Instructions to use TomasFAV/DonutInvoiceCzechV03 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TomasFAV/DonutInvoiceCzechV03 with Transformers:

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

pipe = pipeline("image-text-to-text", model="TomasFAV/DonutInvoiceCzechV03")

# Load model directly
from transformers import AutoTokenizer, AutoModelForImageTextToText

tokenizer = AutoTokenizer.from_pretrained("TomasFAV/DonutInvoiceCzechV03")
model = AutoModelForImageTextToText.from_pretrained("TomasFAV/DonutInvoiceCzechV03")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use TomasFAV/DonutInvoiceCzechV03 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TomasFAV/DonutInvoiceCzechV03"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TomasFAV/DonutInvoiceCzechV03",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/TomasFAV/DonutInvoiceCzechV03

SGLang

How to use TomasFAV/DonutInvoiceCzechV03 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/DonutInvoiceCzechV03" \
    --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/DonutInvoiceCzechV03",
		"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/DonutInvoiceCzechV03" \
        --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/DonutInvoiceCzechV03",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use TomasFAV/DonutInvoiceCzechV03 with Docker Model Runner:
```
docker model run hf.co/TomasFAV/DonutInvoiceCzechV03
```

DonutInvoiceCzechV03 / README.md

TomasFAV

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	---
	library_name: transformers
	license: mit
	base_model: TomasFAV/DonutInvoiceCzechV0R
	tags:
	- generated_from_trainer
	metrics:
	- accuracy
	- f1
	model-index:
	- name: DonutInvoiceCzechV03R
	results: []
	---

	<!-- This model card has been generated automatically according to the information the Trainer had access to. You
	should probably proofread and complete it, then remove this comment. -->

	# DonutInvoiceCzechV03

	This model is a fine-tuned version of [TomasFAV/DonutInvoiceCzechV0](https://huggingface.co/TomasFAV/DonutInvoiceCzechV0) on an unknown dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.2443
	- Accuracy: 0.9274
	- F1: 0.9077

	## 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: 20
	- mixed_precision_training: Native AMP

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \|
	\|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|
	\| 0.2874 \| 1.0 \| 46 \| 0.1856 \| 0.9007 \| 0.8788 \|
	\| 0.1328 \| 2.0 \| 92 \| 0.2057 \| 0.8800 \| 0.8535 \|
	\| 0.0790 \| 3.0 \| 138 \| 0.1899 \| 0.8992 \| 0.8921 \|
	\| 0.0493 \| 4.0 \| 184 \| 0.2266 \| 0.9103 \| 0.8912 \|
	\| 0.0391 \| 5.0 \| 230 \| 0.2266 \| 0.8962 \| 0.8739 \|
	\| 0.0271 \| 6.0 \| 276 \| 0.2532 \| 0.8840 \| 0.8658 \|
	\| 0.0238 \| 7.0 \| 322 \| 0.2393 \| 0.9016 \| 0.8803 \|
	\| 0.0211 \| 8.0 \| 368 \| 0.2429 \| 0.9090 \| 0.8846 \|
	\| 0.0210 \| 9.0 \| 414 \| 0.2326 \| 0.9266 \| 0.8889 \|
	\| 0.0184 \| 10.0 \| 460 \| 0.2241 \| 0.9216 \| 0.9026 \|
	\| 0.0109 \| 11.0 \| 506 \| 0.2483 \| 0.9075 \| 0.8933 \|
	\| 0.0037 \| 12.0 \| 552 \| 0.2443 \| 0.9274 \| 0.9077 \|
	\| 0.0023 \| 13.0 \| 598 \| 0.2457 \| 0.9269 \| 0.8991 \|
	\| 0.0057 \| 14.0 \| 644 \| 0.2397 \| 0.9278 \| 0.9026 \|
	\| 0.0024 \| 15.0 \| 690 \| 0.2320 \| 0.9346 \| 0.9077 \|
	\| 0.0008 \| 16.0 \| 736 \| 0.2390 \| 0.9344 \| 0.9077 \|
	\| 0.0015 \| 17.0 \| 782 \| 0.2401 \| 0.9350 \| 0.9077 \|
	\| 0.0042 \| 18.0 \| 828 \| 0.2405 \| 0.9346 \| 0.9077 \|
	\| 0.0016 \| 19.0 \| 874 \| 0.2426 \| 0.9322 \| 0.9060 \|
	\| 0.0035 \| 20.0 \| 920 \| 0.2426 \| 0.9322 \| 0.9060 \|


	### Framework versions

	- Transformers 5.0.0
	- Pytorch 2.10.0+cu128
	- Datasets 4.0.0
	- Tokenizers 0.22.2