43 citations · 51 across the 2 of their papers we have counts for
3 papers
RecurrentGemma: Moving Past Transformers for Efficient Open Language Models
Aleksandar Botev, Soham De, Samuel L Smith +59
We introduce RecurrentGemma, a family of open language models which uses Google's novel Griffin architecture. Griffin combines linear recurrences with local attention to achieve ex…
Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models
Soham De, Samuel L. Smith, Anushan Fernando +14
Recurrent neural networks (RNNs) have fast inference and scale efficiently on long sequences, but they are difficult to train and hard to scale. We propose Hawk, an RNN with gated…
Resurrecting Recurrent Neural Networks for Long Sequences
Antonio Orvieto, Samuel L Smith, Albert Gu +4
Recurrent Neural Networks (RNNs) offer fast inference on long sequences but are hard to optimize and slow to train. Deep state-space models (SSMs) have recently been shown to perfo…