Google DeepMind's Griffin Model Scales to 14B Parameters for Efficient Language Processing

Google DeepMind has introduced two new architectures, Hawk and Griffin, aimed at enhancing the efficiency of language models. These models incorporate a novel gated linear recurrent layer, designed to replace the conventional multi-query attention mechanism. The introduction of Griffin, in particular, highlights a hybrid approach that combines a gated linear recurrence with local attention, promising to maintain the efficiency of linear recurrent neural networks (RNNs) while incorporating the expressiveness of transformers. This development is significant as it addresses the challenges associated with training RNNs and scaling them efficiently for long sequences. The Griffin model has been scaled up to 14 billion parameters, indicating a substantial leap towards more efficient and effective language modeling.