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Gemma Team, Sherif El Abd, Vaibhav Aggarwal, Robin Algayres et autres
We introduce Gemma 4, a new generation of open-weight, natively multimodal language models in the Gemma model family. Designed to advance compute efficiency and reasoning, the Gemma 4 model suite features dense and Mixture-of-Experts architectures, ranging from 2.3B to 31B parameters. Alongside …
Henrique Schechter Vera, Sahil Dua, Biao Zhang, Daniel Salz et autres
We introduce EmbeddingGemma, a new lightweight, open text embedding model based on the Gemma 3 language model family. Our innovative training recipe strategically captures knowledge from larger models via encoder-decoder initialization and geometric embedding distillation. We improve model robustness and expressiveness with …
Andrew Sellergren, Sahar Kazemzadeh, Tiam Jaroensri, Atilla P. Kiraly et autres
Artificial intelligence (AI) has significant potential in healthcare applications, but its training and deployment faces challenges due to healthcare's diverse data, complex tasks, and the need to preserve privacy. Foundation models that perform well on medical tasks and require less task-specific tuning …
Gemma Team, Morgane Rivière, Shreya Pathak, Pier Giuseppe Sessa et autres
In this work, we introduce Gemma 2, a new addition to the Gemma family of lightweight, state-of-the-art open models, ranging in scale from 2 billion to 27 billion parameters. In this new version, we apply several known technical modifications to the Transformer …
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