Instructions to use davelotito/donut-base-sroie-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use davelotito/donut-base-sroie-v2 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="davelotito/donut-base-sroie-v2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("davelotito/donut-base-sroie-v2")
model = AutoModelForMultimodalLM.from_pretrained("davelotito/donut-base-sroie-v2", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use davelotito/donut-base-sroie-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "davelotito/donut-base-sroie-v2"
# 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-base-sroie-v2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/davelotito/donut-base-sroie-v2

SGLang

How to use davelotito/donut-base-sroie-v2 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-base-sroie-v2" \
    --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-base-sroie-v2",
		"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-base-sroie-v2" \
        --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-base-sroie-v2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use davelotito/donut-base-sroie-v2 with Docker Model Runner:
```
docker model run hf.co/davelotito/donut-base-sroie-v2
```

donut-base-sroie-v2

This model is a fine-tuned version of davelotito/donut-base-sroie on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 0.4355
Bleu: 0.8879
Precisions: [0.943646408839779, 0.9119229045271179, 0.8854285064787452, 0.860009225092251]
Brevity Penalty: 0.9868
Length Ratio: 0.9869
Translation Length: 4525
Reference Length: 4585
Cer: 0.0857
Wer: 0.2978

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: 2e-05
train_batch_size: 4
eval_batch_size: 4
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 8
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
No log	0.99	62	0.4638	0.8823	[0.9399823477493381, 0.9044528977399866, 0.8772128915115751, 0.8514851485148515]	0.9884	0.9884	4532	4585	0.0912	0.3085
0.0043	2.0	125	0.4421	0.8853	[0.9405155320555189, 0.9059428060768543, 0.8794470881486517, 0.8537931034482759]	0.9899	0.9900	4539	4585	0.0889	0.3050
0.0043	2.99	187	0.4328	0.8904	[0.9399122807017544, 0.9068267734044919, 0.8809201623815968, 0.8558682223747426]	0.9945	0.9945	4560	4585	0.0842	0.2939
0.0106	3.97	248	0.4355	0.8879	[0.943646408839779, 0.9119229045271179, 0.8854285064787452, 0.860009225092251]	0.9868	0.9869	4525	4585	0.0857	0.2978

Framework versions

Transformers 4.40.0.dev0
Pytorch 2.1.0
Datasets 2.18.0
Tokenizers 0.15.2

Downloads last month: 13

Safetensors

Model size

0.2B params

Tensor type

I64

F32

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Image-Text-to-Text

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