Instructions to use TomasFAV/DonutInvoiceCzechV0123 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TomasFAV/DonutInvoiceCzechV0123 with Transformers:

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

pipe = pipeline("image-text-to-text", model="TomasFAV/DonutInvoiceCzechV0123")

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

tokenizer = AutoTokenizer.from_pretrained("TomasFAV/DonutInvoiceCzechV0123")
model = AutoModelForMultimodalLM.from_pretrained("TomasFAV/DonutInvoiceCzechV0123", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use TomasFAV/DonutInvoiceCzechV0123 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TomasFAV/DonutInvoiceCzechV0123"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TomasFAV/DonutInvoiceCzechV0123",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/TomasFAV/DonutInvoiceCzechV0123

SGLang

How to use TomasFAV/DonutInvoiceCzechV0123 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/DonutInvoiceCzechV0123" \
    --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/DonutInvoiceCzechV0123",
		"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/DonutInvoiceCzechV0123" \
        --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/DonutInvoiceCzechV0123",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use TomasFAV/DonutInvoiceCzechV0123 with Docker Model Runner:
```
docker model run hf.co/TomasFAV/DonutInvoiceCzechV0123
```

TomasFAV commited on Apr 19

Commit

e58a742

verified ·

1 Parent(s): 9b684cf

Model save

Browse files

Files changed (3) hide show

README.md +81 -0
model.safetensors +1 -1
runs/Apr19_00-01-17_f793f275c12c/events.out.tfevents.1776558120.f793f275c12c.14328.1 +3 -0

README.md ADDED Viewed

	@@ -0,0 +1,81 @@

+---
+library_name: transformers
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- f1
+model-index:
+- name: DonutInvoiceCzechV0123RR
+  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. -->
+# DonutInvoiceCzechV0123RR
+This model was trained from scratch on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1672
+- Accuracy: 0.9460
+- F1: 0.9214
+## 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     |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
+| 0.2086        | 1.0   | 46   | 0.1679          | 0.9141   | 0.8816 |
+| 0.1011        | 2.0   | 92   | 0.1561          | 0.9371   | 0.9128 |
+| 0.0632        | 3.0   | 138  | 0.1630          | 0.9386   | 0.9111 |
+| 0.0376        | 4.0   | 184  | 0.1921          | 0.8951   | 0.8927 |
+| 0.0286        | 5.0   | 230  | 0.1798          | 0.9291   | 0.9094 |
+| 0.0214        | 6.0   | 276  | 0.1672          | 0.9460   | 0.9214 |
+| 0.0159        | 7.0   | 322  | 0.1734          | 0.9432   | 0.9214 |
+| 0.0223        | 8.0   | 368  | 0.1815          | 0.9352   | 0.9074 |
+| 0.0071        | 9.0   | 414  | 0.1857          | 0.9304   | 0.9012 |
+| 0.0175        | 10.0  | 460  | 0.1976          | 0.9288   | 0.9088 |
+| 0.0073        | 11.0  | 506  | 0.1983          | 0.9439   | 0.9111 |
+| 0.0034        | 12.0  | 552  | 0.2064          | 0.9318   | 0.9090 |
+| 0.0077        | 13.0  | 598  | 0.2131          | 0.9359   | 0.9072 |
+| 0.0050        | 14.0  | 644  | 0.2108          | 0.9369   | 0.9072 |
+| 0.0007        | 15.0  | 690  | 0.2074          | 0.9340   | 0.9072 |
+| 0.0006        | 16.0  | 736  | 0.2084          | 0.9357   | 0.9106 |
+| 0.0011        | 17.0  | 782  | 0.2075          | 0.9363   | 0.9128 |
+| 0.0008        | 18.0  | 828  | 0.2030          | 0.9384   | 0.9192 |
+| 0.0009        | 19.0  | 874  | 0.2025          | 0.9353   | 0.9192 |
+| 0.0006        | 20.0  | 920  | 0.2027          | 0.9338   | 0.9175 |
+### Framework versions
+- Transformers 5.0.0
+- Pytorch 2.10.0+cu128
+- Datasets 4.0.0
+- Tokenizers 0.22.2

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