Instructions to use IPADS-SAI/MobiMind-Decider-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use IPADS-SAI/MobiMind-Decider-7B with Transformers:

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

pipe = pipeline("image-text-to-text", model="IPADS-SAI/MobiMind-Decider-7B")
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 AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("IPADS-SAI/MobiMind-Decider-7B")
model = AutoModelForImageTextToText.from_pretrained("IPADS-SAI/MobiMind-Decider-7B")
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?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use IPADS-SAI/MobiMind-Decider-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "IPADS-SAI/MobiMind-Decider-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "IPADS-SAI/MobiMind-Decider-7B",
		"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/IPADS-SAI/MobiMind-Decider-7B

SGLang

How to use IPADS-SAI/MobiMind-Decider-7B 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 "IPADS-SAI/MobiMind-Decider-7B" \
    --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": "IPADS-SAI/MobiMind-Decider-7B",
		"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 "IPADS-SAI/MobiMind-Decider-7B" \
        --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": "IPADS-SAI/MobiMind-Decider-7B",
		"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"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use IPADS-SAI/MobiMind-Decider-7B with Docker Model Runner:
```
docker model run hf.co/IPADS-SAI/MobiMind-Decider-7B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

MobiMind-Decider-7B Model

This is the Decider Model of MobiAgent with 7B parameters, as presented in the paper MobiAgent: A Systematic Framework for Customizable Mobile Agents, capable of high-level GUI interaction decision in GUI agent task execution.

Paper Abstract

With the rapid advancement of Vision-Language Models (VLMs), GUI-based mobile agents have emerged as a key development direction for intelligent mobile systems. However, existing agent models continue to face significant challenges in real-world task execution, particularly in terms of accuracy and efficiency. To address these limitations, we propose MobiAgent, a comprehensive mobile agent system comprising three core components: the MobiMind-series agent models, the AgentRR acceleration framework, and the MobiFlow benchmarking suite. Furthermore, recognizing that the capabilities of current mobile agents are still limited by the availability of high-quality data, we have developed an AI-assisted agile data collection pipeline that significantly reduces the cost of manual annotation. Compared to both general-purpose LLMs and specialized GUI agent models, MobiAgent achieves state-of-the-art performance in real-world mobile scenarios.

About MobiAgent

MobiAgent is a powerful mobile agent system including:

An agent model family: MobiMind
An agent acceleration framework: AgentRR
An agent benchmark: MobiFlow

System Architecture:

Evaluation Results

Usage

Deploy model inference service with vLLM:

vllm serve IPADS-SAI/MobiMind-Decider-7B

For more usage details, e.g., execute GUI tasks with ADB or our Android App, please refer to our repo!

Downloads last month: 22

Safetensors

Model size

8B params

Tensor type

BF16

Model tree for IPADS-SAI/MobiMind-Decider-7B

Quantizations

1 model

Collection including IPADS-SAI/MobiMind-Decider-7B

MobiMind

Collection

MobiMind Model Family of MobiAgent • 7 items • Updated Dec 8, 2025

Paper for IPADS-SAI/MobiMind-Decider-7B

MobiAgent: A Systematic Framework for Customizable Mobile Agents

Paper • 2509.00531 • Published Aug 30, 2025 • 8