3 citations · 4 across the 7 of their papers we have counts for
8 papers
Revisiting the Scaling Properties of Downstream Metrics in Large Language Model Training
Jakub Krajewski, Amitis Shidani, Dan Busbridge +2
While scaling laws for Large Language Models (LLMs) traditionally focus on proxy metrics like pretraining loss, predicting downstream task performance has been considered unreliabl…
-Parametrization for Mixture of Experts
Jan Małaśnicki, Kamil Ciebiera, Mateusz Boruń +8
Recent years have seen a growing interest and adoption of LLMs, with Mixture-of-Experts (MoE) emerging as a leading architecture in extremely large models. Currently, the largest o…
Decoupled Relative Learning Rate Schedules
Jan Ludziejewski, Jan Małaśnicki, Maciej Pióro +8
In this work, we introduce a novel approach for optimizing LLM training by adjusting learning rates across weights of different components in Transformer models. Traditional method…
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…
Joint MoE Scaling Laws: Mixture of Experts Can Be Memory Efficient
Jan Ludziejewski, Maciej Pióro, Jakub Krajewski +8
Mixture of Experts (MoE) architectures have significantly increased computational efficiency in both research and real-world applications of large-scale machine learning models. Ho…
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,…
Mixture of Tokens: Continuous MoE through Cross-Example Aggregation
Szymon Antoniak, Michał Krutul, Maciej Pióro +7
Mixture of Experts (MoE) models based on Transformer architecture are pushing the boundaries of language and vision tasks. The allure of these models lies in their ability to subst…