Instructions to use linhchitwt/donut-bartpho-fulldata with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use linhchitwt/donut-bartpho-fulldata with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="linhchitwt/donut-bartpho-fulldata")# Load model directly from transformers import AutoTokenizer, AutoModelForImageTextToText tokenizer = AutoTokenizer.from_pretrained("linhchitwt/donut-bartpho-fulldata") model = AutoModelForImageTextToText.from_pretrained("linhchitwt/donut-bartpho-fulldata") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use linhchitwt/donut-bartpho-fulldata with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "linhchitwt/donut-bartpho-fulldata" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "linhchitwt/donut-bartpho-fulldata", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/linhchitwt/donut-bartpho-fulldata
- SGLang
How to use linhchitwt/donut-bartpho-fulldata 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 "linhchitwt/donut-bartpho-fulldata" \ --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": "linhchitwt/donut-bartpho-fulldata", "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 "linhchitwt/donut-bartpho-fulldata" \ --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": "linhchitwt/donut-bartpho-fulldata", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use linhchitwt/donut-bartpho-fulldata with Docker Model Runner:
docker model run hf.co/linhchitwt/donut-bartpho-fulldata
| { | |
| "added_tokens_decoder": { | |
| "0": { | |
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| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false, | |
| "special": true | |
| }, | |
| "1": { | |
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| "3": { | |
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| "content": "<s_product_name>", | |
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| "content": "<s_date>", | |
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| "content": "<s_product_id>", | |
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| "special": false | |
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| "content": "</s_product_id>", | |
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| "content": "<s_pos_id>", | |
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| } | |
| }, | |
| "additional_special_tokens": [ | |
| "<s_iitcdip>", | |
| "<s_synthdog>" | |
| ], | |
| "bos_token": "<s>", | |
| "clean_up_tokenization_spaces": true, | |
| "cls_token": "<s>", | |
| "eos_token": "</s>", | |
| "mask_token": "<mask>", | |
| "model_max_length": 1000000000000000019884624838656, | |
| "pad_token": "<pad>", | |
| "processor_class": "DonutProcessor", | |
| "sep_token": "</s>", | |
| "sp_model_kwargs": {}, | |
| "tokenizer_class": "XLMRobertaTokenizer", | |
| "unk_token": "<unk>" | |
| } | |