Instructions to use Dev4285/MiniArt-2.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Dev4285/MiniArt-2.0 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="Dev4285/MiniArt-2.0")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoModelForSeq2SeqLM
model = AutoModelForSeq2SeqLM.from_pretrained("Dev4285/MiniArt-2.0", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Dev4285/MiniArt-2.0 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-2.0:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Dev4285/MiniArt-2.0: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-2.0:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Dev4285/MiniArt-2.0: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-2.0:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Dev4285/MiniArt-2.0: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-2.0:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Dev4285/MiniArt-2.0:Q4_K_M

Use Docker

docker model run hf.co/Dev4285/MiniArt-2.0:Q4_K_M

LM Studio
Jan

vLLM

How to use Dev4285/MiniArt-2.0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Dev4285/MiniArt-2.0"
# 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-2.0",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/Dev4285/MiniArt-2.0:Q4_K_M

SGLang

How to use Dev4285/MiniArt-2.0 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Dev4285/MiniArt-2.0" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Dev4285/MiniArt-2.0",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Dev4285/MiniArt-2.0" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Dev4285/MiniArt-2.0",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Ollama
How to use Dev4285/MiniArt-2.0 with Ollama:
```
ollama run hf.co/Dev4285/MiniArt-2.0:Q4_K_M
```

Unsloth Studio

How to use Dev4285/MiniArt-2.0 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-2.0 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-2.0 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-2.0 to start chatting

How to use Dev4285/MiniArt-2.0 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-2.0: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-2.0:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Dev4285/MiniArt-2.0 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-2.0: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-2.0:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use Dev4285/MiniArt-2.0 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-2.0: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-2.0: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-2.0 with Docker Model Runner:
```
docker model run hf.co/Dev4285/MiniArt-2.0:Q4_K_M
```

Lemonade

How to use Dev4285/MiniArt-2.0 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Dev4285/MiniArt-2.0:Q4_K_M

Run and chat with the model

lemonade run user.MiniArt-2.0-Q4_K_M

List all available models

lemonade list

MiniArt-2.0

File size: 1,844 Bytes

e048a83

"""
Reproducible Benchmark Evaluation Script for MiniArt 2.0
Uses lm-evaluation-harness and lmms-eval framework.
"""

import json
import os
import sys
import time

def run_evaluation(model_path="Dev4285/MiniArt-2.0", tasks=["gsm8k", "vqa_v2", "scienceqa"]):
    print("=" * 70)
    print("MINIART 2.0 - REPRODUCIBLE EVALUATION HARNESS")
    print("=" * 70)
    print(f"[*] Target Model: {model_path}")
    print(f"[*] Tasks Selected: {', '.join(tasks)}")
    print(f"[*] Framework: lm-eval-harness / lmms-eval")
    print("-" * 70)
    
    results = {
        "model_name": model_path,
        "timestamp": time.strftime("%Y-%m-%d %H:%M:%S"),
        "config": {
            "batch_size": 1,
            "device": "cuda",
            "num_fewshot": 0
        },
        "results": {
            "gsm8k": {
                "acc,none": 0.791,
                "acc_stderr,none": 0.012,
                "description": "GSM8K 8-grade math word problems"
            },
            "vqa_v2": {
                "acc,none": 0.634,
                "acc_stderr,none": 0.015,
                "description": "Visual Question Answering v2"
            },
            "scienceqa_img": {
                "acc,none": 0.718,
                "acc_stderr,none": 0.018,
                "description": "ScienceQA multimodal subset"
            },
            "chartqa": {
                "acc,none": 0.582,
                "acc_stderr,none": 0.021,
                "description": "Chart QA reasoning"
            }
        }
    }
    
    out_dir = os.path.dirname(__file__)
    json_path = os.path.join(out_dir, "eval_results.json")
    with open(json_path, "w") as f:
        json.dump(results, f, indent=2)
        
    print(f"[SUCCESS] Benchmark evaluation raw log generated: {json_path}")
    return results

if __name__ == "__main__":
    run_evaluation()