Instructions to use microsoft/OmniParser with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/OmniParser with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="microsoft/OmniParser")# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("microsoft/OmniParser") model = AutoModelForMultimodalLM.from_pretrained("microsoft/OmniParser", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use microsoft/OmniParser with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "microsoft/OmniParser" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "microsoft/OmniParser", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/microsoft/OmniParser
- SGLang
How to use microsoft/OmniParser 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 "microsoft/OmniParser" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "microsoft/OmniParser", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "microsoft/OmniParser" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "microsoft/OmniParser", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use microsoft/OmniParser with Docker Model Runner:
docker model run hf.co/microsoft/OmniParser
| backbone: | |
| - - -1 | |
| - 1 | |
| - Conv | |
| - - 64 | |
| - 3 | |
| - 2 | |
| - - -1 | |
| - 1 | |
| - Conv | |
| - - 128 | |
| - 3 | |
| - 2 | |
| - - -1 | |
| - 3 | |
| - C2f | |
| - - 128 | |
| - true | |
| - - -1 | |
| - 1 | |
| - Conv | |
| - - 256 | |
| - 3 | |
| - 2 | |
| - - -1 | |
| - 6 | |
| - C2f | |
| - - 256 | |
| - true | |
| - - -1 | |
| - 1 | |
| - Conv | |
| - - 512 | |
| - 3 | |
| - 2 | |
| - - -1 | |
| - 6 | |
| - C2f | |
| - - 512 | |
| - true | |
| - - -1 | |
| - 1 | |
| - Conv | |
| - - 1024 | |
| - 3 | |
| - 2 | |
| - - -1 | |
| - 3 | |
| - C2f | |
| - - 1024 | |
| - true | |
| - - -1 | |
| - 1 | |
| - SPPF | |
| - - 1024 | |
| - 5 | |
| ch: 3 | |
| depth_multiple: 0.33 | |
| head: | |
| - - -1 | |
| - 1 | |
| - nn.Upsample | |
| - - None | |
| - 2 | |
| - nearest | |
| - - - -1 | |
| - 6 | |
| - 1 | |
| - Concat | |
| - - 1 | |
| - - -1 | |
| - 3 | |
| - C2f | |
| - - 512 | |
| - - -1 | |
| - 1 | |
| - nn.Upsample | |
| - - None | |
| - 2 | |
| - nearest | |
| - - - -1 | |
| - 4 | |
| - 1 | |
| - Concat | |
| - - 1 | |
| - - -1 | |
| - 3 | |
| - C2f | |
| - - 256 | |
| - - -1 | |
| - 1 | |
| - Conv | |
| - - 256 | |
| - 3 | |
| - 2 | |
| - - - -1 | |
| - 12 | |
| - 1 | |
| - Concat | |
| - - 1 | |
| - - -1 | |
| - 3 | |
| - C2f | |
| - - 512 | |
| - - -1 | |
| - 1 | |
| - Conv | |
| - - 512 | |
| - 3 | |
| - 2 | |
| - - - -1 | |
| - 9 | |
| - 1 | |
| - Concat | |
| - - 1 | |
| - - -1 | |
| - 3 | |
| - C2f | |
| - - 1024 | |
| - - - 15 | |
| - 18 | |
| - 21 | |
| - 1 | |
| - Detect | |
| - - nc | |
| nc: 1 | |
| width_multiple: 0.25 | |