Introducing Google's Latest Investment in Open Models: Gemma 2.0

Hi, I'm Kathleen Cane, Research Engineer at Google DeepMind, and I'm excited to be here today to unveil the latest advancements in our open-source AI models, Gemma!

1. Kathleen Creel is a research engineer at Google DeepMind and technical lead of the Gemma team.
2. Gemma's North Star was to empower and accelerate the open-source community's work.
3. Since launching their first models in February, Kathleen has been amazed by projects built on top of Gemma.
4. Google has published key research in AI and ML for over a decade, including on transformers, BERT, and more.
5. Google DeepMind continues this tradition, sharing research for the world to validate, examine, and build upon.
6. Google's support for the open-source community extends beyond research, with work on hardware breakthroughs like TPUs and ML frameworks such as TensorFlow and Jax.
7. Gemma is Google DeepMind's family of open-source, lightweight, state-of-the-art models built from the same research and technology used to create the Gemini models.
8. Gemma models are responsible by design, with safety being a top priority from day one.
9. Data sets are manually inspected to ensure high quality and safety, and models are evaluated for safety throughout development.
10. Final models undergo rigorous state-of-the-art safety evaluations before deployment.
11. Gemma models achieve unparalleled breakthrough performance for their scale and outperform significantly larger models.
12. Models are highly extensible and optimized for TPUs, GPUs, and local devices.
13. They support many frameworks, including TensorFlow, Jax, Caris, PyTorch, and ONNX.
14. The real power of Gemma models comes from their open access and open license.
15. Models are available on various platforms, allowing developers to use them with preferred tools and when needed.
16. Since the initial launch in February, several variants have been added to the Gemma model family, including Gemma 1.0 (foundational LLMs), Code Gemma (fine-tuned for code generation and evaluation
17. These models have been updated since their initial release, with new features like better instruction following, improved chat capabilities, enhanced code performance, and larger parameter sizes.
18. The latest releases include P-Gemma, which combines the Siegel Vision encoder with the Gemma 1.0 text decoder for various image-text tasks and capabilities, and Gemma V2, available in 9 billion an
19. P-Gemma comes with pre-trained weights for specific tasks, fine-tuned variants for object detection and segmentation, and transfer checkpoints for academic benchmarks.
20. Gemma V2 models are performant, easy to integrate into existing workflows, and designed for efficient downstream fine-tuning.
21. The 27b model is available in Google AI Studios, allowing developers to experiment with prompts right away.
22. Extensive human evaluations show that Gemma models are consistently preferred over other open models, including larger ones.
23. The Gemma cookbook on GitHub contains recipes for using the Gemma models, and it accepts pull requests to share projects with the community.
24. Developers can apply for gcp credits to accelerate research using Gemma 2, access significantly improved documentation, and engage with the Gemma team on Discord or other social media channels.

Source: AI Engineer via YouTube

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