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Nikolas Gritsch

2 accepted papers

2024

BAM! Just Like That: Simple and Efficient Parameter Upcycling for Mixture of Experts

NeurIPS 2024poster

Mixture of Experts (MoE) framework has become a popular architecture for large language models due to its superior performance compared to dense models. However, training MoEs from scratch in a large-scale regime is prohibitively expensive. Previous work addresses this challenge by independently tra…

Cited by 5SourcePDFScholar
2024

Divergent Token Metrics: Measuring degradation to prune away LLM components – and optimize quantization

NAACL 2024long

Large Language Models (LLMs) have reshaped natural language processing with their impressive capabilities. However, their ever-increasing size has raised concerns about their effective deployment and the need for LLM compression. This study introduces the Divergent Token Metrics (DTMs), a novel appr…