Unlock Global Conversations: Introducing Google’s Breakthrough Open Translation Tech

Key Points

• TranslateGemma is a new collection of open translation models that can help people communicate across 55 languages.
• The 12B TranslateGemma model outperforms the larger 27B baseline model in terms of efficiency and quality.
• TranslateGemma models can achieve high-fidelity translation quality using fewer parameters, making them suitable for mobile devices and other applications where resources are limited.

Today, a new collection of open translation models called TranslateGemma has been introduced, built on Gemma 3 and available in 4B, 12B, and 27B parameter sizes. This represents a significant step forward in open translation, helping people communicate across 55 languages, no matter where they are or what device they own. The translation models are designed to be compact and high-performance, allowing for efficient communication without compromising on quality.

The most remarkable finding in the technical evaluation of TranslateGemma is the efficiency of these models. Through a specialized training process, the 12B TranslateGemma model outperforms the Gemma 3 27B baseline as measured using MetricX on the WMT24++ benchmark. This is a significant breakthrough, as it allows developers to achieve high-fidelity translation quality using less than half the parameters of the baseline model.

For developers, this is a massive win, as they can now achieve high-quality translation using fewer resources. The efficiency breakthrough allows for higher throughput and lower latency without sacrificing accuracy. Similarly, the 4B model rivals the performance of the larger 12B baseline, making it a powerful model for mobile inference. This is particularly important for developers who need to deploy translation models on devices with limited resources.

The TranslateGemma models were tested on the WMT24++ dataset, comprising 55 languages covering a wide variety of language families, including high-, mid- and low-resource languages. The results show that TranslateGemma considerably reduced the error rate compared to the baseline Gemma model in all languages, achieving improved quality with greater efficacy. This is a significant step forward in open translation, and it has the potential to improve communication across languages and devices.

Overall, the introduction of TranslateGemma is an important development in the field of open translation. The models’ ability to achieve high-quality translation using fewer parameters makes them suitable for a wide range of applications, from mobile devices to web browsers like Chrome. As the use of translation models continues to grow, it will be interesting to see how TranslateGemma is used to improve communication across languages and devices, and how it integrates with the Chrome browser and ChromeOS ecosystem.

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