Instructions to use TomasFAV/DonutInvoiceCzechV012 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TomasFAV/DonutInvoiceCzechV012 with Transformers:

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

pipe = pipeline("image-text-to-text", model="TomasFAV/DonutInvoiceCzechV012")

# Load model directly
from transformers import AutoTokenizer, AutoModelForImageTextToText

tokenizer = AutoTokenizer.from_pretrained("TomasFAV/DonutInvoiceCzechV012")
model = AutoModelForImageTextToText.from_pretrained("TomasFAV/DonutInvoiceCzechV012")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use TomasFAV/DonutInvoiceCzechV012 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TomasFAV/DonutInvoiceCzechV012"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TomasFAV/DonutInvoiceCzechV012",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/TomasFAV/DonutInvoiceCzechV012

SGLang

How to use TomasFAV/DonutInvoiceCzechV012 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 "TomasFAV/DonutInvoiceCzechV012" \
    --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": "TomasFAV/DonutInvoiceCzechV012",
		"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 "TomasFAV/DonutInvoiceCzechV012" \
        --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": "TomasFAV/DonutInvoiceCzechV012",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use TomasFAV/DonutInvoiceCzechV012 with Docker Model Runner:
```
docker model run hf.co/TomasFAV/DonutInvoiceCzechV012
```

DonutInvoiceCzechV012

This model is a fine-tuned version of TomasFAV/DonutInvoiceCzechV01 on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.3897
Accuracy: 0.8944
F1: 0.8792

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 9e-05
train_batch_size: 4
eval_batch_size: 1
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 10
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
0.1209	1.0	230	0.3804	0.8237	0.8296
0.0876	2.0	460	0.4660	0.8350	0.8330
0.0427	3.0	690	0.4718	0.8289	0.8407
0.0236	4.0	920	0.3743	0.8712	0.8701
0.0127	5.0	1150	0.4255	0.8424	0.8405
0.0115	6.0	1380	0.3897	0.8944	0.8792
0.0075	7.0	1610	0.4155	0.8803	0.8711
0.0062	8.0	1840	0.4623	0.8722	0.8650
0.0021	9.0	2070	0.4560	0.8808	0.8702
0.0033	10.0	2300	0.4488	0.8784	0.8671