Instructions to use davelotito/donut_experiment_bayesian_trial_17 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use davelotito/donut_experiment_bayesian_trial_17 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_17")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("davelotito/donut_experiment_bayesian_trial_17")
model = AutoModelForMultimodalLM.from_pretrained("davelotito/donut_experiment_bayesian_trial_17")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use davelotito/donut_experiment_bayesian_trial_17 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_17"
# 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_17",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/davelotito/donut_experiment_bayesian_trial_17

SGLang

How to use davelotito/donut_experiment_bayesian_trial_17 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_17" \
    --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_17",
		"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_17" \
        --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_17",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use davelotito/donut_experiment_bayesian_trial_17 with Docker Model Runner:
```
docker model run hf.co/davelotito/donut_experiment_bayesian_trial_17
```

donut_experiment_bayesian_trial_17

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: 0.4635
Bleu: 0.0675
Precisions: [0.8301886792452831, 0.7738095238095238, 0.7272727272727273, 0.6895424836601307]
Brevity Penalty: 0.0895
Length Ratio: 0.2930
Translation Length: 477
Reference Length: 1628
Cer: 0.7603
Wer: 0.8297

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: 0.00018015728878154226
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
0.8044	1.0	253	0.7112	0.0610	[0.7535641547861507, 0.6497695852534562, 0.5809018567639257, 0.5125]	0.0987	0.3016	491	1628	0.7647	0.8548
0.3513	2.0	506	0.5640	0.0632	[0.7908902691511387, 0.7089201877934272, 0.6449864498644986, 0.5801282051282052]	0.0934	0.2967	483	1628	0.7549	0.8416
0.2101	3.0	759	0.4754	0.0666	[0.8198757763975155, 0.744131455399061, 0.6802168021680217, 0.6217948717948718]	0.0934	0.2967	483	1628	0.7508	0.8282
0.0756	4.0	1012	0.4635	0.0675	[0.8301886792452831, 0.7738095238095238, 0.7272727272727273, 0.6895424836601307]	0.0895	0.2930	477	1628	0.7603	0.8297

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

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_17

Base model

naver-clova-ix/donut-base

Finetuned

(481)

this model