Instructions to use davelotito/donut_experiment_bayesian_trial_0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use davelotito/donut_experiment_bayesian_trial_0 with Transformers:

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

pipe = pipeline("image-text-to-text", model="davelotito/donut_experiment_bayesian_trial_0")

# Load model directly
from transformers import AutoTokenizer, AutoModelForImageTextToText

tokenizer = AutoTokenizer.from_pretrained("davelotito/donut_experiment_bayesian_trial_0")
model = AutoModelForImageTextToText.from_pretrained("davelotito/donut_experiment_bayesian_trial_0")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use davelotito/donut_experiment_bayesian_trial_0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "davelotito/donut_experiment_bayesian_trial_0"
# 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_experiment_bayesian_trial_0",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/davelotito/donut_experiment_bayesian_trial_0

SGLang

How to use davelotito/donut_experiment_bayesian_trial_0 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_experiment_bayesian_trial_0" \
    --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_experiment_bayesian_trial_0",
		"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_experiment_bayesian_trial_0" \
        --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_experiment_bayesian_trial_0",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use davelotito/donut_experiment_bayesian_trial_0 with Docker Model Runner:
```
docker model run hf.co/davelotito/donut_experiment_bayesian_trial_0
```

donut_experiment_bayesian_trial_0

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: 0.4050
Bleu: 0.0639
Precisions: [0.79957805907173, 0.7386091127098321, 0.7083333333333334, 0.6765676567656765]
Brevity Penalty: 0.0876
Length Ratio: 0.2912
Translation Length: 474
Reference Length: 1628
Cer: 0.7653
Wer: 0.8371

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: 1.2045081648781836e-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
0.9353	1.0	253	0.6228	0.0486	[0.7096774193548387, 0.6053921568627451, 0.5612535612535613, 0.5102040816326531]	0.0820	0.2856	465	1628	0.7751	0.8592
0.462	2.0	506	0.4846	0.0568	[0.7913978494623656, 0.7058823529411765, 0.6609686609686609, 0.6224489795918368]	0.0820	0.2856	465	1628	0.7650	0.8423
0.4071	3.0	759	0.4226	0.0626	[0.7899159663865546, 0.711217183770883, 0.6767955801104972, 0.6459016393442623]	0.0889	0.2924	476	1628	0.7685	0.8436
0.3007	4.0	1012	0.4092	0.0638	[0.7957894736842105, 0.7344497607655502, 0.7008310249307479, 0.6644736842105263]	0.0883	0.2918	475	1628	0.7640	0.8397
0.3114	5.0	1265	0.4050	0.0639	[0.79957805907173, 0.7386091127098321, 0.7083333333333334, 0.6765676567656765]	0.0876	0.2912	474	1628	0.7653	0.8371

Framework versions

Transformers 4.40.0
Pytorch 2.1.0
Datasets 2.18.0
Tokenizers 0.19.1

Downloads last month: 2

Safetensors

Model size

0.2B params

Tensor type

I64

F32

Inference Providers NEW

Image-Text-to-Text

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