activity
20202026
most citedThe RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data, and Web Data Only

157 citations · 340 across the 7 of their papers we have counts for

collaborators

7 papers

cs.AI2026

Laguna M.1/XS.2 Technical Report

Julien Abadji, Marah Abdin, Connor Adams +93

We present Laguna M.1 and Laguna XS.2, two Mixture-of-Experts foundation models built for long-horizon, agentic coding: M.1 has B total parameters (B activated per tok…

cs.CL2024★ 2 cited

Falcon2-11B Technical Report

Quentin Malartic, Nilabhra Roy Chowdhury, Ruxandra Cojocaru +14

We introduce Falcon2-11B, a foundation model trained on over five trillion tokens, and its multimodal counterpart, Falcon2-11B-vlm, which is a vision-to-text model. We report our f…

cs.CL2023★ 117 cited

The Falcon Series of Open Language Models

Ebtesam Almazrouei, Hamza Alobeidli, Abdulaziz Alshamsi +11

We introduce the Falcon series: 7B, 40B, and 180B parameters causal decoder-only models trained on a diverse high-quality corpora predominantly assembled from web data. The largest…

cs.CL2023★ 157 cited

The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data, and Web Data Only

Guilherme Penedo, Quentin Malartic, Daniel Hesslow +6

Large language models are commonly trained on a mixture of filtered web data and curated high-quality corpora, such as social media conversations, books, or technical papers. This…

stat.ML2021★ 29 cited

State, global and local parameter estimation using local ensemble Kalman filters: applications to online machine learning of chaotic dynamics

Quentin Malartic, Alban Farchi, Marc Bocquet

In a recent methodological paper, we showed how to learn chaotic dynamics along with the state trajectory from sequentially acquired observations, using local ensemble Kalman filte…

stat.ML2021

A comparison of combined data assimilation and machine learning methods for offline and online model error correction

Alban Farchi, Marc Bocquet, Patrick Laloyaux +2

Recent studies have shown that it is possible to combine machine learning methods with data assimilation to reconstruct a dynamical system using only sparse and noisy observations…

stat.ML2020★ 35 cited

Online learning of both state and dynamics using ensemble Kalman filters

Marc Bocquet, Alban Farchi, Quentin Malartic

The reconstruction of the dynamics of an observed physical system as a surrogate model has been brought to the fore by recent advances in machine learning. To deal with partial and…