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TranslateGemma - a google Collection
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TranslateGemma - a google Collection

2026.01.18
·Hugging Face·by 성산/부산/잡부
#Gemma#LLM#Translation#Multilingual#AI

Key Points

  • 1The provided text comprehensively lists Google's extensive Gemma model family, including specialized versions like MedGemma, CodeGemma, and various Gemma 2 iterations, alongside new releases such as TxGemma and ShieldGemma.
  • 2It highlights recent developments and updates, specifically noting TranslateGemma's update three days prior, and mentions numerous related applications, benchmarks, and foundational AI tools like HAI-DEF.
  • 3The document contextualizes the Gemma ecosystem by including earlier influential models such as BERT, T5, and ELECTRA, illustrating Google's broad and continuously evolving AI model portfolio.

This document outlines the extensive portfolio of Google's artificial intelligence models, with a predominant focus on the evolution and diversification of the Gemma model family. It details a chronological progression of model releases and updates, showcasing Google's continuous advancements in various AI domains.

The core of this portfolio is anchored by the Gemma series, which includes foundational models like Gemma release, Gemma 2 Release, Gemma 2 2B Release, and Gemma 3n Preview, indicating successive generations and specialized configurations. These foundational models appear to serve as a base for a wide array of domain-specific and application-oriented derivatives.

Key specialized models within the Gemma family include:

  • TranslateGemma: An Image-Text-to-Text model, noted for its significant scale at 29B parameters and recent updates.
  • MedGemma: Likely tailored for medical applications, with MedGemma Concept Apps and MedGemma Release indicating its deployment.
  • PaliGemma: Introduced with PaliGemma Release, PaliGemma FT Models, PaliGemma 2 Mix, and PaliGemma 2 Release, suggesting a focus on multimodal understanding, potentially involving perception or grounded language.
  • CodeGemma: Explicitly designed for code-related tasks, highlighted by its CodeGemma Release.
  • RecurrentGemma: Indicating exploration into recurrent neural network architectures or recurrent processing capabilities within the Gemma framework.
  • TxGemma and ShieldGemma: Suggesting specialized applications, possibly related to transaction processing or security/defense, respectively. ShieldGemma SigLIP2 points to integration with image-language pre-training models like SigLIP.
  • DataGemma and Gemma-APS: Implying capabilities for data analysis or specific application-performance systems.
  • TimesFM: A model dedicated to time-series forecasting.
  • Gemma Scope and Gemma Scope 2: Possibly referring to models optimized for specific visual or contextual scopes.
  • Gemma 2 JPN Release: A region-specific variant, likely optimized for Japanese language understanding or generation.

The document also acknowledges foundational large language models and transformers that predate or complement the Gemma series, including BERT release, ALBERT release, ELECTRA release, T5 release, Flan-T5 release, MT5 release, and Switch-Transformers release. This lineage underscores Google's deep expertise in transformer architectures. Other notable models include SEAHORSE and SigLIP.

Supporting infrastructure and evaluation benchmarks are also referenced, such as MetricX-23 and MetricX-24 for performance measurement, and IndicGenBench for assessing performance on Indic languages. Initiatives like Health AI Developer Foundations (HAI-DEF) indicate a broader strategy for ecosystem development and responsible AI deployment, particularly in health. ImageInWords Release further hints at advancements in image-to-text generation or understanding.