RecurrentGemma: Moving Past Transformers for Efficient Open Language Models
arXiv preprint arXiv:2404.07839, 2024•arxiv.org
We introduce RecurrentGemma, an open language model which uses Google's novel Griffin
architecture. Griffin combines linear recurrences with local attention to achieve excellent
performance on language. It has a fixed-sized state, which reduces memory use and
enables efficient inference on long sequences. We provide a pre-trained model with 2B non-
embedding parameters, and an instruction tuned variant. Both models achieve comparable
performance to Gemma-2B despite being trained on fewer tokens.
architecture. Griffin combines linear recurrences with local attention to achieve excellent
performance on language. It has a fixed-sized state, which reduces memory use and
enables efficient inference on long sequences. We provide a pre-trained model with 2B non-
embedding parameters, and an instruction tuned variant. Both models achieve comparable
performance to Gemma-2B despite being trained on fewer tokens.
We introduce RecurrentGemma, an open language model which uses Google's novel Griffin architecture. Griffin combines linear recurrences with local attention to achieve excellent performance on language. It has a fixed-sized state, which reduces memory use and enables efficient inference on long sequences. We provide a pre-trained model with 2B non-embedding parameters, and an instruction tuned variant. Both models achieve comparable performance to Gemma-2B despite being trained on fewer tokens.
arxiv.org