Gemma (language model)

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Wikipedia

Gemma
Developer(s)Google DeepMind
Initial releaseFebruary 21, 2024; 18 months ago (2024-02-21)[1]
Stable release
Gemma 3 / March 12, 2025; 5 months ago (2025-03-12)[2]
TypeLarge language model
LicenseGemma License
Websitedeepmind.google/models/gemma/

Gemma is a series of open-source large language models developed by Google DeepMind. It is based on similar technologies as Gemini. The first version was released in February 2024, followed by Gemma 2 in June 2024 and Gemma 3 in March 2025. Variants of Gemma have also been developed, such as the vision-language model PaliGemma and the model DolphinGemma for understanding dolphin communication.

History

In February 2024, Google debuted Gemma, a family of free and open-source LLMs that serve as a lightweight version of Gemini. They come in two sizes, with a neural network with two and seven billion parameters, respectively. Multiple publications viewed this as a response to Meta and others open-sourcing their AI models, and a stark reversal from Google's longstanding practice of keeping its AI proprietary.[3][4][5]

Gemma 2 was released on June 27, 2024,[6] and Gemma 3 was released on March 12, 2025.[2][7]

Overview

Based on similar technologies as the Gemini series of models, Gemma is described by Google as helping support its mission of "making AI helpful for everyone."[8] Google offers official Gemma variants optimized for specific use cases, such as MedGemma for medical analysis and DolphinGemma for studying dolphin communication.[9]

Since its release, Gemma models have had over 150 million downloads, with 70,000 variants available on Hugging Face.[10]

The latest generation of models is Gemma 3, offered in 1, 4, 12, and 27 billion parameter sizes with support for over 140 languages. As multimodal models, they support both text and image input.[11] Google also offers Gemma 3n, smaller models optimized for execution on consumer devices like phones, laptops, and tablets.[12]

Architecture

The latest version of Gemma, Gemma 3, is based on a decoder-only transformer architecture with grouped-query attention (GQA) and the SigLIP vision encoder. Every model has a context length of 128K, with the exception of Gemma 3 1B, which has a context length of 32K.[13]

Quantized versions fine-tuned using quantization-aware training (QAT) are also available,[13] offering sizable memory usage improvements with some negative impact on accuracy and precision.[14]

Variants

Google develops official variants of Gemma models designed for specific purposes, like medical analysis or programming. These include:

  • ShieldGemma 2 (4B): Based on the Gemma 3 family, ShieldGemma is designed to identify and filter violent, dangerous, and sexually explicit images.[15]
  • MedGemma (4B and 27B): Also based on Gemma 3, MedGemma is designed for medical applications like image analysis. However, Google also notes that MedGemma "isn't yet clinical grade."[16]
  • DolphinGemma (roughly 400M): Developed in collaboration with researchers at Georgia Tech and the Wild Dolphin Project, DolphinGemma aims to better understand dolphin communication through audio analysis.[17][18]
  • CodeGemma (2B and 7B): CodeGemma is a group of models designed for code completion as well as general coding use.[19] It supports multiple programming languages, including Python, Java, C++, and more.[20]
Technical specifications of Gemma models
Generation Release date Parameters Context length Multimodal Notes
Gemma 121 February 20242B, 7B8,192No2B distilled from 7B. 2B uses multi-query attention while 7B uses multi-head attention.
CodeGemma2B, 7B8,192NoGemma 1 finetuned for code generation.
RecurrentGemma11 April 20242B, 9BUnlimited (trained on 8,192)NoGriffin-based, instead of Transformer-based.[21]
Gemma 227 June 20242B, 9B, 27B8,192No27B trained from web documents, code, science articles. Gemma 2 9B was distilled from 27B. Gemma 2 2B was distilled from a 7B model that remained unreleased. Uses Grouped-Query Attention.[22]
PaliGemma10 July 20243B8,192ImageA vision-language model that takes text and image inputs, and outputs text. It is made by connecting a SigLIP-So400m image encoder with Gemma v1.0 2B.[23][24]
PaliGemma 24 December 20243B, 10B, 28B8,192ImageMade by mating SigLIP-4o400m with Gemma v2.0 2B, 9B, and 27B. Capable of more vision-language tasks.[25][26]
Gemma 312 March 20251B, 4B, 12B, 27B131,072ImageAll models trained with distillation. Post-training focuses on math, coding, chat, instruction following, and multilingual (supports 140 languages). Capable of function calling. 1B is not capable of vision.[27]

Note: open-weight models can have their context length rescaled at inference time. With Gemma 1, Gemma 2, PaliGemma, and PaliGemma 2, the cost is a linear increase of kv-cache size relative to context window size. With Gemma 3 there is an improved growth curve due to the separation of local and global attention. With RecurrentGemma the memory use is unchanged after 2,048 tokens.

