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

File size: 641 Bytes

9eb10cb

{
  "</clinic_address>": 57542,
  "</date>": 57540,
  "</doctor_name>": 57534,
  "</dosage>": 57528,
  "</drug_name>": 57526,
  "</duration>": 57532,
  "</frequency>": 57530,
  "</patient_name>": 57538,
  "</prc_number>": 57536,
  "</prescription>": 57546,
  "</rx_block>": 57544,
  "<clinic_address>": 57541,
  "<date>": 57539,
  "<doctor_name>": 57533,
  "<dosage>": 57527,
  "<drug_name>": 57525,
  "<duration>": 57531,
  "<frequency>": 57529,
  "<patient_name>": 57537,
  "<prc_number>": 57535,
  "<prescription>": 57545,
  "<rx_block>": 57543,
  "<s_answer>": 57547,
  "<s_iitcdip>": 57523,
  "<s_synthdog>": 57524,
  "<sep/>": 57522
}