Instructions to use TomasFAV/Pix2StructCzechInvoiceV01 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TomasFAV/Pix2StructCzechInvoiceV01 with Transformers:

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

pipe = pipeline("image-text-to-text", model="TomasFAV/Pix2StructCzechInvoiceV01")

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("TomasFAV/Pix2StructCzechInvoiceV01")
model = AutoModelForImageTextToText.from_pretrained("TomasFAV/Pix2StructCzechInvoiceV01")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use TomasFAV/Pix2StructCzechInvoiceV01 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TomasFAV/Pix2StructCzechInvoiceV01"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TomasFAV/Pix2StructCzechInvoiceV01",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/TomasFAV/Pix2StructCzechInvoiceV01

SGLang

How to use TomasFAV/Pix2StructCzechInvoiceV01 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/Pix2StructCzechInvoiceV01" \
    --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/Pix2StructCzechInvoiceV01",
		"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/Pix2StructCzechInvoiceV01" \
        --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/Pix2StructCzechInvoiceV01",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use TomasFAV/Pix2StructCzechInvoiceV01 with Docker Model Runner:
```
docker model run hf.co/TomasFAV/Pix2StructCzechInvoiceV01
```

TomasFAV commited on Mar 15

Commit

fbf076b

verified ·

1 Parent(s): 1c2056c

Model save

Browse files

Files changed (3) hide show

README.md +72 -3
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runs/Mar14_23-53-02_77b291cf41b2/events.out.tfevents.1773534537.77b291cf41b2.903.1 +3 -0

README.md CHANGED Viewed

@@ -1,3 +1,72 @@
----
-license: mit
----

+---
+library_name: transformers
+license: apache-2.0
+base_model: TomasFAV/Pix2StructCzechInvoice
+tags:
+- generated_from_trainer
+metrics:
+- f1
+model-index:
+- name: Pix2StructCzechInvoiceV1
+  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. -->
+# Pix2StructCzechInvoiceV1
+This model is a fine-tuned version of [TomasFAV/Pix2StructCzechInvoice](https://huggingface.co/TomasFAV/Pix2StructCzechInvoice) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.4679
+- F1: 0.6432
+## 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: 0.0001
+- train_batch_size: 8
+- 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: cosine_with_restarts
+- lr_scheduler_warmup_steps: 0.1
+- num_epochs: 10
+- mixed_precision_training: Native AMP
+### Training results
+| Training Loss | Epoch | Step | Validation Loss | F1     |
+|:-------------:|:-----:|:----:|:---------------:|:------:|
+| 0.1978        | 1.0   | 75   | 0.3757          | 0.5804 |
+| 0.1031        | 2.0   | 150  | 0.3578          | 0.6399 |
+| 0.0725        | 3.0   | 225  | 0.3504          | 0.6318 |
+| 0.0512        | 4.0   | 300  | 0.3929          | 0.6396 |
+| 0.0500        | 5.0   | 375  | 0.4072          | 0.6394 |
+| 0.0462        | 6.0   | 450  | 0.4655          | 0.4377 |
+| 0.0502        | 7.0   | 525  | 0.6320          | 0.3384 |
+| 0.0528        | 8.0   | 600  | 0.4835          | 0.5018 |
+| 0.0393        | 9.0   | 675  | 0.4679          | 0.6432 |
+| 0.0392        | 10.0  | 750  | 0.5330          | 0.4931 |
+### Framework versions
+- Transformers 5.0.0
+- Pytorch 2.10.0+cu128
+- Datasets 4.0.0
+- Tokenizers 0.22.2

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