# Hierarchical Parameter Allocation

OpenELM is a series of efficient language models introduced by Apple, utilizing a hierarchical scaling strategy to optimize parameter allocation, offering pretrained and instruction-tuned models ranging from 270M to 3B parameters.

OpenELM is a set of open-source efficient language models that employ a hierarchical scaling strategy to optimize parameter allocation, including pre-trained and instruction-tuned versions ranging from 270 million to 3 billion parameters.

OpenELM is a set of open-source efficient language models that employ a hierarchical scaling strategy to optimize parameter allocation and improve model accuracy. It includes four parameter scales: 270M, 450M, 1.1B, and 3B, offering both pre-trained and instruction-tuned versions.

OpenELM is a set of open, efficient language models that employ a hierarchical scaling strategy to optimize parameter allocation and improve model accuracy. It offers pretrained and instruction-tuned versions ranging from 270 million to 3 billion parameters.

OpenELM is a set of open-source efficient language models that adopt a hierarchical scaling strategy to efficiently allocate parameters in each layer of the Transformer model, improving accuracy.