References

  1. ↑ Banks, Jeanine; Warkentin, Tris. "Gemma: Introducing new state-of-the-art open models". The Keyword. Retrieved 16 August 2025.
  2. 1 2 "Introducing Gemma 3: The most capable model you can run on a single GPU or TPU". The Keyword. March 12, 2025.
  3. ↑ Khan, Jeremy (February 21, 2024). "Google unveils new family of open-source AI models called Gemma to take on Meta and others—deciding open-source AI ain't so bad after all". Fast Company. Archived from the original on February 21, 2024. Retrieved February 21, 2024.
  4. ↑ Alba, Davey (February 21, 2024). "Google Delves Deeper Into Open Source with Launch of Gemma AI Model". Bloomberg News. Archived from the original on February 21, 2024. Retrieved February 21, 2024.
  5. ↑ Metz, Cade; Grant, Nico (February 21, 2024). "Google Is Giving Away Some of the A.I. That Powers Chatbots". The New York Times. ISSN 0362-4331. Archived from the original on February 21, 2024. Retrieved February 21, 2024.
  6. ↑ "Gemma 2 is now available to researchers and developers". Google. 2024-06-27. Retrieved 2024-08-15.
  7. ↑ "Welcome Gemma 3: Google's all new multimodal, multilingual, long context open LLM". Hugging Face. March 12, 2025.
  8. ↑ Banks, Jeanine; Warkentin, Tris (February 21, 2024). "Gemma: Introducing new state-of-the-art open models". The Keyword. Retrieved 13 July 2025.
  9. ↑ "Gemma - Google DeepMind". Google DeepMind. Retrieved 13 July 2025.
  10. ↑ Wiggers, Kyle (May 12, 2025). "Google's Gemma AI models surpass 150M downloads". TechCrunch. Retrieved 13 July 2025.
  11. ↑ Gosthipaty, Aritra; merve; Cuenca, Pedro; Srivastav, Vaibhav (March 12, 2025). "Welcome Gemma 3: Google's all new multimodal, multilingual, long context open LLM". Hugging Face. Retrieved 13 July 2025.
  12. ↑ "Gemma 3n model overview". Google AI for Developers. Retrieved 13 July 2025.
  13. 1 2 Gemma Team (2025). "Gemma 3 Technical Report". arXiv:2503.19786v1 [cs.CL].
  14. ↑ Clark, Bryan (May 15, 2025). "What is quantization aware training?". IBM. Retrieved 14 July 2025.
  15. ↑ ShieldGemma Team (2025). "ShieldGemma 2: Robust and Tractable Image Content Moderation". arXiv:2504.01081 [cs.CV].
  16. ↑ "MedGemma". Google Health AI Developer Foundations. Retrieved 15 July 2025.
  17. ↑ "DolphinGemma: How Google AI is helping decode dolphin communication". Georgia Tech. Retrieved 15 July 2025.
  18. ↑ Herzing, Denise; Starner, Thad (April 14, 2025). "DolphinGemma: How Google AI is helping decode dolphin communication". The Keyword. Retrieved 15 July 2025.
  19. ↑ Irwin, Kate (April 10, 2024). "Google Launches Coding AIs That Could Rival Microsoft's GitHub Copilot". PCMag. Retrieved 15 July 2025.
  20. ↑ "CodeGemma". Google AI for Developers. Retrieved 15 July 2025.
  21. ↑ "RecurrentGemma: Moving Past Transformers for Efficient Open Language Models". arxiv.org.
  22. ↑ Gemma Team; Riviere, Morgane; Pathak, Shreya; Sessa, Pier Giuseppe; Hardin, Cassidy; Bhupatiraju, Surya; Hussenot, Léonard; Mesnard, Thomas; Shahriari, Bobak (2024-08-02), Gemma 2: Improving Open Language Models at a Practical Size, arXiv:2408.00118
  23. ↑ "PaLI: Scaling Language-Image Learning in 100+ Languages". research.google. Retrieved 2024-08-15.
  24. ↑ "PaliGemma: A versatile 3B VLM for transfer". arxiv.org. 2024-07-10.
  25. ↑ "Introducing PaliGemma 2 mix: A vision-language model for multiple tasks- Google Developers Blog". developers.googleblog.com. Retrieved 2025-02-22.
  26. ↑ "PaliGemma 2: A Family of Versatile VLMs for Transfer". arxiv.org.
  27. ↑ "Gemma 3 Technical Report". arxiv.org.