mLateOn

About

What mLateOn is

[](https://lighton.ai) [](https://www.linkedin.com/company/lighton/) [](https://x.com/LightOnIO) 📚 Collection | 📝 Multilingual Blog | 📝 English Blog | 📝 Paper 🎯 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. About the mDenseOn / mLateOn Family 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. For more information, please read our multilingual models blog post, our English models blog post and our paper. mLateOn mLateOn is a multilingual ColBERT (multi-vector) retrieval model built on mmBERT-base (307M parameters), trained by LightOn using PyLate. It supports context lengths of up to 8,192 tokens for documents and queries, using MaxSim scoring. mLateOn notably: - Achieves the highest BEIR score among all evaluated models at 57.56 NDCG@10, surpassing even the English-only LateOn (57.22) and dense baselines up to twice its size, showing that multilingual training can actually improve English performance. - Leads all models on MIRACL target languages at 65.61, and remains competitive on the full benchmark (67.04) despite being trained on only nine languages, outperforming most models trained on many more languages. - Opens a massive gap on MLDR at 87.69 on target la

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

Specifications

What it is

How it works

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

$ curl -sS https://api.axforge.ai/v1/embeddings \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"mlateon","input":"text to embed"}'

Details

Languages, data & research

Papers

Licence

Open weights

Sources

Weights & code

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