Guillaume Lample
Co-founder Chief Scientist at Mistral AI
Paris, Ile-de-France
Summary
🤩
Rockstar🎓
Top SchoolGuillaume Lample is a co-founder and Chief Scientist at Mistral AI with a decade of experience translating deep research into production-ready ML systems. Formerly a research scientist and PhD student at Facebook AI Research, he worked on unsupervised machine translation, cross-lingual pretraining and research in symbolic mathematics and theorem proving. His open-source contributions include improvements to the PyTorch XLM implementation and MUSE, where he focused on data pipelines, BPE preprocessing, UTF-8 compatibility fixes and training optimizations like gradient clipping and multi-node support. Based in Paris and educated at École Polytechnique, Carnegie Mellon and Pierre & Marie Curie University, he blends strong theoretical foundations with hands-on engineering that scales multilingual models.
11 years of coding experience
3 years of employment as a software developer
Master's degree, Mathématiques et informatique, Master's degree, Mathématiques et informatique at Ecole polytechniqueMaster's degree, Intelligence artificielle, Master's degree, Intelligence artificielle at Carnegie Mellon UniversityDoctor of Philosophy - PhD, Artificial Intelligence, Doctor of Philosophy - PhD, Artificial Intelligence at Pierre and Marie Curie University
Github Skills (16)
data-preprocessing10
pytorch10
machine-translation10
machine-learning10
bash10
nlp10
python10
natural-language-processing10
utf-89
data-pipelines9
data-pipeline9
utf89
data-loading9
script8
git8
Programming languages (7)
Github contributions (5)
facebookresearch/MUSE
Jan 2018 - Feb 2019
A library for Multilingual Unsupervised or Supervised word Embeddings
Role in this project:
Back-end Developer & DevOps Engineer
Back-end Developer & DevOps EngineerContributions:32 commits, 6 PRs, 33 pushes in 1 year 2 months
Contributions summary:Guillaume contributed to bug fixes, particularly in the `src/utils.py` file, addressing issues related to recentering. The user implemented UTF-8 encoding across multiple files, enhancing compatibility. They also added an experiment name and removed dead code. Additionally, they made adjustments to the embedding export functionality and made several build and library upgrades.
nlpsupervisedword-embeddingsembeddingsunsupervised
facebookresearch/XLM
Feb 2019 - Aug 2019
PyTorch original implementation of Cross-lingual Language Model Pretraining.
Role in this project:Back-end Developer
Back-end DeveloperContributions:26 commits, 12 PRs, 47 pushes in 6 months
Contributions summary:Guillaume primarily contributed to data pipeline fixes, and evaluation scripts. The commits included modifications to data loading scripts related to the XNLI dataset and updates to the GLUE evaluation scripts. Additionally, the user implemented the feature of gradient clipping and support for split training data loading, indicating a focus on optimizing model training and data handling. The user also made modifications to the training process, including causal prediction context support and multi-node job termination.
pytorchnlplanguage-modeldeep-learningcross-lingual
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