HomeGemma Unveils Advanced Open Models
At Google, we're committed to making AI accessible and beneficial for everyone. Our track record of open-source contributions, including Transformers, TensorFlow, BERT, T5, JAX, AlphaFold, and AlphaCode, reflects this commitment. We're thrilled to announce the launch of our latest open models, Gemma, designed to empower developers and researchers in building AI responsibly.
Gemma Open Models
Gemma represents a new line of lightweight, cutting-edge open models, drawing from the same research and technology that fueled the creation of our Gemini models. Developed by Google DeepMind and other Google teams, Gemma takes inspiration from Gemini, with its name derived from the Latin word gemma, meaning "precious stone." Alongside the model weights, we're releasing a suite of tools aimed at sparking developer innovation, promoting collaboration, and ensuring responsible use of Gemma models.
Gemma is now available globally. Here's what you need to know:
- We're offering two model sizes: Gemma 2B and Gemma 7B, each available in pre-trained and instruction-tuned versions.
- Our new Responsible Generative AI Toolkit equips you with the necessary guidance and tools to develop safer AI applications using Gemma.
- We've created toolchains for inference and supervised fine-tuning (SFT) compatible with major frameworks like JAX, PyTorch, and TensorFlow via native Keras 3.0.
- To help you get started, we've prepared Colab and Kaggle notebooks and integrated Gemma with popular platforms such as Hugging Face, MaxText, NVIDIA NeMo, and TensorRT-LLM.
- Whether you're using a laptop, workstation, or Google Cloud, deploying Gemma is straightforward with Vertex AI and Google Kubernetes Engine (GKE).
- Gemma is optimized for performance across various AI hardware platforms, including NVIDIA GPUs and Google Cloud TPUs.
- The terms of use allow for responsible commercial usage and distribution, catering to organizations of all sizes.
State-of-the-Art Performance at Size
Gemma models leverage the same technical and infrastructural backbone as Gemini, our most advanced and widely accessible AI model. This synergy allows Gemma 2B and 7B to deliver top-tier performance for their sizes, outperforming other open models. Remarkably, Gemma models can be run directly on a developer's laptop or desktop. They even surpass much larger models on crucial benchmarks, all while maintaining our strict standards for safe and responsible outputs. For a deep dive into performance metrics, dataset details, and modeling approaches, check out our technical report.
Responsible by Design
Responsibility is at the core of Gemma's design, guided by our AI Principles. To ensure the safety and reliability of our pre-trained models, we've employed automated techniques to remove personal information and sensitive data from our training sets. We've also extensively fine-tuned and used reinforcement learning from human feedback (RLHF) to ensure our instruction-tuned models align with responsible behaviors. To minimize risks, we've conducted thorough evaluations, including manual red-teaming, automated adversarial testing, and assessments of potential misuse. These efforts are detailed in our Model Card.1
Together with Gemma, we're launching a new Responsible Generative AI Toolkit to support developers and researchers in building safe and responsible AI applications. This toolkit includes:
- Safety classification: A novel method for developing robust safety classifiers with limited examples.
- Debugging: A tool to help you understand and correct Gemma's behavior.
- Guidance: Access to best practices for model development, drawn from Google's experience with large language models.
Optimized Across Frameworks, Tools, and Hardware
You can tailor Gemma models to your specific needs, such as summarization or retrieval-augmented generation (RAG), by fine-tuning them with your data. Gemma is compatible with a broad range of tools and systems:
- Multi-framework tools: Use your preferred framework with reference implementations for inference and fine-tuning, including multi-framework Keras 3.0, native PyTorch, JAX, and Hugging Face Transformers.
- Cross-device compatibility: Gemma models work seamlessly across various devices, from laptops and desktops to IoT and mobile devices, as well as cloud platforms, making AI accessible to a wide audience.
- Cutting-edge hardware platforms: In partnership with NVIDIA, we've optimized Gemma for NVIDIA GPUs, ensuring top performance from data centers to local RTX AI PCs.
- Optimized for Google Cloud: Vertex AI offers a comprehensive MLOps toolkit with various tuning options and one-click deployment using built-in inference optimizations. For advanced customization, you can use fully-managed Vertex AI tools or self-managed GKE, deploying to cost-efficient infrastructure across GPU, TPU, and CPU.
Free Credits for Research and Development
Gemma is designed for the vibrant community of developers and researchers driving AI innovation. Start exploring Gemma today with free access on Kaggle, a free tier for Colab notebooks, and $300 in credits for first-time Google Cloud users. Researchers can also apply for up to $500,000 in Google Cloud credits to boost their projects.
