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microsoft
/
udop-large

Image-Text-to-Text
Transformers
Safetensors
udop
vision
Model card Files Files and versions
xet
Community
10

Instructions to use microsoft/udop-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use microsoft/udop-large with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("image-text-to-text", model="microsoft/udop-large")
    # Load model directly
    from transformers import AutoProcessor, AutoModelForImageTextToText
    
    processor = AutoProcessor.from_pretrained("microsoft/udop-large")
    model = AutoModelForImageTextToText.from_pretrained("microsoft/udop-large")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • vLLM

    How to use microsoft/udop-large with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "microsoft/udop-large"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "microsoft/udop-large",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/microsoft/udop-large
  • SGLang

    How to use microsoft/udop-large 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/udop-large" \
        --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/udop-large",
    		"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/udop-large" \
            --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/udop-large",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use microsoft/udop-large with Docker Model Runner:

    docker model run hf.co/microsoft/udop-large
udop-large
2.97 GB
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  • 3 contributors
History: 13 commits
tnaumann's picture
tnaumann
bsnelling's picture
bsnelling
Update README.md (#10)
2d5f5b1 verified 5 months ago
  • .gitattributes
    1.52 kB
    initial commit about 2 years ago
  • README.md
    2.43 kB
    Update README.md (#10) 5 months ago
  • added_tokens.json
    29.6 kB
    Upload processor about 2 years ago
  • config.json
    907 Bytes
    Upload UdopForConditionalGeneration about 2 years ago
  • data_summary_card.md
    4.45 kB
    Upload data_summary_card.md (#9) 5 months ago
  • generation_config.json
    147 Bytes
    Upload UdopForConditionalGeneration about 2 years ago
  • model.safetensors
    2.97 GB
    xet
    Upload UdopForConditionalGeneration about 2 years ago
  • preprocessor_config.json
    759 Bytes
    Upload processor about 2 years ago
  • special_tokens_map.json
    24.1 kB
    Upload processor about 2 years ago
  • spiece.model
    792 kB
    xet
    Upload processor about 2 years ago
  • tokenizer_config.json
    238 kB
    Upload processor about 2 years ago