# Efficient Language Model

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.

The RWKV-7 model in flash-linear attention format, supporting English text generation tasks.

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, 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.

A linear computational cost inference model based on RetNet architecture hybridized with Transformer, achieved through cross-architecture transfer learning

This model is a fine-tuned version of TinyBERT_L-4_H-312_v2 on the wikitext dataset, primarily used for text-related tasks.