Translated 发布 Lara Mini base：用 MoE 架构让 45 种语言共存于小模型

Authors:Translated, Maria Pia Adinolfi, Isabelle Andrieu, Grazia Fiorella Anfuso, Melania Annunziata, Daniele Antonini, Marta Arena, Luca Barone, Dana Barras Franco, Francesca Basiricò, Francesca Bee, Sofia Bertini, Nicola Bertoldi, Giorgio Biancini, Alessandra Bigi, Giulia Boccalini, Tommaso Bonomo, Enrico Boscardi, Sebastien Bratieres, Francesco Brigante, Giulia Brignola, Romolo Buondestino, Brenda Patricia Byrne, Giorgia Campora, Edoardo Cappelli, Veronica Carascosi, Francesca Carducci, Cody Carlson, Davide Caroselli, Ambra Cascio, Dario Cascio, Mauro Cassani, Andrea Castro, Alessandro Cattelan, Giulia Ceccacci, Paola Cellini, Giulia Chiriatti, Federico Cialini, Valentina Ciarlariello, Daniele Cocozza, Emanuela Coghe, Kathleen Compares, Marco Comunità, Davide Conti, Helen Rebecca Corfield, Michele Cortellessa, Anna Cracco, Aurora Cuppone, Arianna D'Andrea, Maurizio Davoli, Andrea De Angelis, Andrea De Blasi, Rebecca De Giorgi, Annalisa De Petra, Arianna De Pilla, Joris Demiraj, Elena Deufemia, Federica Di Antonio, Pierluigi Di Cianni, Vincenzo Di Cicco, Michela Di Clemente, Alan Di Francesco, Ylenia Di Gese, Federica Di Iulio, Claudia Di Lorenzo, Andrea Di Paola, Giulia Di Pilla, Jessica Dickson, Francesco Elefante, Ilaria Fabrizio, Alessandro Falappa, Viviana Fantozzi, Antonio Farina, Andrea Fernandez Tostado Ro.Diego Andrea, Andrea Ferrari, Sara Ferri, Simona Ferri, Fabio Fioramonti, Stefano Florio, Mariachiara Fornaro, Anna Maria Foti, Niccolò Fransoni, Alessandro Fusacchia, Emma Gamba, Francesco Gasperoni, Davide Gattini, Marta Giani, Claudia Giannetti, Valerio Giannini, Antonella Giudici, Mara Gramendola, Michele Grandinetti, Eleonora Graziani, L'Meese Hope Greaney, Dario Gubernari, Filomena Guglielmi, Jonathan Hammond, Stephen William Harris, Rowena Elizabeth Hogg, Marta Hontanon Del Olmo

et al. (106 additional authors not shown)

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Abstract:General-purpose language models are trained mostly on English: over open models we review, at most, non-English pretraining data makes up only a 12% share. Models built for European languages give them far more, but score well below general-purpose models of the same size. A common concern is that, in small models, languages compete for capacity. We present Lara Mini base, a model trained from scratch on 6T tokens in English and 44 other languages, which together cover all 24 official languages of the European Union. As training progresses, the 44 other languages together come to outweigh English. To give these languages enough capacity at low cost, Lara Mini base uses a mixture of experts architecture with 7.2B total and 0.6B active parameters. Among open base models with up to 2B active parameters, it achieves the highest average score across standard English, multilingual, math, and code benchmarks, ahead of models trained on 4 to 6 times as many tokens. Its margin is largest on multilingual benchmarks in the languages it supports and on math benchmarks. It supports contexts of up to 128k tokens. We find that our MoE architecture reduces capacity competition between languages. For a single user on one GPU, without quantization, it generates text at least 1.8 times faster than any model with a higher average score. We release the weights of Lara Mini base under the CC BY-NC-SA 4.0 license. In addition, we will release T-HellaSwag, a higher-quality machine translation of the commonsense reasoning benchmark HellaSwag into 110 language variants.

Submission history

From: Marco Trombetti [view email]
[v1] Thu, 12 Nov 2026 15:28:56 UTC (101 KB)