Mungert/LightOnOCR-1B-1025-GGUF

LightOnOCR-1B-1025 GGUF Models

Model Generation Details

This model was generated using llama.cpp at commit 16724b5b6.

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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.

📝 Read the full blog post | 🚀 Try the demo

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

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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.

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Benchmarks

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 on the Olmo-Bench.

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Installation

uv venv --python 3.12 --seed
source .venv/bin/activate

uv pip install -U vllm \
    --torch-backend=auto \
    --extra-index-url https://wheels.vllm.ai/nightly \
    --prerelease=allow

# if this fails try adding triton-kernels package
'triton-kernels @ git+https://github.com/triton-lang/[email protected]#subdirectory=python/triton_kernels'
uv pip install pypdfium2 pillow requests

Start Server

vllm serve lightonai/LightOnOCR-1B-1025 \
    --limit-mm-per-prompt '{"image": 1}' \
    --async-scheduling

PDF Inference

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)

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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

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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

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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

Example fine-tuning configurations will be released alongside the dataset.

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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.

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License

Apache License 2.0

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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}}
}

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🚀 If you find these models useful

Help me test my AI-Powered Quantum Network Monitor Assistant with quantum-ready security checks:

👉 Quantum Network Monitor

The full Open Source Code for the Quantum Network Monitor Service available at my github repos ( repos with NetworkMonitor in the name) : Source Code Quantum Network Monitor. You will also find the code I use to quantize the models if you want to do it yourself GGUFModelBuilder

💬 How to test:
Choose an AI assistant type:

• TurboLLM (GPT-4.1-mini)
• HugLLM (Hugginface Open-source models)
• TestLLM (Experimental CPU-only)

What I’m Testing

I’m pushing the limits of small open-source models for AI network monitoring, specifically:

• Function calling against live network services
• How small can a model go while still handling:
  • Automated Nmap security scans
  • Quantum-readiness checks
  • Network Monitoring tasks

🟡 TestLLM – Current experimental model (llama.cpp on 2 CPU threads on huggingface docker space):

• ✅ Zero-configuration setup
• ⏳ 30s load time (slow inference but no API costs) . No token limited as the cost is low.
• 🔧 Help wanted! If you’re into edge-device AI, let’s collaborate!

Other Assistants

🟢 TurboLLM – Uses gpt-4.1-mini :

• **It performs very well but unfortunatly OpenAI charges per token. For this reason tokens usage is limited.
• Create custom cmd processors to run .net code on Quantum Network Monitor Agents
• Real-time network diagnostics and monitoring
• Security Audits
• Penetration testing (Nmap/Metasploit)

🔵 HugLLM – Latest Open-source models:

• 🌐 Runs on Hugging Face Inference API. Performs pretty well using the lastest models hosted on Novita.

💡 Example commands you could test:

1. "Give me info on my websites SSL certificate"
2. "Check if my server is using quantum safe encyption for communication"
3. "Run a comprehensive security audit on my server"
4. '"Create a cmd processor to .. (what ever you want)" Note you need to install a Quantum Network Monitor Agent to run the .net code on. This is a very flexible and powerful feature. Use with caution!

Final Word

I fund the servers used to create these model files, run the Quantum Network Monitor service, and pay for inference from Novita and OpenAI—all out of my own pocket. All the code behind the model creation and the Quantum Network Monitor project is open source. Feel free to use whatever you find helpful.

If you appreciate the work, please consider buying me a coffee ☕. Your support helps cover service costs and allows me to raise token limits for everyone.

I'm also open to job opportunities or sponsorship.

Thank you! 😊