T5Gemma: A new collection of encoder-decoder Gemma models

In the rapidly evolving landscape of large language models (LLMs), the spotlight has largely focused on the decoder-only architecture. While these models have shown impressive capabilities across a wide range of generation tasks, the classic encoder-decoder architecture, such as T5 (The Text-to-Text Transfer Transformer), remains a popular choice for many real-world applications. Encoder-decoder models often excel at summarization, translation, QA, and more due to their high inference efficiency, design flexibility, and richer encoder representation for understanding input. Nevertheless, the powerful encoder-decoder architecture has received little relative attention.

Today, we revisit this architecture and introduce T5Gemma, a new collection of encoder-decoder LLMs developed by converting pretrained decoder-only models into the encoder-decoder architecture through a technique called adaptation. T5Gemma is based on the Gemma 2 framework, including adapted Gemma 2 2B and 9B models as well as a set of newly trained T5-sized models (Small, Base, Large and XL). We are excited to release pretrained and instruction-tuned T5Gemma models to the community to unlock new opportunities for research and development.

In T5Gemma, we ask the following question: can we build top-tier encoder-decoder models based on pretrained decoder-only models? We answer this question by exploring a technique called model adaptation. The core idea is to initialize the parameters of an encoder-decoder model using the weights of an already pretrained decoder-only model, and then further adapt them via UL2 or PrefixLM-based pre-training.

This adaptation method is highly flexible, allowing for creative combinations of model sizes. For instance, we can pair a large encoder with a small decoder (e.g., a 9B encoder with a 2B decoder) to create an "unbalanced" model. This allows us to fine-tune the quality-efficiency trade-off for specific tasks, such as summarization, where a deep understanding of the input is more critical than the complexity of the generated output.

How does T5Gemma perform?

In our experiments, T5Gemma models achieve comparable or better performance than their decoder-only Gemma counterparts, nearly dominating the quality-inference efficiency pareto frontier across several benchmarks, such as SuperGLUE which measures the quality of the learned representation.

This performance advantage isn't just theoretical; it translates to real-world quality and speed too. When measuring the actual latency for GSM8K (math reasoning), T5Gemma provided a clear win. For example, T5Gemma 9B-9B achieves higher accuracy than Gemma 2 9B but with a similar latency. Even more impressively, T5Gemma 9B-2B delivers a significant accuracy boost over the 2B-2B model, yet its latency is nearly identical to the much smaller Gemma 2 2B model. Ultimately, these experiments showcase that encoder-decoder adaptation offers a flexible, powerful way to balance across quality and inference speed.

Could encoder-decoder LLMs have similar capabilities to decoder-only models?

Yes, T5Gemma shows promising capabilities both before and after instruction tuning.

After pre-training, T5Gemma achieves impressive gains on complex tasks that require reasoning. For instance, T5Gemma 9B-9B scores over 9 points higher on GSM8K (math reasoning) and 4 points higher on DROP (reading comprehension) than the original Gemma 2 9B model. This pattern demonstrates that the encoder-decoder architecture, when initialized via adaptation, has the potential to create a more capable, performant foundational model.

These foundational improvements from pre-training set the stage for even more dramatic gains after instruction tuning. For example, comparing Gemma 2 IT to T5Gemma IT, the performance gap widens significantly across the board. T5Gemma 2B-2B IT sees its MMLU score jump by nearly 12 points over the Gemma 2 2B, and its GSM8K score increases from 58.0% to 70.7%. The adapted architecture not only potentially provides a better starting point but also responds more effectively to instruction-tuning, ultimately leading to a substantially more capable and helpful final model.

We’re very excited to present this new method of building powerful, general purpose encoder-decoder models by adapting from pretrained decoder-only LLMs like Gemma 2. To help accelerate further research and allow the community to build on this work, we are excited to release a suite of our T5Gemma checkpoints.