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

Libraries

How to use davelotito/donut-base-sroie-test 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-test")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("davelotito/donut-base-sroie-test")
model = AutoModelForMultimodalLM.from_pretrained("davelotito/donut-base-sroie-test")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

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

Use Docker

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

SGLang

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

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

donut-base-sroie-test

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

Loss: 0.3913
Bleu: 0.0706
Precisions: [0.8125, 0.7440860215053764, 0.7064676616915423, 0.6637168141592921]
Brevity Penalty: 0.0968
Length Ratio: 0.2998
Translation Length: 528
Reference Length: 1761
Cer: 0.7448
Wer: 0.8259

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: 10
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	0.5515	0.0638	[0.7647058823529411, 0.6831896551724138, 0.6309226932668329, 0.5857988165680473]	0.0962	0.2993	527	1761	0.7627	0.8549
0.5624	2.0	125	0.4773	0.0665	[0.7763157894736842, 0.6865671641791045, 0.6403940886699507, 0.5918367346938775]	0.0992	0.3021	532	1761	0.7562	0.8390
0.5624	2.99	187	0.4273	0.0658	[0.7840909090909091, 0.6903225806451613, 0.6517412935323383, 0.6047197640117994]	0.0968	0.2998	528	1761	0.7513	0.8373
0.2964	4.0	250	0.4007	0.0679	[0.800376647834275, 0.7072649572649573, 0.6592592592592592, 0.6023391812865497]	0.0986	0.3015	531	1761	0.7478	0.8286
0.2238	4.99	312	0.3965	0.0710	[0.8142589118198874, 0.7297872340425532, 0.683046683046683, 0.6308139534883721]	0.0999	0.3027	533	1761	0.7427	0.8271
0.2238	6.0	375	0.3939	0.0719	[0.8301886792452831, 0.7537473233404711, 0.7054455445544554, 0.656891495601173]	0.0980	0.3010	530	1761	0.7414	0.8246
0.147	6.99	437	0.3853	0.0693	[0.8159392789373814, 0.7370689655172413, 0.6932668329177057, 0.6449704142011834]	0.0962	0.2993	527	1761	0.7437	0.8237
0.1316	8.0	500	0.3827	0.0698	[0.8037735849056604, 0.7301927194860813, 0.6856435643564357, 0.6392961876832844]	0.0980	0.3010	530	1761	0.7456	0.8296
0.1316	8.99	562	0.3895	0.0704	[0.8174904942965779, 0.7516198704103672, 0.715, 0.6706231454005934]	0.0956	0.2987	526	1761	0.7439	0.8259
0.1153	9.92	620	0.3913	0.0706	[0.8125, 0.7440860215053764, 0.7064676616915423, 0.6637168141592921]	0.0968	0.2998	528	1761	0.7448	0.8259

Framework versions

Transformers 4.40.0.dev0
Pytorch 2.1.0
Datasets 2.18.0
Tokenizers 0.15.2

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Safetensors

Model size

0.2B params

Tensor type

I64

F32

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