Image-Text-to-Text
Transformers
Safetensors
qwen2_5_vl
multimodal
gui
conversational
text-generation-inference
Instructions to use IPADS-SAI/MobiMind-Decider-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("IPADS-SAI/MobiMind-Decider-7B") model = AutoModelForMultimodalLM.from_pretrained("IPADS-SAI/MobiMind-Decider-7B", device_map="auto") 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 Settings
- 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
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license:
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pipeline_tag: image-text-to-text
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tags:
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library_name: transformers
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# MobiMind-Decider-7B Model
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---
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license: apache-2.0
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pipeline_tag: image-text-to-text
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tags:
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- multimodal
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- gui
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library_name: transformers
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# MobiMind-Decider-7B Model
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This is the Decider Model of [MobiAgent](https://github.com/IPADS-SAI/MobiAgent) with 7B parameters, capable of high-level GUI interaction decision in GUI agent task execution.
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## About MobiAgent
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**MobiAgent** is a powerful mobile agent system including:
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* **An agent model family**: MobiMind
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* **An agent acceleration framework**: AgentRR
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* **An agent benchmark**: MobiFlow
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**System Architecture:**
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<div align="center">
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<p align="center">
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<img src="https://github.com/IPADS-SAI/MobiAgent/blob/main/assets/arch.png" width="100%"/>
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</p>
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</div>
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## Evaluation Results
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<div align="center">
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<p align="center">
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<img src="https://github.com/IPADS-SAI/MobiAgent/blob/main/assets/result1.png" width="30%" style="margin-right: 15px;"/>
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<img src="https://github.com/IPADS-SAI/MobiAgent/blob/main/assets/result2.png" width="30%" style="margin-right: 15px;"/>
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<img src="https://github.com/IPADS-SAI/MobiAgent/blob/main/assets/result3.png" width="30%"/>
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</p>
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</div>
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## Usage
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Deploy with vLLM:
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```bash
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vllm serve IPADS-SAI/MobiMind-Decider-7B
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```
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For more usage details, please refer to our repo!
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