Instructions to use davelotito/donut_experiment_bayesian_trial_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use davelotito/donut_experiment_bayesian_trial_2 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_2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForImageTextToText

tokenizer = AutoTokenizer.from_pretrained("davelotito/donut_experiment_bayesian_trial_2")
model = AutoModelForImageTextToText.from_pretrained("davelotito/donut_experiment_bayesian_trial_2")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use davelotito/donut_experiment_bayesian_trial_2 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_2"
# 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_2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/davelotito/donut_experiment_bayesian_trial_2

SGLang

How to use davelotito/donut_experiment_bayesian_trial_2 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_2" \
    --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_2",
		"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_2" \
        --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_2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use davelotito/donut_experiment_bayesian_trial_2 with Docker Model Runner:
```
docker model run hf.co/davelotito/donut_experiment_bayesian_trial_2
```

donut_experiment_bayesian_trial_2

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: 0.4983
Bleu: 0.0695
Precisions: [0.8257261410788381, 0.7717647058823529, 0.7255434782608695, 0.6816720257234726]
Brevity Penalty: 0.0928
Length Ratio: 0.2961
Translation Length: 482
Reference Length: 1628
Cer: 0.7610
Wer: 0.8275

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: 0.00015752383448484097
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
0.3017	1.0	253	0.7248	0.0641	[0.7525150905432596, 0.65, 0.587467362924282, 0.5276073619631901]	0.1027	0.3053	497	1628	0.7622	0.8495
0.1875	2.0	506	0.6129	0.0670	[0.7914110429447853, 0.7152777777777778, 0.6613333333333333, 0.60062893081761]	0.0974	0.3004	489	1628	0.7565	0.8375
0.1171	3.0	759	0.5027	0.0697	[0.8202479338842975, 0.7587822014051522, 0.7162162162162162, 0.6741214057507987]	0.0941	0.2973	484	1628	0.7563	0.8293
0.0432	4.0	1012	0.4983	0.0695	[0.8257261410788381, 0.7717647058823529, 0.7255434782608695, 0.6816720257234726]	0.0928	0.2961	482	1628	0.7610	0.8275

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

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