Instructions to use davelotito/donut_experiment_bayesian_trial_7 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use davelotito/donut_experiment_bayesian_trial_7 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_7")

# Load model directly
from transformers import AutoTokenizer, AutoModelForImageTextToText

tokenizer = AutoTokenizer.from_pretrained("davelotito/donut_experiment_bayesian_trial_7")
model = AutoModelForImageTextToText.from_pretrained("davelotito/donut_experiment_bayesian_trial_7")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use davelotito/donut_experiment_bayesian_trial_7 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_7"
# 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_7",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/davelotito/donut_experiment_bayesian_trial_7

SGLang

How to use davelotito/donut_experiment_bayesian_trial_7 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_7" \
    --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_7",
		"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_7" \
        --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_7",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use davelotito/donut_experiment_bayesian_trial_7 with Docker Model Runner:
```
docker model run hf.co/davelotito/donut_experiment_bayesian_trial_7
```

donut_experiment_bayesian_trial_7

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: 0.3786
Bleu: 0.0669
Precisions: [0.8477801268498943, 0.7836538461538461, 0.7465181058495822, 0.7052980132450332]
Brevity Penalty: 0.0870
Length Ratio: 0.2905
Translation Length: 473
Reference Length: 1628
Cer: 0.7532
Wer: 0.8192

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: 3.540464175534869e-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
0.5323	1.0	253	0.4204	0.0580	[0.7710084033613446, 0.6778042959427207, 0.6132596685082873, 0.5639344262295082]	0.0889	0.2924	476	1628	0.7617	0.8431
0.2487	2.0	506	0.3788	0.0609	[0.8123667377398721, 0.7402912621359223, 0.6929577464788732, 0.6476510067114094]	0.0845	0.2881	469	1628	0.7561	0.8279
0.1746	3.0	759	0.3551	0.0652	[0.836864406779661, 0.7759036144578313, 0.729050279329609, 0.6843853820598007]	0.0864	0.2899	472	1628	0.7541	0.8213
0.1191	4.0	1012	0.3690	0.0680	[0.8547368421052631, 0.784688995215311, 0.7451523545706371, 0.7039473684210527]	0.0883	0.2918	475	1628	0.7514	0.8192
0.1072	5.0	1265	0.3786	0.0669	[0.8477801268498943, 0.7836538461538461, 0.7465181058495822, 0.7052980132450332]	0.0870	0.2905	473	1628	0.7532	0.8192

Framework versions

Transformers 4.40.0
Pytorch 2.1.0
Datasets 2.18.0
Tokenizers 0.19.1

Downloads last month: 7

Safetensors

Model size

0.2B params

Tensor type

I64

F32

Inference Providers NEW

Image-Text-to-Text

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