Instructions to use subhavarshith/donut_table_data_u with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use subhavarshith/donut_table_data_u with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="subhavarshith/donut_table_data_u")

# Load model directly
from transformers import AutoTokenizer, AutoModelForImageTextToText

tokenizer = AutoTokenizer.from_pretrained("subhavarshith/donut_table_data_u")
model = AutoModelForImageTextToText.from_pretrained("subhavarshith/donut_table_data_u")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use subhavarshith/donut_table_data_u with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "subhavarshith/donut_table_data_u"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "subhavarshith/donut_table_data_u",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/subhavarshith/donut_table_data_u

SGLang

How to use subhavarshith/donut_table_data_u 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 "subhavarshith/donut_table_data_u" \
    --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": "subhavarshith/donut_table_data_u",
		"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 "subhavarshith/donut_table_data_u" \
        --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": "subhavarshith/donut_table_data_u",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use subhavarshith/donut_table_data_u with Docker Model Runner:
```
docker model run hf.co/subhavarshith/donut_table_data_u
```

donut_table_data_u / tokenizer_config.json

subhavarshith

Training done

7653a88 verified almost 2 years ago

raw

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5.02 kB

	{
	"added_tokens_decoder": {
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	"content": "<s>",
	"lstrip": false,
	"normalized": true,
	"rstrip": false,
	"single_word": false,
	"special": false
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	"57525": {
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	"57526": {
	"content": "</s_table>",
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	"57538": {
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	"57539": {
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	"57540": {
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	"57541": {
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	"additional_special_tokens": [
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	],
	"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>"
	}