Portrait of Quentin Fournier

Quentin Fournier

quentin.fournier@mila.quebec
Research Fellow, Talent and Ecosystem

Publications

Protein Language Models: Is Scaling Necessary?
Quentin Fournier
Robert M. Vernon
Almer van der Sloot
Benjamin Schulz
Sarath Chandar
Christopher James Langmead
2024-09-23
bioRxiv (preprint)
doi.org
Exploring Quantization for Efficient Pre-Training of Transformer Language Models
Kamran Chitsaz
Quentin Fournier
Gonccalo Mordido
Sarath Chandar
The increasing scale of Transformer models has led to an increase in their pre-training computational requirements. While quantization has p… (see more)roven to be effective after pre-training and during fine-tuning, applying quantization in Transformers during pre-training has remained largely unexplored at scale for language modeling. This study aims to explore the impact of quantization for efficient pre-training of Transformers, with a focus on linear layer components. By systematically applying straightforward linear quantization to weights, activations, gradients, and optimizer states, we assess its effects on model efficiency, stability, and performance during training. By offering a comprehensive recipe of effective quantization strategies to be applied during the pre-training of Transformers, we promote high training efficiency from scratch while retaining language modeling ability. Code is available at https://github.com/chandar-lab/EfficientLLMs.
2024-07-16
ArXiv (preprint)
doi.org
arxiv.org
A Deep Dive into the Trade-Offs of Parameter-Efficient Preference Alignment Techniques
Megh Thakkar
Quentin Fournier
Matthew D Riemer
Pin-Yu Chen
Amal Zouaq
Payel Das
Sarath Chandar
2024-06-07
ArXiv (preprint)
doi.org
arxiv.org
Predicting the Impact of Model Expansion through the Minima Manifold: A Loss Landscape Perspective
Pranshu Malviya
Jerry Huang
Quentin Fournier
Sarath Chandar
The optimal model for a given task is often challenging to determine, requiring training multiple models from scratch which becomes prohibit… (see more)ive as dataset and model sizes grow. A more efficient alternative is to reuse smaller pre-trained models by expanding them, however, this is not widely adopted as how this impacts training dynamics remains poorly understood. While prior works have introduced statistics to measure these effects, they remain flawed. To rectify this, we offer a new approach for understanding and quantifying the impact of expansion through the lens of the loss landscape, which has been shown to contain a manifold of linearly connected minima. Building on this new perspective, we propose a metric to study the impact of expansion by estimating the size of the manifold. Experimental results show a clear relationship between gains in performance and manifold size, enabling the comparison of candidate models and presenting a first step towards expanding models more reliably based on geometric properties of the loss landscape.
2024-05-24
ArXiv (preprint)
doi.org
arxiv.org