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

MiniArt-1.0

File size: 1,627 Bytes

---
language:
- en
license: apache-2.0
tags:
- text-generation
- gguf
- creative
- roleplay
- lm-studio
pipeline_tag: text-generation
---

# 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

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

### Ollama

```bash
ollama create miniart -f ./Modelfile
ollama run miniart
```

### llama.cpp

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

---

## License

This project is licensed under the [Apache 2.0 License](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.