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Humayoun
/
ThesisDonut

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

Instructions to use Humayoun/ThesisDonut with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use Humayoun/ThesisDonut with Transformers:

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

    How to use Humayoun/ThesisDonut with vLLM:

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

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

    How to use Humayoun/ThesisDonut with Docker Model Runner:

    docker model run hf.co/Humayoun/ThesisDonut
ThesisDonut
815 MB
Ctrl+K
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  • 1 contributor
History: 12 commits
Humayoun's picture
Humayoun
End of training
c02dd17 about 3 years ago
  • runs
    End of training about 3 years ago
  • .gitattributes
    1.48 kB
    initial commit about 3 years ago
  • .gitignore
    13 Bytes
    Training in progress, epoch 1 about 3 years ago
  • README.md
    1.1 kB
    End of training about 3 years ago
  • added_tokens.json
    889 Bytes
    End of training about 3 years ago
  • config.json
    5.03 kB
    Training in progress, epoch 1 about 3 years ago
  • generation_config.json
    165 Bytes
    End of training about 3 years ago
  • preprocessor_config.json
    420 Bytes
    End of training about 3 years ago
  • pytorch_model.bin
    809 MB
    xet
    Training in progress, epoch 10 about 3 years ago
  • sentencepiece.bpe.model
    1.3 MB
    xet
    End of training about 3 years ago
  • special_tokens_map.json
    997 Bytes
    End of training about 3 years ago
  • tokenizer.json
    4.02 MB
    End of training about 3 years ago
  • tokenizer_config.json
    510 Bytes
    End of training about 3 years ago
  • training_args.bin
    4.09 kB
    xet
    Training in progress, epoch 1 about 3 years ago