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Akajackson
/
donut_rus

Image-Text-to-Text
Transformers
PyTorch
Russian
English
vision-encoder-decoder
Model card Files Files and versions
xet
Community
4

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

  • Libraries
  • Transformers

    How to use Akajackson/donut_rus with Transformers:

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

    How to use Akajackson/donut_rus with vLLM:

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

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

    How to use Akajackson/donut_rus with Docker Model Runner:

    docker model run hf.co/Akajackson/donut_rus
donut_rus
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  • 1 contributor
History: 118 commits
Akajackson's picture
Akajackson
Update README.md
18eaf71 about 3 years ago
  • .gitattributes
    1.52 kB
    Training in progress, epoch 0 about 3 years ago
  • README.md
    1.59 kB
    Update README.md about 3 years ago
  • added_tokens.json
    50 Bytes
    Training in progress, epoch 0 about 3 years ago
  • config.json
    5.02 kB
    Training in progress, epoch 0 about 3 years ago
  • generation_config.json
    212 Bytes
    Training in progress, epoch 0 about 3 years ago
  • preprocessor_config.json
    422 Bytes
    Training in progress, epoch 0 about 3 years ago
  • pytorch_model.bin

    Detected Pickle imports (4)

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

    What is a pickle import?

    717 MB
    xet
    Training in progress, epoch 2 about 3 years ago
  • sentencepiece.bpe.model
    944 kB
    xet
    Training in progress, epoch 0 about 3 years ago
  • source.spm
    1.08 MB
    xet
    Training in progress, epoch 0 about 3 years ago
  • special_tokens_map.json
    279 Bytes
    Training in progress, epoch 0 about 3 years ago
  • target.spm
    803 kB
    Training in progress, epoch 0 about 3 years ago
  • tokenizer.json
    2.63 MB
    Training in progress, epoch 0 about 3 years ago
  • tokenizer_config.json
    676 Bytes
    Training in progress, epoch 0 about 3 years ago
  • vocab.json
    2.73 MB
    Training in progress, epoch 0 about 3 years ago