Instructions to use davelotito/donut-base-sroie-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use davelotito/donut-base-sroie-v3 with Transformers:

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

pipe = pipeline("image-text-to-text", model="davelotito/donut-base-sroie-v3")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("davelotito/donut-base-sroie-v3")
model = AutoModelForMultimodalLM.from_pretrained("davelotito/donut-base-sroie-v3", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use davelotito/donut-base-sroie-v3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "davelotito/donut-base-sroie-v3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "davelotito/donut-base-sroie-v3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/davelotito/donut-base-sroie-v3

SGLang

How to use davelotito/donut-base-sroie-v3 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 "davelotito/donut-base-sroie-v3" \
    --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": "davelotito/donut-base-sroie-v3",
		"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 "davelotito/donut-base-sroie-v3" \
        --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": "davelotito/donut-base-sroie-v3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use davelotito/donut-base-sroie-v3 with Docker Model Runner:
```
docker model run hf.co/davelotito/donut-base-sroie-v3
```

donut-base-sroie-v3

This model is a fine-tuned version of naver-clova-ix/donut-base on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 1.7091
Bleu: 0.0045
Precisions: [0.32051282051282054, 0.10617283950617284, 0.043859649122807015, 0.02867383512544803]
Brevity Penalty: 0.0555
Length Ratio: 0.2570
Translation Length: 468
Reference Length: 1821
Cer: 0.8657
Wer: 0.9978

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 4
eval_batch_size: 4
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 8
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 4
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Bleu	Precisions	Brevity Penalty	Length Ratio	Translation Length	Reference Length	Cer	Wer
No log	0.99	62	1.7048	0.0026	[0.3287981859410431, 0.09259259259259259, 0.025396825396825397, 0.015873015873015872]	0.0438	0.2422	441	1821	0.8729	1.0
0.6536	2.0	125	1.7425	0.0035	[0.32051282051282054, 0.0962962962962963, 0.029239766081871343, 0.017921146953405017]	0.0555	0.2570	468	1821	0.8701	0.9986
0.6536	2.99	187	1.6949	0.0038	[0.3148936170212766, 0.09582309582309582, 0.03197674418604651, 0.021352313167259787]	0.0564	0.2581	470	1821	0.8670	0.9978
0.6585	3.97	248	1.7091	0.0045	[0.32051282051282054, 0.10617283950617284, 0.043859649122807015, 0.02867383512544803]	0.0555	0.2570	468	1821	0.8657	0.9978

Framework versions

Transformers 4.40.0.dev0
Pytorch 2.1.0
Datasets 2.18.0
Tokenizers 0.15.2

Downloads last month: 2

Safetensors

Model size

0.2B params

Tensor type

I64

F32

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