Instructions to use tedad09/PolizzeDonut-ConDOC-3Epochs-40img with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tedad09/PolizzeDonut-ConDOC-3Epochs-40img with Transformers:

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

pipe = pipeline("image-text-to-text", model="tedad09/PolizzeDonut-ConDOC-3Epochs-40img")

# Load model directly
from transformers import AutoTokenizer, AutoModelForImageTextToText

tokenizer = AutoTokenizer.from_pretrained("tedad09/PolizzeDonut-ConDOC-3Epochs-40img")
model = AutoModelForImageTextToText.from_pretrained("tedad09/PolizzeDonut-ConDOC-3Epochs-40img")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use tedad09/PolizzeDonut-ConDOC-3Epochs-40img with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tedad09/PolizzeDonut-ConDOC-3Epochs-40img"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tedad09/PolizzeDonut-ConDOC-3Epochs-40img",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/tedad09/PolizzeDonut-ConDOC-3Epochs-40img

SGLang

How to use tedad09/PolizzeDonut-ConDOC-3Epochs-40img 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 "tedad09/PolizzeDonut-ConDOC-3Epochs-40img" \
    --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": "tedad09/PolizzeDonut-ConDOC-3Epochs-40img",
		"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 "tedad09/PolizzeDonut-ConDOC-3Epochs-40img" \
        --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": "tedad09/PolizzeDonut-ConDOC-3Epochs-40img",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use tedad09/PolizzeDonut-ConDOC-3Epochs-40img with Docker Model Runner:
```
docker model run hf.co/tedad09/PolizzeDonut-ConDOC-3Epochs-40img
```

PolizzeDonut-ConDOC-3Epochs-40img / preprocessor_config.json

tedad09

PolizzeDonut-conDOC-3Epochs-40img

033452e verified about 2 years ago

raw

history blame contribute delete

421 Bytes

	{
	"do_align_long_axis": false,
	"do_normalize": true,
	"do_pad": true,
	"do_rescale": true,
	"do_resize": true,
	"do_thumbnail": true,
	"image_mean": [
	0.5,
	0.5,
	0.5
	],
	"image_processor_type": "DonutImageProcessor",
	"image_std": [
	0.5,
	0.5,
	0.5
	],
	"processor_class": "DonutProcessor",
	"resample": 2,
	"rescale_factor": 0.00392156862745098,
	"size": [
	780,
	1050
	]
	}