LateOn-unsupervised
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LightOn-rerank-LW-2B
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View details ↗LightOn-rerank-LW-4B
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View details ↗LightOn-rerank-PW-2B
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View details ↗LightOn-rerank-PW-4B
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View details ↗LightOnOCR-0.9B-16k-1025
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View details ↗LightOnOCR-0.9B-32k-1025
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View details ↗LightOnOCR-1B-1025
lightonai> [!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** 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.
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View details ↗LightOnOCR-2-1B
lightonai📄 Paper | 📝 Blog | 🚀 Demo | 📊 Dataset | 📓 Finetuning **Best OCR model .** LightOnOCR-2-1B is **LightOn's** flagship OCR model, refined with RLVR training for maximum accuracy. We recommend this variant for most OCR tasks. LightOnOCR-2 is an efficient end-to-end 1B-parameter vision-language model for converting documents (PDFs, scans, images) into clean, naturally ordered text without relying on brittle pipelines. This second version is trained on a larger and higher-quality corpus with stronger French, arXiv, and scan coverage, improved LaTeX handling, and cleaner normalization. LightOnOCR-2 achieves state-of-the-art performance on OlmOCR-Bench while being ~9× smaller and significantly faster than competing approaches.
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View details ↗LightOnOCR-2-1B-base
lightonai**Base model for fine-tuning.** This is the pre-RLVR checkpoint with strong OCR capabilities, ideal as a starting point for domain adaptation and custom fine-tuning. LightOnOCR-2 is an efficient end-to-end 1B-parameter vision-language model for converting documents (PDFs, scans, images) into clean, naturally ordered text without relying on brittle pipelines. This second version is trained on a larger and higher-quality corpus with stronger French, arXiv, and scan coverage, improved LaTeX handling, and cleaner normalization. LightOnOCR-2 achieves state-of-the-art performance on OlmOCR-Bench while being ~9× smaller and significantly faster than competing approaches.
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View details ↗LightOnOCR-2-1B-bbox
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View details ↗LightOnOCR-2-1B-bbox-soup
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View details ↗LightOnOCR-2-1B-ocr-soup
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View details ↗LightOnOCR-3-0.8B
lightonai**Smallest model.** LightOnOCR-3-0.8B is the lightest model of **LightOn's** LightOnOCR-3 family, with the same layout and visual understanding features. LightOnOCR-3 is a new family of highly performant lightweight OCR models. Compared to the previous generation, they bring significant improvements in speed and transcription quality and introduce new visual understanding features: the models now output bounding box coordinates with labels for all visual elements of a document, short descriptions of images, and the numerical data of figures and charts. With these capabilities, they offer a ready-to-use, easier-to-maintain alternative to complex document understanding pipelines. The models come in three sizes. The 1B keeps the LightOnOCR-2-1B architecture, while the 0.8B and 4B adopt the Qwen3.5 vision-language architecture, which simplifies integration with existing tools and brings a significant speed-up. All models are released under the Apache 2.0 license for research and commercial use.
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View details ↗LightOnOCR-3-1B
lightonai**Same architecture as LightOnOCR-2-1B, new capabilities.** LightOnOCR-3-1B keeps the architecture of **LightOn's** previous LightOnOCR-2 models, so switching requires no change, and adds the new grounding, image description and chart extraction features. LightOnOCR-3 is a new family of highly performant lightweight OCR models. Compared to the previous generation, they bring significant improvements in speed and transcription quality and introduce new visual understanding features: the models now output bounding box coordinates with labels for all visual elements of a document, short descriptions of images, and the numerical data of figures and charts. With these capabilities, they offer a ready-to-use, easier-to-maintain alternative to complex document understanding pipelines. The models come in three sizes. The 1B keeps the LightOnOCR-2-1B architecture, while the 0.8B and 4B adopt the Qwen3.5 vision-language architecture, which simplifies integration with existing tools and brings a significant speed-up. All models are released under the Apache 2.0 license for research and commercial use.
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View details ↗LightOnOCR-3-4B
lightonai**Best OCR model.** LightOnOCR-3-4B is the largest and most accurate model of the LightOnOCR-3 family, **LightOn's** new generation of lightweight end-to-end OCR models with layout and visual understanding. We recommend it for most OCR tasks. LightOnOCR-3 is a new family of highly performant lightweight OCR models. Compared to the previous generation, they bring significant improvements in speed and transcription quality and introduce new visual understanding features: the models now output bounding box coordinates with labels for all visual elements of a document, short descriptions of images, and the numerical data of figures and charts. With these capabilities, they offer a ready-to-use, easier-to-maintain alternative to complex document understanding pipelines. The models come in three sizes. The 1B keeps the LightOnOCR-2-1B architecture, while the 0.8B and 4B adopt the Qwen3.5 vision-language architecture, which simplifies integration with existing tools and brings a significant speed-up. All models are released under the Apache 2.0 license for research and commercial use.
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View details ↗mDenseOn
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View details ↗mDenseOn-unsupervised
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View details ↗mLateOn
lightonai> 🎯 **TL;DR**: A 307M-parameter multilingual ColBERT (multi-vector) retrieval model achieving state-of-the-art results across **multilingual retrieval (MIRACL), long-document retrieval (MLDR), English general-domain retrieval (BEIR), and code retrieval (MTEB Code)**. Trained on nine languages only, yet generalizes to unseen languages and scripts, demonstrating that **late interaction enables translate-train viability** for broad multilingual coverage without exhaustive translation. With DenseOn and LateOn, we demonstrated that an open, carefully curated data recipe can match closed-data retrieval models on English. mDenseOn and mLateOn extend this recipe to multilingual, long-context, and code retrieval. Rather than independently collecting multilingual corpora from scratch (which would be expensive, uneven across languages, and hard to curate at the same quality), we applied the **translate-train** approach: machine-translating our validated English data into eight target languages (French, German, Italian, Spanish, Portuguese, Swedish, Norwegian, and Arabic) and adding cross-lingual pairs for cross-lingual alignment.
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View details ↗modernbert-embed-large
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View details ↗modernbert-embed-large-unsupervised
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View details ↗mSlideOn
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View details ↗Qwen3-8B-FR
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View details ↗Reason-ModernColBERT
lightonaiReason-ModernColBERT is a late interaction model trained on the reasonir-hq dataset. It achieves extremely competitive performance on the BRIGHT benchmark aimed at evaluating reasoning-intensive retrieval performance, outperforming all existing models up to 7B (more than 45 times its size) and even surprisingly improving performance of ReasonIR-8B (a 8B model trained on the same data) by more than 2.5 NDCG@10 on average on Stack Exchange splits. We attribute such strong results to late-interaction, see evaluation section. Unfortunately, since the ReasonIR data has been released under a cc-by-nc-4.0 license, we cannot release this model under an Apache 2.0 license. However, the authors of ReasonIR released code to generate the data. Anyone willing to reproduce the data could then easily reproduce this model under an Apache 2.0 license by running a fine-tuning lasting lower than 2 hours using this boilerplate. This is a multi-vector (ColBERT-style late interaction) embedding model finetuned from lightonai/GTE-ModernColBERT-v1 on the reasonir-hq dataset. It maps sentences & paragraphs to sequences of 128-dimensional dense vectors and can be used for semantic textual similarity using the MaxSim operator.
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