Armand Joulin
Lead of the Gemma 2 and Gemma 3 reports at Google DeepMind; co-author of Meta's LLaMA and the fastText papers
- Gemma Team (lead on the Gemma 2 and Gemma 3 reports; core contributor on the July 2026 Gemma 4 report), Google DeepMind234
Armand Joulin is listed as the lead in the contributor lists of the Gemma 2 (2024) and Gemma 3 (2025) technical reports from Google DeepMind's Gemma Team, and among the leads of the first Gemma report (2024). The July 2026 Gemma 4 Technical Report from the same team lists Joulin among its core contributors and says the Gemma 4 models are released under an Apache 2.0 license. Earlier, Joulin was first author of Bag of Tricks for Efficient Text Classification (2016), written at Facebook AI Research, and of FastText.zip, two of the three papers listed in the references of the MIT-licensed fastText repository. Joulin is also a co-author of LLaMA: Open and Efficient Foundation Language Models (2023), written at Meta AI.1234567
Work
GemmaOpen weights
Listed among the leads of the Gemma (2024) report and as the lead of the Gemma 2 (2024) and Gemma 3 (2025) reports, and among the core contributors of the Gemma 4 Technical Report (2026); the Gemma 4 report says those models are released under an Apache 2.0 license.1234
LLaMAPublished research
Co-author of LLaMA: Open and Efficient Foundation Language Models (2023), whose author list gives a Meta AI affiliation and whose abstract says all models were released to the research community.7
fastText (external site: github.com)Open-source code
First author of Bag of Tricks for Efficient Text Classification (2016) and of FastText.zip, and co-author of Enriching Word Vectors with Subword Information, the three papers listed in the references of the MIT-licensed fastText repository (archived in 2024).65
Other documented roles
Roles the sources document but that are not confirmed as current.
- Author listed with Facebook AI Research (2016) and Meta AI (2023), Meta (Facebook AI Research and Meta AI)57
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