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nbroad
/
donut-base-ascii

Image-Text-to-Text
Transformers
PyTorch
vision-encoder-decoder
Model card Files Files and versions
xet
Community
1

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

  • Libraries
  • Transformers

    How to use nbroad/donut-base-ascii with Transformers:

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

    How to use nbroad/donut-base-ascii with vLLM:

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

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

    How to use nbroad/donut-base-ascii with Docker Model Runner:

    docker model run hf.co/nbroad/donut-base-ascii
donut-base-ascii
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  • 1 contributor
History: 8 commits
nbroad's picture
nbroad
Update README.md
39129e8 almost 3 years ago
  • .gitattributes
    1.52 kB
    initial commit almost 3 years ago
  • README.md
    1.08 kB
    Update README.md almost 3 years ago
  • added_tokens.json
    52 Bytes
    add additional special tokens, embeddings multiple of 16 almost 3 years ago
  • config.json
    4.97 kB
    add additional special tokens, embeddings multiple of 16 almost 3 years ago
  • generation_config.json
    160 Bytes
    first almost 3 years ago
  • preprocessor_config.json
    440 Bytes
    first almost 3 years ago
  • pytorch_model.bin

    Detected Pickle imports (4)

    • "collections.OrderedDict",
    • "torch._utils._rebuild_tensor_v2",
    • "torch.LongStorage",
    • "torch.FloatStorage"

    What is a pickle import?

    686 MB
    xet
    add additional special tokens, embeddings multiple of 16 almost 3 years ago
  • remove-donut-tokens.ipynb
    1.73 MB
    Upload remove-donut-tokens.ipynb almost 3 years ago
  • run_speed_tests.sh
    206 Bytes
    Upload 2 files almost 3 years ago
  • sentencepiece.bpe.model
    719 kB
    xet
    first almost 3 years ago
  • special_tokens_map.json
    243 Bytes
    add additional special tokens, embeddings multiple of 16 almost 3 years ago
  • speed_test.py
    4.4 kB
    Upload 2 files almost 3 years ago
  • tokenizer_config.json
    471 Bytes
    first almost 3 years ago