Instructions to use ajmaclin/prescription-donut-ocr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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
```

prescription-donut-ocr / README.md

ajmaclin

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metadata

license: apache-2.0
tags:
  - document-understanding
  - ocr
  - prescription
  - medical-ocr
  - donut
library_name: transformers

Prescription OCR Reader — Donut

Stage 2 ng YOLO+Donut prescription OCR pipeline.

Usage

from transformers import DonutProcessor, VisionEncoderDecoderModel
from huggingface_hub import hf_hub_download
from PIL import Image

processor = DonutProcessor.from_pretrained("ajmaclin/prescription-donut-ocr")
model     = VisionEncoderDecoderModel.from_pretrained("ajmaclin/prescription-donut-ocr")

image        = Image.open("prescription_crop.jpg")
pixel_values = processor(image, return_tensors="pt").pixel_values
outputs      = model.generate(pixel_values, max_length=256)
result       = processor.batch_decode(outputs, skip_special_tokens=True)[0]
print(result)