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, AutoModelForVisualQuestionAnswering processor = AutoProcessor.from_pretrained("microsoft/OmniParser") model = AutoModelForVisualQuestionAnswering.from_pretrained("microsoft/OmniParser") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- 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
| { | |
| "_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 | |
| } | |
| } | |