Instructions to use Ransaka/sinhala-ocr-model-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Ransaka/sinhala-ocr-model-v3 with Transformers:

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

pipe = pipeline("image-text-to-text", model="Ransaka/sinhala-ocr-model-v3")

# Load model directly
from transformers import AutoTokenizer, AutoModelForImageTextToText

tokenizer = AutoTokenizer.from_pretrained("Ransaka/sinhala-ocr-model-v3")
model = AutoModelForImageTextToText.from_pretrained("Ransaka/sinhala-ocr-model-v3")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Ransaka/sinhala-ocr-model-v3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Ransaka/sinhala-ocr-model-v3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Ransaka/sinhala-ocr-model-v3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Ransaka/sinhala-ocr-model-v3

SGLang

How to use Ransaka/sinhala-ocr-model-v3 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 "Ransaka/sinhala-ocr-model-v3" \
    --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": "Ransaka/sinhala-ocr-model-v3",
		"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 "Ransaka/sinhala-ocr-model-v3" \
        --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": "Ransaka/sinhala-ocr-model-v3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Ransaka/sinhala-ocr-model-v3 with Docker Model Runner:
```
docker model run hf.co/Ransaka/sinhala-ocr-model-v3
```

sinhala-ocr-model-v3

This model is a fine-tuned version of Ransaka/sinhala-ocr-model on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 4.7242
Cer: 0.2764

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: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 16
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
training_steps: 6000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Cer
3.6711	6.54	500	4.9311	0.4178
2.3499	13.07	1000	4.5366	0.3482
1.5601	19.61	1500	4.4634	0.3204
0.987	26.14	2000	4.4804	0.3011
0.6487	32.68	2500	4.6310	0.2863
0.3816	39.22	3000	4.6093	0.2788
0.3494	45.75	3500	4.6291	0.2827
0.2357	52.29	4000	4.6399	0.2780
0.2188	58.82	4500	4.6313	0.2798
0.1413	65.36	5000	4.6828	0.2768
0.0985	71.9	5500	4.7135	0.2772
0.1086	78.43	6000	4.7242	0.2764

Framework versions

Transformers 4.35.2
Pytorch 2.0.0
Datasets 2.16.0
Tokenizers 0.15.0

Downloads last month: 8

Safetensors

Model size

0.3B params

Tensor type

F32

Inference Providers NEW

Image-Text-to-Text

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