Model Overview
LightOnOCR combines a Vision Transformer encoder(Pixtral-based) with a lightweight text decoder(Qwen3-based) distilled from high-quality open VLMs.
It is optimized for document parsing tasks, producing accurate, layout-aware text extraction from high-resolution pages.
Benchmarks
Table with columns: Model, ArXiv, Old Scans, Math, Tables, Multi-Column, Tiny Text, Base, Overall| Model | ArXiv | Old Scans | Math | Tables | Multi-Column | Tiny Text | Base | Overall |
|---|
| LightOnOCR-1B-1025 (151k vocab) | 81.4 | 71.6 | 76.4 | 35.2 | 80.0 | 88.7 | 99.5 | 76.1 |
| LightOnOCR-1B-32k (32k vocab) | 80.6 | 66.2 | 73.5 | 33.5 | 71.2 | 87.6 | 99.5 | 73.1 |
| LightOnOCR-1B-16k (16k vocab) | 82.3 | 72.9 | 75.3 | 33.5 | 78.6 | 85.1 | 99.8 | 75.4 |
All benchmarks evaluated using vLLM.
Installation
[2025/11/24] 🚀 LightOnOCR is now officially supported in vLLM v0.11.1 🚀
uv venv --python 3.12 --seed
source .venv/bin/activate
# install any version higher than 0.11.1
uv pip install vllm==0.11.2
# extra deps need only to run the example below
uv pip install pypdfium2 pillow requests
Start Server
vllm serve lightonai/LightOnOCR-0.9B-16k-1025 \
--limit-mm-per-prompt '{"image": 1}'
PDF Inference
import base64
import requests
import pypdfium2 as pdfium
import io
ENDPOINT = "http://localhost:8000/v1/chat/completions"
MODEL = "lightonai/LightOnOCR-0.9B-16k-1025"
pdf_url = "https://arxiv.org/pdf/2412.13663"
pdf_data = requests.get(pdf_url).content
pdf = pdfium.PdfDocument(pdf_data)
page = pdf[0]
pil_image = page.render(scale=2.77).to_pil()
buffer = io.BytesIO()
pil_image.save(buffer, format="PNG")
image_base64 = base64.b64encode(buffer.getvalue()).decode('utf-8')
payload = {
"model": MODEL,
"messages": [{
"role": "user",
"content": [{
"type": "image_url",
"image_url": {"url": f"data:image/png;base64,{image_base64}"}
}]
}],
"max_tokens": 4096,
"temperature": 0.2,
"top_p": 0.9,
}
response = requests.post(ENDPOINT, json=payload)
text = response.json()['choices'][0]['message']['content']
print(text)
Rendering and Preprocessing Tips
- Render PDFs to PNG or JPEG at a target longest dimension of 1280–1300 px
- Maintain aspect ratio to preserve text geometry
- LightOnOCR is robust to moderate skew; heavy rotation correction is optional
- Use one image per page; batching supported by vLLM
Variants
Table with columns: Variant, Description| Variant | Description |
|---|
| LightOnOCR-1B-1025 | Full multilingual model (default) |
| LightOnOCR-1B-32k | Fastest pruned-vocabulary version (32k tokens) optimized for European languages |
| LightOnOCR-1B-16k | Most compact variant with smallest vocabulary |
Fine-tuning
Transformers integration is coming soon for training.
LightOnOCR is fully differentiable and supports:
- LoRA
- Domain adaptation (receipts, scientific articles, forms, etc.)
- Multilingual fine-tuning with task-specific corpora
Example fine-tuning configurations will be released alongside the dataset.
Data
Trained on a diverse large-scale PDF corpus covering:
- Scientific papers, books, receipts, invoices, tables, forms, and handwritten text
- Multiple languages (Latin alphabet dominant)
- Real and synthetic document scans
The dataset will be released under an open license.
License
Apache License 2.0
Acknowlegments
The project received funding from the BPI Scribe project.
Citation
@misc{lightonocr2025,
title = {LightOnOCR-1B: End-to-End and Efficient Domain-Specific Vision-Language Models for OCR},
author = {Said Taghadouini and Baptiste Aubertin and Adrien Cavaillès},
year = {2025},
howpublished = {\url{https://huggingface.co/blog/lightonai/lightonocr}}
}
