Utilizes interleaving local-global attentions and group-query attention, trained with knowledge distillation instead of next token prediction to achieve competitive performance comparable with larger models.

A more lightweight variant of the Gemini 1.5 pro, designed for efficiency with minimal regression in quality, making it suitable for applications where compute resources are limited.

Based on Griffin, uses a combination of linear recurrences and local attention instead of global attention to model long sequences efficiently.

Open code models based on Gemma models by further training on over 500 billion tokens of primarily code

A family of 2B and 7B, state-of-the-art language models based on Google's Gemini models, offering advancements in language understanding, reasoning, and safety.

A highly compute-efficient multimodal mixture-of-experts model that excels in long-context retrieval tasks and understanding across text, video, and audio modalities.

A family of highly capable multi-modal models, trained jointly across image, audio, video, and text data for the purpose of building a model with strong generalist capabilities across modalities.