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

Libraries

How to use davelotito/donut-base-sroie-bayesian-optimization 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-bayesian-optimization")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

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

Use Docker

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

SGLang

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

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

donut-base-sroie-bayesian-optimization

This model is a fine-tuned version of naver-clova-ix/donut-base on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.1396
Bleu: 0.0196
Precisions: [0.9883177570093458, 0.9724655819774718, 0.954177897574124, 0.9328467153284672]
Brevity Penalty: 0.0203
Length Ratio: 0.2043
Translation Length: 856
Reference Length: 4190
Cer: 0.8584
Wer: 1.0

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: 1.2010406976282324e-05
train_batch_size: 1
eval_batch_size: 1
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 2
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 5
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
0.021	1.0	253	0.1656	0.0194	[0.9848130841121495, 0.9649561952440551, 0.9420485175202157, 0.9153284671532846]	0.0203	0.2043	856	4190	0.8596	1.0
0.0353	2.0	506	0.1501	0.0195	[0.9813736903376019, 0.9588528678304239, 0.9328859060402684, 0.9026162790697675]	0.0207	0.2050	859	4190	0.8595	1.0
0.0417	3.0	759	0.1423	0.0195	[0.9871495327102804, 0.9699624530663329, 0.9501347708894878, 0.927007299270073]	0.0203	0.2043	856	4190	0.8586	1.0
0.0308	4.0	1012	0.1403	0.0193	[0.9859649122807017, 0.9674185463659147, 0.9460188933873145, 0.9210526315789473]	0.0202	0.2041	855	4190	0.8593	1.0
0.0464	5.0	1265	0.1396	0.0196	[0.9883177570093458, 0.9724655819774718, 0.954177897574124, 0.9328467153284672]	0.0203	0.2043	856	4190	0.8584	1.0

Framework versions

Transformers 4.41.0.dev0
Pytorch 2.1.0
Datasets 2.19.0
Tokenizers 0.19.1

Downloads last month: 88

Safetensors

Model size

0.2B params

Tensor type

I64

F32

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Model tree for davelotito/donut-base-sroie-bayesian-optimization

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