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zipbomb
/
donut-base-rr

Image-Text-to-Text
Transformers
PyTorch
TensorBoard
vision-encoder-decoder
Generated from Trainer
Model card Files Files and versions
xet
Metrics Training metrics Community
1

Instructions to use zipbomb/donut-base-rr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use zipbomb/donut-base-rr with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("image-text-to-text", model="zipbomb/donut-base-rr")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForMultimodalLM
    
    tokenizer = AutoTokenizer.from_pretrained("zipbomb/donut-base-rr")
    model = AutoModelForMultimodalLM.from_pretrained("zipbomb/donut-base-rr", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps Settings
  • vLLM

    How to use zipbomb/donut-base-rr with vLLM:

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

    How to use zipbomb/donut-base-rr 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 "zipbomb/donut-base-rr" \
        --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": "zipbomb/donut-base-rr",
    		"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 "zipbomb/donut-base-rr" \
            --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": "zipbomb/donut-base-rr",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use zipbomb/donut-base-rr with Docker Model Runner:

    docker model run hf.co/zipbomb/donut-base-rr
donut-base-rr / runs
97.6 kB
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  • 1 contributor
History: 25 commits
zipbomb's picture
zipbomb
End of training
8c64b45 over 3 years ago
  • Feb03_23-39-02_df67044e0734
    End of training over 3 years ago
  • Jan09_20-14-15_6ca51f925ac4
    End of training over 3 years ago
  • Jan09_20-14-45_6ca51f925ac4
    End of training over 3 years ago
  • Jan09_20-15-24_6ca51f925ac4
    End of training over 3 years ago
  • Jan09_21-54-15_e20588376481
    End of training over 3 years ago
  • Jan09_21-55-20_e20588376481
    End of training over 3 years ago