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lightonai/
LightOnOCR-1B-1025

Name: LightOnOCR-1B-1025
Availability: InStock

document-understanding

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[!NOTE] 🚀 LightOnOCR-2 is now available and state-of-the-art on OlmOCR-bench, with new image detection variants! Check it out here: lightonai/LightOnOCR-2-1B

LightOnOCR-1B-1025

Full BF16 version of the model. We recommend this variant for inference and further fine-tuning.

LightOnOCR-1B is a compact, end-to-end vision–language model for Optical Character Recognition (OCR) and document understanding. It achieves state-of-the-art accuracy in its weight class while being several times faster and cheaper than larger general-purpose VLMs.

Open In Colab

📄 Paper | 📝 Read the full blog post | 🚀 Try the demo | 📓 Finetuning notebook

Highlights

⚡ Speed: 5× faster than dots.ocr, 2× faster than PaddleOCR-VL-0.9B, 1.73× faster than DeepSeekOCR
💸 Efficiency: Processes 5.71 pages/s on a single H100 (~493k pages/day) for <$0.01 per 1,000 pages
🧠 End-to-End: Fully differentiable, no external OCR pipeline
🧾 Versatile: Handles tables, receipts, forms, multi-column layouts, and math notation
🌍 Compact variants: 32k and 16k vocab options for European languages

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

All benchmarks evaluated using vLLM on the Olmo-Bench.

Installation

VLLM

[2025/11/24] 🚀 LightOnOCR is now officially supported in vLLM v0.11.1 🚀

bash

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

bash

vllm serve lightonai/LightOnOCR-1B-1025 \
    --limit-mm-per-prompt '{"image": 1}' --mm-processor-cache-gb 0 --no-enable-prefix-caching

PDF Inference

python

import base64
import requests
import pypdfium2 as pdfium
import io

ENDPOINT = "http://localhost:8000/v1/chat/completions"
MODEL = "lightonai/LightOnOCR-1B-1025"

# Download PDF from arXiv
pdf_url = "https://arxiv.org/pdf/2412.13663"
pdf_data = requests.get(pdf_url).content

# Open PDF and convert first page to image
pdf = pdfium.PdfDocument(pdf_data)
page = pdf[0]
# Render at 200 DPI (scale factor = 200/72 ≈ 2.77)
pil_image = page.render(scale=2.77).to_pil()

# Convert to base64
buffer = io.BytesIO()
pil_image.save(buffer, format="PNG")
image_base64 = base64.b64encode(buffer.getvalue()).decode('utf-8')

# Make request
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)

Transformers

Note: LightOnOCR-2 requires transformers installed from source (not yet in a stable release).

uv pip install git+https://github.com/huggingface/transformers

python

import torch
from transformers import LightOnOcrForConditionalGeneration, LightOnOcrProcessor

device = "mps" if torch.backends.mps.is_available() else "cuda" if torch.cuda.is_available() else "cpu"
dtype = torch.float32 if device == "mps" else torch.bfloat16

model = LightOnOcrForConditionalGeneration.from_pretrained("lightonai/LightOnOCR-2-1B-base", torch_dtype=dtype).to(device)
processor = LightOnOcrProcessor.from_pretrained("lightonai/LightOnOCR-2-1B-base")

url = "https://huggingface.co/datasets/hf-internal-testing/fixtures_ocr/resolve/main/SROIE-receipt.jpeg"
conversation = [{"role": "user", "content": [{"type": "image", "url": url}]}]

inputs = processor.apply_chat_template(
    conversation,
    add_generation_prompt=True,
    tokenize=True,
    return_dict=True,
    return_tensors="pt",
)
inputs = {k: v.to(device=device, dtype=dtype) if v.is_floating_point() else v.to(device) for k, v in inputs.items()}

output_ids = model.generate(**inputs, max_new_tokens=1024)
generated_ids = output_ids[0, inputs["input_ids"].shape[1]:]
output_text = processor.decode(generated_ids, skip_special_tokens=True)
print(output_text)

Rendering and Preprocessing Tips

Render PDFs to PNG or JPEG at a target longest dimension of 1540px
Maintain aspect ratio to preserve text geometry
Use one image per page; batching supported by vLLM

Variants

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 and inference.

LightOnOCR is fully differentiable and supports:

LoRA fine-tuning
Domain adaptation (receipts, scientific articles, forms, etc.)
Multilingual fine-tuning with task-specific corpora

📓 Finetuning notebook

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}}
}