Instructions to use Serialtechlab/paligemma2-dhivehi-ocr-full with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Serialtechlab/paligemma2-dhivehi-ocr-full with Transformers:

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

pipe = pipeline("image-text-to-text", model="Serialtechlab/paligemma2-dhivehi-ocr-full")

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("Serialtechlab/paligemma2-dhivehi-ocr-full")
model = AutoModelForImageTextToText.from_pretrained("Serialtechlab/paligemma2-dhivehi-ocr-full")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Serialtechlab/paligemma2-dhivehi-ocr-full with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Serialtechlab/paligemma2-dhivehi-ocr-full"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Serialtechlab/paligemma2-dhivehi-ocr-full",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Serialtechlab/paligemma2-dhivehi-ocr-full

SGLang

How to use Serialtechlab/paligemma2-dhivehi-ocr-full 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/paligemma2-dhivehi-ocr-full" \
    --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/paligemma2-dhivehi-ocr-full",
		"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/paligemma2-dhivehi-ocr-full" \
        --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/paligemma2-dhivehi-ocr-full",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Serialtechlab/paligemma2-dhivehi-ocr-full with Docker Model Runner:
```
docker model run hf.co/Serialtechlab/paligemma2-dhivehi-ocr-full
```

paligemma2-dhivehi-ocr-full / README.md

Serialtechlab

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metadata

license: apache-2.0
language:
  - dv
tags:
  - ocr
  - dhivehi
  - thaana
  - paligemma
  - vision-language
  - text-recognition
base_model: google/paligemma2-3b-pt-224
datasets:
  - alakxender/dhivehi-vrd-images
metrics:
  - accuracy
library_name: transformers

paligemma2-dhivehi-ocr-full

Model Description

This is a fine-tuned PaliGemma model for Dhivehi (Thaana script) Optical Character Recognition (OCR). The model has been merged from a LoRA adapter into a standalone model for easy deployment.

Original adapter: alakxender/paligemma2-qlora-dhivehi-ocr-224-sl-md-16k
Base model: google/paligemma2-3b-pt-224
Merged on: 2025-06-29 09:02:20

Capabilities

Extract Dhivehi/Thaana text from images
Handle both single-line and multi-line text
Optimized for printed Dhivehi text recognition
Works with various image formats and qualities

Usage

import torch
from PIL import Image
from transformers import AutoProcessor, PaliGemmaForConditionalGeneration

# Load the merged model (no base model loading required!)
model_id = "Serialtechlab/paligemma2-dhivehi-ocr-full"
model = PaliGemmaForConditionalGeneration.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)
processor = AutoProcessor.from_pretrained(model_id)

# Load your image
image = Image.open("your_image.png")

# Prepare inputs
prompt = "<image>What text is written in this image?"
inputs = processor(text=prompt, images=image, return_tensors="pt")

# Move to GPU
for k, v in inputs.items():
    if k == "pixel_values":
        inputs[k] = v.to(torch.bfloat16).to("cuda")
    else:
        inputs[k] = v.to("cuda")

# Generate
with torch.inference_mode():
    outputs = model.generate(
        **inputs,
        max_new_tokens=500,
        do_sample=False
    )

# Decode result
result = processor.batch_decode(outputs, skip_special_tokens=True)[0]
dhivehi_text = result.replace(prompt, "").strip()
print(f"Extracted text: " + dhivehi_text)

Model Details

Architecture: PaliGemma (Vision-Language Model)
Fine-tuning: LoRA (Low-Rank Adaptation)
Training data: Dhivehi text images
Language: Dhivehi (Thaana script)
Model size: ~5.9GB (merged weights)

Performance

This model provides accurate Dhivehi text extraction from images with good performance on:

Printed text
Various font sizes
Different image qualities
Single and multi-line text layouts

Limitations

Optimized for printed text (handwritten text may have lower accuracy)
Performance depends on image quality and text clarity
Best results with high-contrast, clear images

Training Details

Base model: google/paligemma2-3b-pt-224
Fine-tuning method: LoRA (Low-Rank Adaptation)
Target modules: Vision and language model layers
Rank: 16
Alpha: 32

Citation

If you use this model, please cite:

@misc{dhivehi-ocr-paligemma,
  title={Dhivehi OCR with PaliGemma},
  author={Serialtechlab},
  year={2024},
  howpublished={\url{https://huggingface.co/Serialtechlab/paligemma2-dhivehi-ocr-full}}
}

License

This model is released under the Apache 2.0 license, following the base model's licensing terms.