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
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94dace7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 | {
"_name_or_path": "Salesforce/blip2-opt-2.7b",
"architectures": [
"Blip2ForConditionalGeneration"
],
"initializer_factor": 1.0,
"initializer_range": 0.02,
"model_type": "blip-2",
"num_query_tokens": 32,
"qformer_config": {
"classifier_dropout": null,
"model_type": "blip_2_qformer"
},
"text_config": {
"_name_or_path": "facebook/opt-2.7b",
"activation_dropout": 0.0,
"architectures": [
"OPTForCausalLM"
],
"eos_token_id": 50118,
"ffn_dim": 10240,
"hidden_size": 2560,
"model_type": "opt",
"num_attention_heads": 32,
"num_hidden_layers": 32,
"prefix": "</s>",
"torch_dtype": "float16",
"word_embed_proj_dim": 2560
},
"torch_dtype": "bfloat16",
"transformers_version": "4.40.2",
"use_decoder_only_language_model": true,
"vision_config": {
"dropout": 0.0,
"initializer_factor": 1.0,
"model_type": "blip_2_vision_model",
"num_channels": 3,
"projection_dim": 512
}
}
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