Research And Development

MonoQwen-Vision, the first visual document reranker

We introduce MonoQwen2-VL-v0.1, the first visual document reranker to enhance the quality of the retrieved visual documents and take these pipelines to the next level. Reranking a small number of candidates with MonoQwen2-VL-v0.1 achieve top results on the ViDoRe leaderboard.

DuckSearch: search through Hugging Face datasets

DuckSearch is a lightweight Python library built on DuckDB, designed for efficient document search and filtering with Hugging Face datasets and standard documents.

FC-AMF-OCR Dataset : LightOn releases a 9.3 million images OCR dataset to improve real world document parsing

With over 9.3 million annotated images, this dataset offers researchers and AI developers a valuable resource for creating models adapted to real world documents.

PyLate: Flexible Training and Retrieval for ColBERT Models

We release PyLate, a new user-friendly library for training and experimenting with ColBERT models, a family of models that exhibit strong retrieval capabilities on out-of-domain data.

ArabicWeb24: Creating a high quality Arabic Web-only pre-training dataset

Training Mamba Models on AMD MI250/MI250X GPUs with Custom Kernels

In this blogpost we show how we can train a Mamba model interchangeably on both NVIDIA and AMD and we compare both training performance and convergence in both cases. This shows that our training stack is becoming more GPU-agnostic.

Transforming LLMs into Agents for Enterprise Automation

Developing Agentic Capabilities for LLMs to automate business workflows and create smart assistants.

Passing the Torch: Training a Mamba Model for Smooth Handover

We present our explorations on training language models based on the new Mamba architecture, which deviates from the traditional Transformer architecture.

LightOn AI Meetup Creating a Large Dataset for Pretraining LLMs