A Gemma 3 variant optimized for medical text and image comprehension.

A suite of safety content classifier models built on Gemma 2 and designed to detect harmful content in AI models’ text inputs and outputs.

A family of lightweight, open, vision-language models that can interpret text and image inputs.

Fine-tuned Gemma 2 models that integrate retrieval techniques to ground responses in real-world data.

A set of interpretability tools built to help researchers understand the inner workings of Gemma 2.

A collection of powerful, lightweight models that can perform a variety of coding tasks.

A research tool that uses abstractive proposition segmentation (APS) to break down complex text into meaningful components.

A collection of open models designed to improve the efficiency of therapeutic development.

A family of open models using a novel recurrent architecture for faster processing of long sequences.