DenseOn

About

What DenseOn is

[](https://lighton.ai) [](https://www.linkedin.com/company/lighton/) [](https://x.com/LightOnIO) 📚 Collection | 📝 Blog 🎯 TL;DR: A 149M-parameter dense (single-vector) retrieval model achieving 56.20 nDCG@10 on BEIR, the first sub-150M dense model to break the 56 mark, topping all base-size dense models and outperforming several models 4× larger. About the LateOn / DenseOn Family State-of-the-art retrieval is increasingly dominated by closed models, either hidden behind APIs or trained on undisclosed data. This blocks reproducibility, prevents study of possible data leakage, and gatekeeps progress to a handful of private labs. We thus decided to gather and curate a large amount of data and explore various mixtures. We release all the data used in our explorations: - Gathered pre-training data, 1.4B query-documents pairs alongside annotations used for non-destructive filtering (structural filtering, deduplication, cross-encoder pair relevancy) - Best pre-training mixture found with already applied filters - Fine-tuning datasets with query, positive and 2048 mined documents alongside their scores for 1.88M samples. Based on our findings, we trained LateOn (multi-vector/ColBERT) and DenseOn (single vector/dense) models on a proprietary Apache 2.0-compatible training dataset and release those models as well. Both are built on the ModernBERT backbone at 149M parameters, a size we believe sits at the sweet spot: large enough to handle real-world queries and documents, small enough to serve at high throughput in latency-sensitive production systems. For more information, please read our blogpost. DenseOn DenseOn is a dense (single-vector) retrieval model built on ModernBERT (149M parameters), trained by LightOn. It encodes queries and documents independently using cosine similarity with query:/document: prefixes and CLS pooling. DenseOn achieves 56.75 average NDCG@10 on BEIR (15 datasets) and 57.71 on decontaminated BEIR (12 datasets), topping all base-size dense models and outperforming models up to 4x larger. Notably it: - Tops all base-size dense models on BEIR, ahead of GTE-ModernBERT (55.19) and on par with much larger Snowflake Arctic Embed L v2 (55.22, 568M) and

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 denseon for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (denseon 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":"denseon","input":"text to embed"}'

Details

Languages, data & research

Papers

Licence

Open weights

Sources

Weights & code

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