Image-Text-to-Text
Transformers
TensorBoard
Safetensors
vision-encoder-decoder
Generated from Trainer
Instructions to use TomasFAV/DonutInvoiceCzechV03 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
- 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
| { | |
| "backend": "tokenizers", | |
| "bos_token": "<s>", | |
| "cls_token": "<s>", | |
| "eos_token": "</s>", | |
| "extra_special_tokens": [ | |
| "</s_cust_register_id>", | |
| "<s_payment_type>", | |
| "</s_supp_register_id>", | |
| "<s_due_date>", | |
| "</s_iban>", | |
| "<s_issue_date>", | |
| "<s_supp_register_id>", | |
| "</s_const_symbol>", | |
| "</s_issue_date>", | |
| "</s_bic>", | |
| "</s_total>", | |
| "</s_invoice_number>", | |
| "</s_supp_tax_id>", | |
| "<s_const_symbol>", | |
| "</s>", | |
| "<s_cust_tax_id>", | |
| "<s_cust_register_id>", | |
| "<s_bic>", | |
| "</s_bank_account_number>", | |
| "</s_due_date>", | |
| "<s_total>", | |
| "<s_bank_account_number>", | |
| "</s_variable_symbol>", | |
| "<s_variable_symbol>", | |
| "</s_cust_tax_id>", | |
| "<s_cord-v2>", | |
| "</s_payment_type>", | |
| "<s_iban>", | |
| "<s_taxable_supply_date>", | |
| "<s_invoice_number>", | |
| "<s_supp_tax_id>", | |
| "</s_taxable_supply_date>" | |
| ], | |
| "from_slow": true, | |
| "is_local": false, | |
| "mask_token": "<mask>", | |
| "max_length": 768, | |
| "model_max_length": 1000000000000000019884624838656, | |
| "pad_to_multiple_of": null, | |
| "pad_token": "<pad>", | |
| "pad_token_type_id": 0, | |
| "padding_side": "right", | |
| "processor_class": "DonutProcessor", | |
| "sep_token": "</s>", | |
| "sp_model_kwargs": {}, | |
| "stride": 0, | |
| "tokenizer_class": "TokenizersBackend", | |
| "truncation_side": "right", | |
| "truncation_strategy": "longest_first", | |
| "unk_token": "<unk>" | |
| } | |