LightOnOCR-1025

About

What LightOnOCR-1025 is

[!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. [](https://colab.research.google.com/#fileId=https%3A//huggingface.co/lightonai/LightOnOCR-1B-1025/blob/main/notebook.ipynb) 📄 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 🚀 Start Server PDF Inference Transformers Note: LightOnOCR-2 requires transformers installed from source (not yet in a stable release). 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 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

Summarised from the published model card. Read the full card on the HuggingFace links below.

Specifications

What it is

How it works

How language models work

Variants

Sizes & precisions

Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys lightonocr-1025 for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (lightonocr-1025 below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/chat/completions \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"lightonocr-1025","messages":[{"role":"user","content":"Hello"}]}'

Details

Languages, data & research

Papers

Licence

Open weights

Sources

Weights & code

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