Instructions to use mariovigliar/trocr-base-printed_license_plates_ocr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mariovigliar/trocr-base-printed_license_plates_ocr with Transformers:

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

pipe = pipeline("image-text-to-text", model="mariovigliar/trocr-base-printed_license_plates_ocr")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("mariovigliar/trocr-base-printed_license_plates_ocr")
model = AutoModelForMultimodalLM.from_pretrained("mariovigliar/trocr-base-printed_license_plates_ocr")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use mariovigliar/trocr-base-printed_license_plates_ocr with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mariovigliar/trocr-base-printed_license_plates_ocr"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mariovigliar/trocr-base-printed_license_plates_ocr",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/mariovigliar/trocr-base-printed_license_plates_ocr

SGLang

How to use mariovigliar/trocr-base-printed_license_plates_ocr 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 "mariovigliar/trocr-base-printed_license_plates_ocr" \
    --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": "mariovigliar/trocr-base-printed_license_plates_ocr",
		"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 "mariovigliar/trocr-base-printed_license_plates_ocr" \
        --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": "mariovigliar/trocr-base-printed_license_plates_ocr",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use mariovigliar/trocr-base-printed_license_plates_ocr with Docker Model Runner:
```
docker model run hf.co/mariovigliar/trocr-base-printed_license_plates_ocr
```

trocr-base-printed_license_plates_ocr

This model is a fine-tuned version of microsoft/trocr-base-printed on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.1479
Cer: 0.0343

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: 5e-05
train_batch_size: 32
eval_batch_size: 32
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 2

Training results

Training Loss	Epoch	Step	Validation Loss	Cer
0.356	1.0	500	0.1934	0.041
0.1558	2.0	1000	0.1479	0.0343

Framework versions

Transformers 4.37.2
Pytorch 2.0.0+cu117
Datasets 2.17.0
Tokenizers 0.15.2

Downloads last month: 4

Safetensors

Model size

0.3B params

Tensor type

F32

Inference Providers NEW

Image-Text-to-Text

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Model tree for mariovigliar/trocr-base-printed_license_plates_ocr

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microsoft/trocr-base-printed

Finetuned

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