Instructions to use Ogpoggi/donut_receipt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ogpoggi/donut_receipt with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="Ogpoggi/donut_receipt")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("Ogpoggi/donut_receipt") model = AutoModelForMultimodalLM.from_pretrained("Ogpoggi/donut_receipt", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Ogpoggi/donut_receipt with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Ogpoggi/donut_receipt" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Ogpoggi/donut_receipt", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Ogpoggi/donut_receipt
- SGLang
How to use Ogpoggi/donut_receipt 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 "Ogpoggi/donut_receipt" \ --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": "Ogpoggi/donut_receipt", "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 "Ogpoggi/donut_receipt" \ --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": "Ogpoggi/donut_receipt", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Ogpoggi/donut_receipt with Docker Model Runner:
docker model run hf.co/Ogpoggi/donut_receipt
Download tokenizer_config.json from Ogpoggi/donut_receipt: direct link, hf CLI and curl.
- Browser
- Download file 489 Bytes
-
https://huggingface.co/Ogpoggi/donut_receipt/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://Ogpoggi/donut_receipt/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/Ogpoggi/donut_receipt/resolve/main/tokenizer_config.json
489 Bytes
| { | |
| "bos_token": "<s>", | |
| "cls_token": "<s>", | |
| "eos_token": "</s>", | |
| "mask_token": { | |
| "__type": "AddedToken", | |
| "content": "<mask>", | |
| "lstrip": true, | |
| "normalized": true, | |
| "rstrip": false, | |
| "single_word": false | |
| }, | |
| "name_or_path": "nielsr/donut-base", | |
| "pad_token": "<pad>", | |
| "processor_class": "DonutProcessor", | |
| "sep_token": "</s>", | |
| "sp_model_kwargs": {}, | |
| "special_tokens_map_file": null, | |
| "tokenizer_class": "XLMRobertaTokenizer", | |
| "unk_token": "<unk>" | |
| } | |