T5Gemma 2: The next generation of encoder-decoder models

T5Gemma 2: The next generation of encoder-decoder models T5Gemma 2 is the next evolution of our encoder-decoder family based on Gemma 3, featuring the first multi-modal and long-context encoder-decoder models. Unlike T5Gemma, T5Gemma 2 adopts tied word embeddings (over encoder and decoder) and merged decoder self- and cross-attention to save model parameters. It offers compact pre-trained models at sizes of 270M-270M (~370M total, excluding vision encoder), 1B-1B (~1.7B) and 4B-4B (~7B) parameters, making them ideal for rapid experimentation and deployment in on-device applications. Background With the original T5Gemma , we demonstrated that we could successfully adapt modern, pre-trained decoder-only models into an encoder-decoder architecture, unlocking new versatility. By initializing with weights from a powerful decoder-only model and then applying continued pre-training, we created high-quality, inference-efficient models while bypassing the computational cost of training from scratch. T5Gemma 2 extends this into the realm of vision-language models by incorporating key innovations from Gemma 3. What’s new T5Gemma 2 is more than a re-training. It incorporates significant architectural changes while inheriting many of the powerful, next-generation features of the Gemma 3 family. Architectural innovations for efficiency To maximize efficiency at smaller scales, we have introduced key structural refinements: Tied embeddings: We now tie the embeddings between the encoder and decoder. This significantly reduces the overall parameter count, allowing us to pack more active capabilities into the same memory footprint - crucial for our new compact 270M-270M model. Merged attention: In the decoder, we adopt a merged attention mechanism, combining self- and cross-attention into a single, unified attention layer. This reduces model parameters and architectural complexity, improving model parallelization and benefiting inference. Next-generation capabilities Drawing from Gemma 3, T5Gemma 2 also represents a significant upgrade in model capabilities: Multimodality: T5Gemma 2 models can understand and process images alongside text. By utilizing a highly efficient vision encoder, the models can seamlessly perform visual question answering and multimodal reasoning tasks. Extended long context: We've dramatically expanded the context window. Leveraging Gemma 3's alternating local and global attention mechanism, T5Gemma 2 can handle context windows of up to 128K tokens. Massively multilingual: Trained on a larger, more diverse dataset, these models now support over 140 languages out of the box. Performance T5Gemma 2 sets a new standard for what compact encoder-decoder models can achieve. Our new models demonstrate strong performance across key capability areas, inheriting the powerful multimodal and long-context features from the Gemma 3 architecture. Pre-training performance of Gemma 3, T5Gemma and T5Gemma 2 across five unique capabilities. As shown in the charts above, T5Gemma 2 delivers: Strong multimodal performance , outperforming Gemma 3 on several benchmarks. We adapt text-only Gemma 3 base models (270M and 1B) into effective multimodal encoder-decoder models. Superior long-context capability , with substantial quality gains over Gemma 3 and T5Gemma. Using a separate encoder makes T5Gemma 2 better at handling long-context problems. Improved general capabilities . Across coding, reasoning and multilingual tasks, T5Gemma 2 generally surpasses its corresponding Gemma 3 counterpart. Post-training performance. Note: we are not releasing any post-trained /...

Preview: ~500 words