Instructions to use davelotito/donut_experiment_bayesian_trial_9 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use davelotito/donut_experiment_bayesian_trial_9 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_9")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("davelotito/donut_experiment_bayesian_trial_9")
model = AutoModelForMultimodalLM.from_pretrained("davelotito/donut_experiment_bayesian_trial_9")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use davelotito/donut_experiment_bayesian_trial_9 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_9"
# 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_9",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/davelotito/donut_experiment_bayesian_trial_9

SGLang

How to use davelotito/donut_experiment_bayesian_trial_9 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_9" \
    --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_9",
		"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_9" \
        --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_9",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use davelotito/donut_experiment_bayesian_trial_9 with Docker Model Runner:
```
docker model run hf.co/davelotito/donut_experiment_bayesian_trial_9
```

donut_experiment_bayesian_trial_9

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: 0.5922
Bleu: 0.0587
Precisions: [0.735655737704918, 0.6310904872389791, 0.5668449197860963, 0.5173501577287066]
Brevity Penalty: 0.0967
Length Ratio: 0.2998
Translation Length: 488
Reference Length: 1628
Cer: 0.7688
Wer: 0.8559

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: 1.393489197537874e-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: 2
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
1.1982	1.0	253	0.7300	0.0555	[0.6828282828282828, 0.5616438356164384, 0.5091863517060368, 0.45987654320987653]	0.1014	0.3041	495	1628	0.7812	0.8659
0.5755	2.0	506	0.5922	0.0587	[0.735655737704918, 0.6310904872389791, 0.5668449197860963, 0.5173501577287066]	0.0967	0.2998	488	1628	0.7688	0.8559

Framework versions

Transformers 4.40.0
Pytorch 2.1.0
Datasets 2.18.0
Tokenizers 0.19.1

Downloads last month: 4

Safetensors

Model size

0.2B params

Tensor type

I64

F32

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Image-Text-to-Text

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