Instructions to use shivalikasingh/donut-cheque-parser with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use shivalikasingh/donut-cheque-parser with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="shivalikasingh/donut-cheque-parser")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("shivalikasingh/donut-cheque-parser")
model = AutoModelForMultimodalLM.from_pretrained("shivalikasingh/donut-cheque-parser", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use shivalikasingh/donut-cheque-parser with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "shivalikasingh/donut-cheque-parser"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "shivalikasingh/donut-cheque-parser",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/shivalikasingh/donut-cheque-parser

SGLang

How to use shivalikasingh/donut-cheque-parser 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 "shivalikasingh/donut-cheque-parser" \
    --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": "shivalikasingh/donut-cheque-parser",
		"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 "shivalikasingh/donut-cheque-parser" \
        --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": "shivalikasingh/donut-cheque-parser",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use shivalikasingh/donut-cheque-parser with Docker Model Runner:
```
docker model run hf.co/shivalikasingh/donut-cheque-parser
```

donut-cheque-parser / added_tokens.json

shivi

Training complete!Pushing Processor to Hub

396e1b5 almost 4 years ago

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448 Bytes

	{
	"</s_amt_in_figures>": 57530,
	"</s_amt_in_words>": 57528,
	"</s_bank_name>": 57534,
	"</s_cheque_date>": 57536,
	"</s_cheque_details>": 57526,
	"</s_payee_name>": 57532,
	"<parse-cheque>": 57537,
	"<s_amt_in_figures>": 57529,
	"<s_amt_in_words>": 57527,
	"<s_bank_name>": 57533,
	"<s_cheque_date>": 57535,
	"<s_cheque_details>": 57525,
	"<s_iitcdip>": 57523,
	"<s_payee_name>": 57531,
	"<s_synthdog>": 57524,
	"<sep/>": 57522
	}