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

Libraries

How to use Serialtechlab/dhivehi-trocr-small 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")

# Load model directly
from transformers import AutoTokenizer, AutoModelForImageTextToText

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

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

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

Use Docker

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

SGLang

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

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

dhivehi-trocr-small / README.md

Serialtechlab

End of training

b4b85ec verified about 2 months ago

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	---
	library_name: transformers
	base_model: microsoft/trocr-small-printed
	tags:
	- generated_from_trainer
	model-index:
	- name: dhivehi-trocr-small
	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

	This model is a fine-tuned version of [microsoft/trocr-small-printed](https://huggingface.co/microsoft/trocr-small-printed) on the None dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.0606
	- Cer: 0.1147

	## 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: 4e-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: 6
	- mixed_precision_training: Native AMP

	### Training results

	\| Training Loss \| Epoch \| Step \| Cer \| Validation Loss \|
	\|:-------------:\|:------:\|:-----:\|:------:\|:---------------:\|
	\| 2.0301 \| 0.1337 \| 1000 \| 0.4438 \| 1.9695 \|
	\| 1.8511 \| 0.2673 \| 2000 \| 0.4465 \| 1.7758 \|
	\| 1.7348 \| 0.4010 \| 3000 \| 0.4479 \| 1.6484 \|
	\| 1.6226 \| 0.5346 \| 4000 \| 0.4414 \| 1.5418 \|
	\| 1.5449 \| 0.6683 \| 5000 \| 0.4396 \| 1.4657 \|
	\| 1.4864 \| 0.8019 \| 6000 \| 0.4335 \| 1.3844 \|
	\| 1.3952 \| 0.9356 \| 7000 \| 0.4282 \| 1.3176 \|
	\| 1.3033 \| 1.0692 \| 8000 \| 0.4102 \| 1.1908 \|
	\| 1.1261 \| 1.2029 \| 9000 \| 0.3844 \| 1.0153 \|
	\| 0.9671 \| 1.3365 \| 10000 \| 0.3496 \| 0.9206 \|
	\| 0.8140 \| 1.4702 \| 11000 \| 0.3190 \| 0.9407 \|
	\| 0.6986 \| 1.6038 \| 12000 \| 0.2873 \| 0.6398 \|
	\| 0.6410 \| 1.7375 \| 13000 \| 0.2570 \| 0.5260 \|
	\| 0.5472 \| 1.8712 \| 14000 \| 0.2511 \| 0.7460 \|
	\| 0.4880 \| 2.0048 \| 15000 \| 0.2288 \| 0.4803 \|
	\| 0.4308 \| 2.1385 \| 16000 \| 0.2219 \| 0.5494 \|
	\| 0.4045 \| 2.2721 \| 17000 \| 0.2115 \| 0.4733 \|
	\| 0.3729 \| 2.4058 \| 18000 \| 0.2059 \| 0.4428 \|
	\| 0.3587 \| 2.5394 \| 19000 \| 0.1910 \| 0.3564 \|
	\| 0.3318 \| 2.6731 \| 20000 \| 0.1906 \| 0.4022 \|
	\| 0.3069 \| 2.8067 \| 21000 \| 0.1927 \| 0.5067 \|
	\| 0.3014 \| 2.9404 \| 22000 \| 0.1782 \| 0.3107 \|
	\| 0.3963 \| 1.2283 \| 23000 \| 0.2111 \| 0.6155 \|
	\| 0.3335 \| 1.2817 \| 24000 \| 0.1934 \| 0.3839 \|
	\| 0.3227 \| 1.3351 \| 25000 \| 0.1777 \| 0.2538 \|
	\| 0.2834 \| 1.3885 \| 26000 \| 0.1752 \| 0.2734 \|
	\| 0.2660 \| 1.4419 \| 27000 \| 0.1617 \| 0.2125 \|
	\| 0.2424 \| 1.4953 \| 28000 \| 0.1682 \| 0.2456 \|
	\| 0.1846 \| 3.2041 \| 30000 \| 0.3984 \| 0.1719 \|
	\| 0.1262 \| 3.7381 \| 35000 \| 0.1237 \| 0.1373 \|
	\| 0.0982 \| 4.2721 \| 40000 \| 0.0775 \| 0.1236 \|
	\| 0.0712 \| 4.8062 \| 45000 \| 0.1080 \| 0.1265 \|
	\| 0.0616 \| 5.3402 \| 50000 \| 0.0868 \| 0.1191 \|
	\| 0.0527 \| 5.8742 \| 55000 \| 0.0606 \| 0.1147 \|


	### Framework versions

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