T5Gemma: A new collection of encoder-decoder Gemma models

In the evolving field of large language models, the T5Gemma introduces a novel approach by adapting pretrained decoder-only models into encoder-decoder architectures, leveraging the Gemma 2 framework. This method, known as model adaptation, uses the weights of existing decoder-only models to initialize encoder-decoder models, subsequently refining them through UL2 or PrefixLM-based pre-training. The T5Gemma models, which include various sizes such as Small, Base, Large, XL, and unbalanced configurations like a 9B encoder with a 2B decoder, excel in inference efficiency and quality across benchmarks like SuperGLUE and GSM8K. The approach not only maintains a high quality-inference efficiency ratio but also demonstrates significant gains in tasks requiring complex reasoning, outperforming previous models in both foundational and fine-tuned capabilities. The release of T5Gemma checkpoints aims to spur further research and development within the community, offering pretrained and instruction-tuned variants to explore model architecture, efficiency, and performance.

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