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 Settings
- 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
File size: 1,376 Bytes
00586f7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 | {
"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>"
}
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