Getting Started
To learn more about Gemma and find quickstart guides, visit ai.google.dev/gemma.
As we continue to expand the Gemma model family, we're excited to introduce new variants tailored for various applications. Keep an eye out for upcoming events and opportunities to engage, learn, and innovate with Gemma.
We can't wait to see what you'll create with Gemma!
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Comments (32)
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![RoyMartínez]()
Google setzt mit Gemma mal wieder ein Zeichen für offene KI. Aber mal ehrlich, wie viele dieser 'Open'-Modelle sind am Ende wirklich für alle zugänglich und nicht nur für große Tech-Firmen mit den nötigen Rechenressourcen? 🤔 Die Liste der bisherigen Projekte ist beeindruckend, keine Frage. Hoffentlich bleibt es nicht nur bei einer Ankündigung und die Dokumentation ist auch für Einsteiger verständlich.
![BillyGreen]()
Super cool to see Google dropping Gemma models! 😎 Open-source AI like this could spark some wild innovations, but I wonder how it stacks up against closed models in real-world tasks.
![WalterWalker]()
Googleの最新オープンモデルは素晴らしいですが、技術に詳しくない私たちにもっと簡単に使えるようにしてほしいです。それでも、オープンソースへのこのような取り組みを見るのは素晴らしいですね！Google、限界に挑戦し続けてください！🚀
![CharlesJohnson]()
Los nuevos modelos de Gemma son un cambio de juego. Los he estado usando en mis proyectos y son muy intuitivos y potentes. El único inconveniente es que la curva de aprendizaje puede ser empinada para principiantes. En general, una excelente adición al catálogo de IA de Google. 😎
![WilliamMiller]()
Os últimos modelos abertos do Google são impressionantes, mas acho que poderiam fazer mais para simplificar a tecnologia para nós, não técnicos. Ainda assim, é ótimo ver esse compromisso com o open-source! Continue empurrando os limites, Google! 🚀
![RogerJackson]()
구글의 최신 오픈 모델은 인상적이지만, 기술에 익숙하지 않은 우리에게 더 쉽게 만들어 주었으면 좋겠어요. 그래도 오픈소스에 대한 이런 헌신을 보는 건 멋지네요! 구글, 계속해서 한계를 밀어붙여 주세요! 🚀
At Google, we're committed to making AI accessible and beneficial for everyone. Our track record of open-source contributions, including Transformers, TensorFlow, BERT, T5, JAX, AlphaFold, and AlphaCode, reflects this commitment. We're thrilled to announce the launch of our latest open models, Gemma, designed to empower developers and researchers in building AI responsibly.
Gemma Open Models
Gemma represents a new line of lightweight, cutting-edge open models, drawing from the same research and technology that fueled the creation of our Gemini models. Developed by Google DeepMind and other Google teams, Gemma takes inspiration from Gemini, with its name derived from the Latin word gemma, meaning "precious stone." Alongside the model weights, we're releasing a suite of tools aimed at sparking developer innovation, promoting collaboration, and ensuring responsible use of Gemma models.
Gemma is now available globally. Here's what you need to know:
- We're offering two model sizes: Gemma 2B and Gemma 7B, each available in pre-trained and instruction-tuned versions.
- Our new Responsible Generative AI Toolkit equips you with the necessary guidance and tools to develop safer AI applications using Gemma.
- We've created toolchains for inference and supervised fine-tuning (SFT) compatible with major frameworks like JAX, PyTorch, and TensorFlow via native Keras 3.0.
- To help you get started, we've prepared Colab and Kaggle notebooks and integrated Gemma with popular platforms such as Hugging Face, MaxText, NVIDIA NeMo, and TensorRT-LLM.
- Whether you're using a laptop, workstation, or Google Cloud, deploying Gemma is straightforward with Vertex AI and Google Kubernetes Engine (GKE).
- Gemma is optimized for performance across various AI hardware platforms, including NVIDIA GPUs and Google Cloud TPUs.
- The terms of use allow for responsible commercial usage and distribution, catering to organizations of all sizes.
State-of-the-Art Performance at Size
Gemma models leverage the same technical and infrastructural backbone as Gemini, our most advanced and widely accessible AI model. This synergy allows Gemma 2B and 7B to deliver top-tier performance for their sizes, outperforming other open models. Remarkably, Gemma models can be run directly on a developer's laptop or desktop. They even surpass much larger models on crucial benchmarks, all while maintaining our strict standards for safe and responsible outputs. For a deep dive into performance metrics, dataset details, and modeling approaches, check out our technical report.
