Image-Text-to-Text
Transformers
Safetensors
vision-encoder-decoder
document-understanding
ocr
prescription
medical-ocr
donut
Instructions to use ajmaclin/prescription-donut-ocr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ajmaclin/prescription-donut-ocr with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="ajmaclin/prescription-donut-ocr")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("ajmaclin/prescription-donut-ocr") model = AutoModelForMultimodalLM.from_pretrained("ajmaclin/prescription-donut-ocr", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use ajmaclin/prescription-donut-ocr with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ajmaclin/prescription-donut-ocr" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ajmaclin/prescription-donut-ocr", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/ajmaclin/prescription-donut-ocr
- SGLang
How to use ajmaclin/prescription-donut-ocr 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 "ajmaclin/prescription-donut-ocr" \ --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": "ajmaclin/prescription-donut-ocr", "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 "ajmaclin/prescription-donut-ocr" \ --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": "ajmaclin/prescription-donut-ocr", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use ajmaclin/prescription-donut-ocr with Docker Model Runner:
docker model run hf.co/ajmaclin/prescription-donut-ocr
- Xet hash:
- 09905a773b656e748aa51c47682fcdaf19b73e554d9b676cbed6a1f08b2abb1f
- Size of remote file:
- 5.78 kB
- SHA256:
- f4fe1f691ba98e94f057a673bbc80ae49d6d6e4720cd7d58d19318f5db6afbe5
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