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 / checkpoint-50 /tokenizer_config.json

ajmaclin

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	{
	"added_tokens_decoder": {
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	"content": "<s>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false,
	"special": true
	},
	"1": {
	"content": "<pad>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false,
	"special": true
	},
	"2": {
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	"3": {
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	"content": "<sep/>",
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	"57525": {
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	"content": "</drug_name>",
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	"content": "<dosage>",
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	"content": "</dosage>",
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	"content": "</frequency>",
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	"57533": {
	"content": "<doctor_name>",
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	"57534": {
	"content": "</doctor_name>",
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	"57535": {
	"content": "<prc_number>",
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	"57536": {
	"content": "</prc_number>",
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	"57537": {
	"content": "<patient_name>",
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	},
	"57538": {
	"content": "</patient_name>",
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	},
	"57539": {
	"content": "<date>",
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	},
	"57540": {
	"content": "</date>",
	"lstrip": false,
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	},
	"57541": {
	"content": "<clinic_address>",
	"lstrip": false,
	"normalized": false,
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	"special": true
	},
	"57542": {
	"content": "</clinic_address>",
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	},
	"57543": {
	"content": "<rx_block>",
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	"57544": {
	"content": "</rx_block>",
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	},
	"57545": {
	"content": "<prescription>",
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	},
	"57546": {
	"content": "</prescription>",
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	"57547": {
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	}
	},
	"additional_special_tokens": [
	"<drug_name>",
	"</drug_name>",
	"<dosage>",
	"</dosage>",
	"<frequency>",
	"</frequency>",
	"<duration>",
	"</duration>",
	"<doctor_name>",
	"</doctor_name>",
	"<prc_number>",
	"</prc_number>",
	"<patient_name>",
	"</patient_name>",
	"<date>",
	"</date>",
	"<clinic_address>",
	"</clinic_address>",
	"<rx_block>",
	"</rx_block>",
	"<prescription>",
	"</prescription>",
	"<sep/>",
	"<s_answer>"
	],
	"bos_token": "<s>",
	"clean_up_tokenization_spaces": true,
	"cls_token": "<s>",
	"eos_token": "</s>",
	"mask_token": "<mask>",
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	"pad_token": "<pad>",
	"processor_class": "DonutProcessor",
	"sep_token": "</s>",
	"sp_model_kwargs": {},
	"tokenizer_class": "XLMRobertaTokenizer",
	"unk_token": "<unk>"
	}