3 citations · 3 across the 3 of their papers we have counts for
3 papers
Projected Compression: Trainable Projection for Efficient Transformer Compression
Maciej Stefaniak, Michał Krutul, Jan Małaśnicki +6
Large language models have steadily increased in size to achieve improved performance; however, this growth has also led to greater inference time and computational demands. Conseq…
Scaling Fine-Grained MoE Beyond 50B Parameters: Empirical Evaluation and Practical Insights
Jakub Krajewski, Marcin Chochowski, Daniel Korzekwa
Mixture of Experts (MoE) architectures have emerged as pivotal for scaling Large Language Models (LLMs) efficiently. Fine-grained MoE approaches - utilizing more numerous, smaller…
Scaling Laws for Fine-Grained Mixture of Experts
Jakub Krajewski, Jan Ludziejewski, Kamil Adamczewski +9
Mixture of Experts (MoE) models have emerged as a primary solution for reducing the computational cost of Large Language Models. In this work, we analyze their scaling properties,…