Google Launches T5Gemma to Reclaim Encoder-Decoder Architecture Benefits
Google has launched T5Gemma, a new collection of encoder-decoder large language models (LLMs) that promise improved quality and inference efficiency compared to their decoder-only counterparts. It is based on the Gemma 2 framework. Unlike the current trend that favours decoder-only LLMs, T5Gemma revisits the classic encoder-decoder architecture used in models like T5. The company introduced an adaptation technique that converts pretrained decoder-only models into encoder-decoder ones. “We study a novel problem: adapting pretrained decoder-only LLMs to encoder-decoder, with the goal of leveraging the strengths of both approaches to achieve a more favourable quality efficiency trade-off,” the research paper mentioned. The researchers further highlight that this adaptation not only enables inheriting the capability of decoder-only LLMs but also reduces the computational demand compared to pretraining from scratch. “Can we build top-tier encoder-decoder models based on pretrained decoder-only models? We answer this question by exploring model adaptation,” the company explained in the blog post. T5Gemma includes both newly trained T5-sized models, ranging from small to XL, and adapted Gemma 2 models with 2B and 9B parameters. It also offers unbalanced combinations such as a 9B encoder with a 2B decoder, aimed at optimising performance for tasks where input understanding is more important than output complexity. According to benchmark results shared by Google, T5Gemma dominates the quality-inference efficiency Pareto frontier. On SuperGLUE and GSM8K, the models outperform comparable decoder-only models in both accuracy and latency. For example, T5Gemma 9B-9B delivered higher GSM8K accuracy than Gemma 2 9B while maintaining similar latency. The gains extend beyond pretraining. After instruction tuning, T5Gemma models showed dramatic improvements. The 2B-2B model’s MMLU score jumped 12 points, while GSM8K accuracy rose from 58.0% to 70.7%, highlighting the architecture’s responsiveness to fine-tuning. Google has released a wide range of T5Gemma checkpoints, including pretrained and instruction-tuned variants, with multiple training objectives such as PrefixLM and UL2. The models are now available on Hugging Face, Kaggle, and Vertex AI for further experimentation and deployment.
Google’s new AI models take a new approach with an older architecture.
Google’s new AI models take a new approach with an older architecture.
["Google"]
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["AI", "ML", "Aim", "Hugging Face", "RAG", "TPU", "R", "Go", "T5", "RPA"]
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