Instructions to use davelotito/donut_experiment_bayesian_trial_16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use davelotito/donut_experiment_bayesian_trial_16 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_16")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("davelotito/donut_experiment_bayesian_trial_16")
model = AutoModelForMultimodalLM.from_pretrained("davelotito/donut_experiment_bayesian_trial_16")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use davelotito/donut_experiment_bayesian_trial_16 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_16"
# 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_16",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/davelotito/donut_experiment_bayesian_trial_16

SGLang

How to use davelotito/donut_experiment_bayesian_trial_16 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_16" \
    --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_16",
		"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_16" \
        --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_16",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use davelotito/donut_experiment_bayesian_trial_16 with Docker Model Runner:
```
docker model run hf.co/davelotito/donut_experiment_bayesian_trial_16
```

donut_experiment_bayesian_trial_16

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: 0.5541
Bleu: 0.0670
Precisions: [0.8417721518987342, 0.7841726618705036, 0.7388888888888889, 0.6996699669966997]
Brevity Penalty: 0.0876
Length Ratio: 0.2912
Translation Length: 474
Reference Length: 1628
Cer: 0.7567
Wer: 0.8224

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: 0.00011219603369833024
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: 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
0.0965	1.0	253	0.5550	0.0624	[0.7995824634655533, 0.7085308056872038, 0.6520547945205479, 0.6038961038961039]	0.0908	0.2942	479	1628	0.7576	0.8347
0.0844	2.0	506	0.5896	0.0651	[0.8218029350104822, 0.7476190476190476, 0.696969696969697, 0.6535947712418301]	0.0895	0.2930	477	1628	0.7557	0.8302
0.0539	3.0	759	0.5594	0.0666	[0.8322851153039832, 0.7642857142857142, 0.7134986225895317, 0.673202614379085]	0.0895	0.2930	477	1628	0.7552	0.8223
0.023	4.0	1012	0.5541	0.0670	[0.8417721518987342, 0.7841726618705036, 0.7388888888888889, 0.6996699669966997]	0.0876	0.2912	474	1628	0.7567	0.8224

Framework versions

Transformers 4.40.0
Pytorch 2.1.0
Datasets 2.18.0
Tokenizers 0.19.1

Downloads last month: 3

Safetensors

Model size

0.2B params

Tensor type

I64

F32

Inference Providers NEW

Image-Text-to-Text

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for davelotito/donut_experiment_bayesian_trial_16

Base model

naver-clova-ix/donut-base

Finetuned

(481)

this model