Image-Text-to-Text
Transformers
TensorBoard
Safetensors
pix2struct
Generated from Trainer
invoice-processing
information-extraction
czech-language
document-ai
multimodal-model
generative-model
synthetic-data
hybrid-data
Instructions to use TomasFAV/Pix2StructCzechInvoiceV012 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TomasFAV/Pix2StructCzechInvoiceV012 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="TomasFAV/Pix2StructCzechInvoiceV012")# Load model directly from transformers import AutoProcessor, AutoModelForImageTextToText processor = AutoProcessor.from_pretrained("TomasFAV/Pix2StructCzechInvoiceV012") model = AutoModelForImageTextToText.from_pretrained("TomasFAV/Pix2StructCzechInvoiceV012") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use TomasFAV/Pix2StructCzechInvoiceV012 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "TomasFAV/Pix2StructCzechInvoiceV012" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "TomasFAV/Pix2StructCzechInvoiceV012", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/TomasFAV/Pix2StructCzechInvoiceV012
- SGLang
How to use TomasFAV/Pix2StructCzechInvoiceV012 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/Pix2StructCzechInvoiceV012" \ --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/Pix2StructCzechInvoiceV012", "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/Pix2StructCzechInvoiceV012" \ --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/Pix2StructCzechInvoiceV012", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use TomasFAV/Pix2StructCzechInvoiceV012 with Docker Model Runner:
docker model run hf.co/TomasFAV/Pix2StructCzechInvoiceV012
| { | |
| "backend": "tokenizers", | |
| "eos_token": "</s>", | |
| "extra_ids": 100, | |
| "extra_special_tokens": [ | |
| "</s_const_symbol>", | |
| "</s_issue_date>", | |
| "</s_bic>", | |
| "<s_iban>", | |
| "<s_supp_register_id>", | |
| "</s_cust_tax_id>", | |
| "</s_payment_type>", | |
| "<s_variable_symbol>", | |
| "<s_taxable_supply_date>", | |
| "</s_due_date>", | |
| "</s_bank_account_number>", | |
| "<s_const_symbol>", | |
| "</s_taxable_supply_date>", | |
| "<s_bic>", | |
| "<s_issue_date>", | |
| "<s_supp_tax_id>", | |
| "</s_iban>", | |
| "<s_bank_account_number>", | |
| "</s_cust_register_id>", | |
| "<s_due_date>", | |
| "</s_variable_symbol>", | |
| "</s_invoice_number>", | |
| "<s_total>", | |
| "</s_supp_register_id>", | |
| "<s_cust_tax_id>", | |
| "<s_payment_type>", | |
| "</s_total>", | |
| "<s_invoice_number>", | |
| "</s_supp_tax_id>", | |
| "<s_cust_register_id>" | |
| ], | |
| "is_local": true, | |
| "is_vqa": true, | |
| "model_max_length": 1000000000000000019884624838656, | |
| "pad_token": "<pad>", | |
| "processor_class": "Pix2StructProcessor", | |
| "sp_model_kwargs": {}, | |
| "tokenizer_class": "T5Tokenizer", | |
| "unk_token": "<unk>" | |
| } | |