Instructions to use Serialtechlab/dhivehi-trocr-small-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Serialtechlab/dhivehi-trocr-small-v2 with Transformers:

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

pipe = pipeline("image-text-to-text", model="Serialtechlab/dhivehi-trocr-small-v2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForImageTextToText

tokenizer = AutoTokenizer.from_pretrained("Serialtechlab/dhivehi-trocr-small-v2")
model = AutoModelForImageTextToText.from_pretrained("Serialtechlab/dhivehi-trocr-small-v2")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Serialtechlab/dhivehi-trocr-small-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Serialtechlab/dhivehi-trocr-small-v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Serialtechlab/dhivehi-trocr-small-v2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Serialtechlab/dhivehi-trocr-small-v2

SGLang

How to use Serialtechlab/dhivehi-trocr-small-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 "Serialtechlab/dhivehi-trocr-small-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": "Serialtechlab/dhivehi-trocr-small-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 "Serialtechlab/dhivehi-trocr-small-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": "Serialtechlab/dhivehi-trocr-small-v2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Serialtechlab/dhivehi-trocr-small-v2 with Docker Model Runner:
```
docker model run hf.co/Serialtechlab/dhivehi-trocr-small-v2
```

dhivehi-trocr-small-v2 / README.md

Serialtechlab

End of training

4e614e2 verified about 2 months ago

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	---
	library_name: transformers
	tags:
	- generated_from_trainer
	model-index:
	- name: dhivehi-trocr-small-v2
	results: []
	---

	<!-- This model card has been generated automatically according to the information the Trainer had access to. You
	should probably proofread and complete it, then remove this comment. -->

	# dhivehi-trocr-small-v2

	This model was trained from scratch on the None dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.0533
	- Cer: 0.1894

	## 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: 1e-05
	- train_batch_size: 32
	- eval_batch_size: 32
	- seed: 42
	- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
	- lr_scheduler_type: linear
	- num_epochs: 3
	- mixed_precision_training: Native AMP

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Cer \|
	\|:-------------:\|:------:\|:-----:\|:---------------:\|:------:\|
	\| 0.1992 \| 0.2350 \| 5000 \| 0.2111 \| 0.1751 \|
	\| 0.1335 \| 0.4700 \| 10000 \| 0.1193 \| 0.1761 \|
	\| 0.1055 \| 0.7050 \| 15000 \| 0.0941 \| 0.1838 \|
	\| 0.0928 \| 0.9400 \| 20000 \| 0.0898 \| 0.1903 \|
	\| 0.0806 \| 1.1750 \| 25000 \| 0.0911 \| 0.1823 \|
	\| 0.0698 \| 1.4100 \| 30000 \| 0.0699 \| 0.1954 \|
	\| 0.0666 \| 1.6450 \| 35000 \| 0.0662 \| 0.1899 \|
	\| 0.0619 \| 1.8801 \| 40000 \| 0.0674 \| 0.1960 \|
	\| 0.0577 \| 2.1151 \| 45000 \| 0.0584 \| 0.1895 \|
	\| 0.0579 \| 2.3501 \| 50000 \| 0.0686 \| 0.1932 \|
	\| 0.0515 \| 2.5851 \| 55000 \| 0.0551 \| 0.1892 \|
	\| 0.0528 \| 2.8201 \| 60000 \| 0.0533 \| 0.1894 \|


	### Framework versions

	- Transformers 5.0.0
	- Pytorch 2.10.0+cu128
	- Datasets 4.0.0
	- Tokenizers 0.22.2