Instructions to use hf-tuner/donut-classification-turbo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hf-tuner/donut-classification-turbo with Transformers:

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

pipe = pipeline("image-text-to-text", model="hf-tuner/donut-classification-turbo")

# Load model directly
from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("hf-tuner/donut-classification-turbo")
model = AutoModel.from_pretrained("hf-tuner/donut-classification-turbo")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use hf-tuner/donut-classification-turbo with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/hf-tuner/donut-classification-turbo

SGLang

How to use hf-tuner/donut-classification-turbo 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 "hf-tuner/donut-classification-turbo" \
    --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": "hf-tuner/donut-classification-turbo",
		"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 "hf-tuner/donut-classification-turbo" \
        --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": "hf-tuner/donut-classification-turbo",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use hf-tuner/donut-classification-turbo with Docker Model Runner:
```
docker model run hf.co/hf-tuner/donut-classification-turbo
```

donut-classification-turbo

This model is a fine-tuned version of donut-base-finetuned-rvl-cdip on rvl-cdip-document-classification dataset. It achieves the following results on the evaluation set:

Loss: 0.0499
Accuracy: 93.34%

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 1e-05
train_batch_size: 2
eval_batch_size: 8
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 3
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
0.1319	0.5	2000	0.1200
0.1365	1.0	4000	0.0845
0.1203	1.5	6000	0.0751
0.1128	2.0	8000	0.0677
0.0734	2.5	10000	0.0541
0.0707	3.0	12000	0.0499

Framework versions

Transformers 4.56.1
Pytorch 2.8.0+cu126
Datasets 4.0.0
Tokenizers 0.22.1

Downloads last month: 1

Safetensors

Model size

0.2B params

Tensor type

I64

F32

Inference Providers NEW

Image-Text-to-Text

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Dataset used to train hf-tuner/donut-classification-turbo

Collection including hf-tuner/donut-classification-turbo

Donut 🍩

Collection

OCR-free Document Understanding Transformer (Donut) • 4 items • Updated Oct 15, 2025 • 1