arco-3-gguf:Q8_0

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 appvoid/arco-3-gguf:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf appvoid/arco-3-gguf:Q8_0

Use Docker

docker model run hf.co/appvoid/arco-3-gguf:Q8_0

LM Studio
Jan

vLLM

How to use appvoid/arco-3-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "appvoid/arco-3-gguf"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "appvoid/arco-3-gguf",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/appvoid/arco-3-gguf:Q8_0

Ollama
How to use appvoid/arco-3-gguf with Ollama:
```
ollama run hf.co/appvoid/arco-3-gguf:Q8_0
```

Unsloth Studio

How to use appvoid/arco-3-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 appvoid/arco-3-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 appvoid/arco-3-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for appvoid/arco-3-gguf to start chatting

Docker Model Runner
How to use appvoid/arco-3-gguf with Docker Model Runner:
```
docker model run hf.co/appvoid/arco-3-gguf:Q8_0
```

Lemonade

How to use appvoid/arco-3-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull appvoid/arco-3-gguf:Q8_0

Run and chat with the model

lemonade run user.arco-3-gguf-Q8_0

List all available models

lemonade list

Atomic Chat

Model Card

Name	Link
Repo	arco 3
Arch	qwen 3
Author	appvoid
Quant	`llama.cpp`

CLI

llama-cli --hf "appvoid/arco-3-gguf:Q8_0" -p "The meaning to life is"

These are the weights that were used for meta-arena. Check the original repo for details. Big shout out to Georgi and the llama.cpp team for their contributions to the edge world.

Downloads last month: 24

GGUF

Model size

0.6B params

Architecture

qwen3

Hardware compatibility

8-bit

Model tree for appvoid/arco-3-gguf

Base model

appvoid/arco-3

Quantized

(1)

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