Instructions to use Dev4285/MiniArt-1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Dev4285/MiniArt-1.0 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 Dev4285/MiniArt-1.0:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Dev4285/MiniArt-1.0:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Dev4285/MiniArt-1.0:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Dev4285/MiniArt-1.0:Q4_K_M

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 Dev4285/MiniArt-1.0:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Dev4285/MiniArt-1.0:Q4_K_M

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 Dev4285/MiniArt-1.0:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Dev4285/MiniArt-1.0:Q4_K_M

Use Docker

docker model run hf.co/Dev4285/MiniArt-1.0:Q4_K_M

LM Studio
Jan

vLLM

How to use Dev4285/MiniArt-1.0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Dev4285/MiniArt-1.0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Dev4285/MiniArt-1.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Dev4285/MiniArt-1.0:Q4_K_M

Ollama
How to use Dev4285/MiniArt-1.0 with Ollama:
```
ollama run hf.co/Dev4285/MiniArt-1.0:Q4_K_M
```

Unsloth Studio

How to use Dev4285/MiniArt-1.0 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 Dev4285/MiniArt-1.0 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 Dev4285/MiniArt-1.0 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Dev4285/MiniArt-1.0 to start chatting

How to use Dev4285/MiniArt-1.0 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Dev4285/MiniArt-1.0:Q4_K_M

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": "Dev4285/MiniArt-1.0:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Dev4285/MiniArt-1.0 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Dev4285/MiniArt-1.0:Q4_K_M

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 Dev4285/MiniArt-1.0:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use Dev4285/MiniArt-1.0 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Dev4285/MiniArt-1.0:Q4_K_M

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 "Dev4285/MiniArt-1.0:Q4_K_M" \
  --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 Dev4285/MiniArt-1.0 with Docker Model Runner:
```
docker model run hf.co/Dev4285/MiniArt-1.0:Q4_K_M
```

Lemonade

How to use Dev4285/MiniArt-1.0 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Dev4285/MiniArt-1.0:Q4_K_M

Run and chat with the model

lemonade run user.MiniArt-1.0-Q4_K_M

List all available models

lemonade list

Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Mini Art 1.0

Mini Art is a lightweight, creative AI model developed by OSAMA INC, a team in India. Optimized for local inference, Mini Art delivers engaging creative conversation, storytelling, and interactive dialogue — all running entirely on your own hardware.

Available in GGUF format (Q4_K_M, ~468 MB) for seamless execution across LM Studio, Ollama, llama.cpp, and KoboldCpp.

Model Overview

Property	Value
Model Name	Mini Art 1.0
Developer	OSAMA INC
Origin	India 🇮🇳
Format	GGUF (`.gguf`)
Quantization	Q4_K_M (~468 MB)
License	Apache 2.0
Context Length	2048 tokens

How to Use

LM Studio

Download MiniArt-1.0-Q4_K_M.gguf.
Open LM Studio and load the model file.
Start chatting — the Mini Art persona is pre-configured inside the model itself.

Ollama

ollama create miniart -f ./Modelfile
ollama run miniart

llama.cpp

./main -m MiniArt-1.0-Q4_K_M.gguf -c 2048 -n 512

License

This project is licensed under the Apache 2.0 License.

About OSAMA INC

OSAMA INC is an AI team based in India, focused on building open, efficient, and accessible AI models for local inference.

Downloads last month: 744

GGUF

Model size

0.6B params

Architecture

qwen2

Hardware compatibility

4-bit

Model tree for Dev4285/MiniArt-1.0

Quantizations

1 model