Sylvain Gugger
Former core maintainer of Hugging Face Transformers and co-author of Deep Learning for Coders
Sylvain Gugger's GitHub profile lists past work at Hugging Face and fast.ai. Hugging Face's LLM Course describes Gugger as a Research Engineer at Hugging Face and one of the core maintainers of the Transformers library, and as a former Research Scientist at fast.ai who co-wrote Deep Learning for Coders with fastai and PyTorch with Jeremy Howard. Gugger is a co-author of the paper that introduced the Transformers library and is listed first in the citation for Accelerate, Hugging Face's thin wrapper for running PyTorch training loops on multiple GPUs, TPUs, or mixed precision without boilerplate code.1235
Work
TransformersOpen-source code
Co-author of the 2019 paper introducing Hugging Face's Transformers library (arXiv 1910.03771), and described on Hugging Face's LLM Course page as one of the library's core maintainers. The repository is released under the Apache 2.0 license.324
Accelerate (external site: github.com)Open-source code
Listed first among the authors in the citation entry of the Accelerate README (2022). The Apache-2.0 library lets people who write their own PyTorch training loops run them in distributed or mixed-precision setups.5
Deep Learning for Coders with fastai and PyTorchPublished research
Co-wrote the book with Jeremy Howard, according to the author biographies on Hugging Face's LLM Course page.2
Other documented roles
Roles the sources document but that are not confirmed as current.
- Research Engineer and one of the core maintainers of the Transformers library, Hugging Face21
- Research Scientist, fast.ai21
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