Responsible by Design
Responsibility is at the core of Gemma's design, guided by our AI Principles. To ensure the safety and reliability of our pre-trained models, we've employed automated techniques to remove personal information and sensitive data from our training sets. We've also extensively fine-tuned and used reinforcement learning from human feedback (RLHF) to ensure our instruction-tuned models align with responsible behaviors. To minimize risks, we've conducted thorough evaluations, including manual red-teaming, automated adversarial testing, and assessments of potential misuse. These efforts are detailed in our Model Card.1
Together with Gemma, we're launching a new Responsible Generative AI Toolkit to support developers and researchers in building safe and responsible AI applications. This toolkit includes:
- Safety classification: A novel method for developing robust safety classifiers with limited examples.
- Debugging: A tool to help you understand and correct Gemma's behavior.
- Guidance: Access to best practices for model development, drawn from Google's experience with large language models.
Optimized Across Frameworks, Tools, and Hardware
You can tailor Gemma models to your specific needs, such as summarization or retrieval-augmented generation (RAG), by fine-tuning them with your data. Gemma is compatible with a broad range of tools and systems:
- Multi-framework tools: Use your preferred framework with reference implementations for inference and fine-tuning, including multi-framework Keras 3.0, native PyTorch, JAX, and Hugging Face Transformers.
- Cross-device compatibility: Gemma models work seamlessly across various devices, from laptops and desktops to IoT and mobile devices, as well as cloud platforms, making AI accessible to a wide audience.
- Cutting-edge hardware platforms: In partnership with NVIDIA, we've optimized Gemma for NVIDIA GPUs, ensuring top performance from data centers to local RTX AI PCs.
- Optimized for Google Cloud: Vertex AI offers a comprehensive MLOps toolkit with various tuning options and one-click deployment using built-in inference optimizations. For advanced customization, you can use fully-managed Vertex AI tools or self-managed GKE, deploying to cost-efficient infrastructure across GPU, TPU, and CPU.
Free Credits for Research and Development
Gemma is designed for the vibrant community of developers and researchers driving AI innovation. Start exploring Gemma today with free access on Kaggle, a free tier for Colab notebooks, and $300 in credits for first-time Google Cloud users. Researchers can also apply for up to $500,000 in Google Cloud credits to boost their projects.
Getting Started
To learn more about Gemma and find quickstart guides, visit ai.google.dev/gemma.
As we continue to expand the Gemma model family, we're excited to introduce new variants tailored for various applications. Keep an eye out for upcoming events and opportunities to engage, learn, and innovate with Gemma.
We can't wait to see what you'll create with Gemma!
Google Tests Remy AI Agent for Gemini as Focus Shifts to User Control
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Google setzt mit Gemma mal wieder ein Zeichen für offene KI. Aber mal ehrlich, wie viele dieser 'Open'-Modelle sind am Ende wirklich für alle zugänglich und nicht nur für große Tech-Firmen mit den nötigen Rechenressourcen? 🤔 Die Liste der bisherigen Projekte ist beeindruckend, keine Frage. Hoffentlich bleibt es nicht nur bei einer Ankündigung und die Dokumentation ist auch für Einsteiger verständlich.
Super cool to see Google dropping Gemma models! 😎 Open-source AI like this could spark some wild innovations, but I wonder how it stacks up against closed models in real-world tasks.
Googleの最新オープンモデルは素晴らしいですが、技術に詳しくない私たちにもっと簡単に使えるようにしてほしいです。それでも、オープンソースへのこのような取り組みを見るのは素晴らしいですね！Google、限界に挑戦し続けてください！🚀
Los nuevos modelos de Gemma son un cambio de juego. Los he estado usando en mis proyectos y son muy intuitivos y potentes. El único inconveniente es que la curva de aprendizaje puede ser empinada para principiantes. En general, una excelente adición al catálogo de IA de Google. 😎
Os últimos modelos abertos do Google são impressionantes, mas acho que poderiam fazer mais para simplificar a tecnologia para nós, não técnicos. Ainda assim, é ótimo ver esse compromisso com o open-source! Continue empurrando os limites, Google! 🚀
구글의 최신 오픈 모델은 인상적이지만, 기술에 익숙하지 않은 우리에게 더 쉽게 만들어 주었으면 좋겠어요. 그래도 오픈소스에 대한 이런 헌신을 보는 건 멋지네요! 구글, 계속해서 한계를 밀어붙여 주세요! 🚀
