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
```

Pix2StructCzechInvoiceV01

File size: 1,077 Bytes

82fa3e1

{
  "backend": "tokenizers",
  "eos_token": "</s>",
  "extra_ids": 100,
  "extra_special_tokens": [
    "</s_due_date>",
    "</s_issue_date>",
    "<s_invoice_number>",
    "</s_invoice_number>",
    "</s_cust_tax_id>",
    "<s_taxable_supply_date>",
    "<s_supp_tax_id>",
    "<s_bank_account_number>",
    "</s_total>",
    "<s_iban>",
    "</s_supp_tax_id>",
    "<s_const_symbol>",
    "</s_payment_type>",
    "</s_variable_symbol>",
    "<s_payment_type>",
    "<s_variable_symbol>",
    "</s_iban>",
    "<s_total>",
    "</s_const_symbol>",
    "</s_taxable_supply_date>",
    "<s_cust_register_id>",
    "</s_cust_register_id>",
    "<s_issue_date>",
    "</s_bank_account_number>",
    "<s_cust_tax_id>",
    "</s_bic>",
    "</s_supp_register_id>",
    "<s_supp_register_id>",
    "<s_due_date>",
    "<s_bic>"
  ],
  "is_local": false,
  "is_vqa": true,
  "model_max_length": 1000000000000000019884624838656,
  "pad_token": "<pad>",
  "processor_class": "Pix2StructProcessor",
  "sp_model_kwargs": {},
  "tokenizer_class": "T5Tokenizer",
  "unk_token": "<unk>"
}