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
Instructions to use TomasFAV/Pix2StructCzechInvoiceV0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TomasFAV/Pix2StructCzechInvoiceV0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="TomasFAV/Pix2StructCzechInvoiceV0")# Load model directly from transformers import AutoProcessor, AutoModelForImageTextToText processor = AutoProcessor.from_pretrained("TomasFAV/Pix2StructCzechInvoiceV0") model = AutoModelForImageTextToText.from_pretrained("TomasFAV/Pix2StructCzechInvoiceV0") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use TomasFAV/Pix2StructCzechInvoiceV0 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "TomasFAV/Pix2StructCzechInvoiceV0" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "TomasFAV/Pix2StructCzechInvoiceV0", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/TomasFAV/Pix2StructCzechInvoiceV0
- SGLang
How to use TomasFAV/Pix2StructCzechInvoiceV0 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/Pix2StructCzechInvoiceV0" \ --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/Pix2StructCzechInvoiceV0", "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/Pix2StructCzechInvoiceV0" \ --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/Pix2StructCzechInvoiceV0", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use TomasFAV/Pix2StructCzechInvoiceV0 with Docker Model Runner:
docker model run hf.co/TomasFAV/Pix2StructCzechInvoiceV0
Upload processor
Browse files- tokenizer_config.json +4 -0
tokenizer_config.json
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"eos_token": "</s>",
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"extra_ids": 100,
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"extra_special_tokens": {},
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"model_max_length": 1000000000000000019884624838656,
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"pad_token": "<pad>",
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"processor_class": "Pix2StructProcessor",
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"sp_model_kwargs": {},
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"tokenizer_class": "T5Tokenizer",
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| 1124 |
"eos_token": "</s>",
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"extra_ids": 100,
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| 1126 |
"extra_special_tokens": {},
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| 1127 |
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"max_length": 512,
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| 1128 |
"model_max_length": 1000000000000000019884624838656,
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| 1129 |
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"pad_to_multiple_of": null,
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"pad_token": "<pad>",
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"pad_token_type_id": 0,
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"padding_side": "right",
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"processor_class": "Pix2StructProcessor",
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"sp_model_kwargs": {},
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"tokenizer_class": "T5Tokenizer",
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