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LatestLightOn2
LightOnVision-Language

LightOn Releases OCR-2, a 1B Document AI Model

The new vision model from the Paris-based AI lab uses Mistral architecture to extract text and structure from complex documents like PDFs and forms.

Jan 16, 2026
NotableOther
LightOnOCR-2 1B

Parisian AI company LightOn has released LightOnOCR-2, a new 1-billion-parameter vision language model specialized in document understanding. The model is designed to perform optical character recognition (OCR) on complex documents, extracting not just text but also structural information.

Unlike simple OCR tools, LightOnOCR-2 is built to parse challenging layouts like tables, forms, and multi-column PDFs. This makes it suitable for enterprise automation tasks such as processing invoices or digitizing records, a domain often dominated by proprietary, API-gated services.

A Mistral-based Architecture

The model features a Transformer-based encoder-decoder architecture. In an interesting design choice, its decoder was initialized using a subset of weights from Mistral-7B-v0.1, allowing it to leverage the powerful language capabilities of the popular open model while maintaining a much smaller, more efficient footprint.

The complete model weights and code are available on the Hugging Face Hub for developers to download and use. It's released under a custom LightOnAI-OpenRAIL-M license, which permits commercial use but includes some use-case restrictions common to Responsible AI licenses.

Sources

  • lightonai/LightOnOCR-2-1B

    Hugging Face

    Visit
OlderBlack Forest Labs Releases 9B FLUX.2 Image ModelBlack Forest Labs · Text → Image · 10 months agoNewerZhipu AI Releases GLM-4.7-Flash MoE ModelZhipu AI · Text / LLM · 9 months ago

Get the model

Hugging Face

Specs

Parameters1B
Context window16K tokens
Size2 GB
PrecisionBF16
ArchitectureLightOnOCRForConditionalGeneration
LicenseOTHER
Downloads755.1K
Likes850

Can you run it?

Runs on just about anything.

  • BF16 (as published)

    Any modern laptop · 8 GB RAM

    3.9 GB
  • 8-bit

    Any modern laptop · 8 GB RAM

    2.6 GB
  • 4-bit

    Any modern laptop · 8 GB RAM

    2.3 GB
Your machine
GGUF builds
  • KV cache sized for 8,192 tokens of context; longer prompts need proportionally more.

Based on Hugging Face weights metadata. Assumes an 8K context and ~1 GB runtime overhead; actual needs vary. How we estimate


Modalities

Vision-Language

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