Gemma2-2B: Smaller, Safer, More Transparent: Advancing Responsible AI with Gemma
AI has the potential to tackle some of humanity’s most urgent challenges, but this can only happen if everyone has access to the necessary tools. That’s why Google introduced Gemma earlier this year—a family of lightweight, cutting-edge open models developed using the same research and technology behind the Gemini models. The Gemma lineup has since expanded to include CodeGemma, RecurrentGemma, and PaliGemma, each designed for specific AI tasks and easily accessible through partnerships with platforms like Hugging Face, NVIDIA, and Ollama.
Google is now officially launching Gemma 2 for researchers and developers worldwide. Available in both 9 billion (9B) and 27 billion (27B) parameter sizes, Gemma 2 outperforms and is more efficient at inference than its predecessor, with significant improvements in safety. At 27B, it offers competitive alternatives to models over twice its size, achieving performance levels that were previously only possible with proprietary models as recently as December. This performance is now achievable on a single NVIDIA H100 Tensor Core GPU or TPU host, greatly reducing deployment costs.
Gemma is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models. They are text-to-text, decoder-only large language models, available in English, with open weights, pre-trained variants, and instruction-tuned variants. Gemma models are well-suited for a variety of text generation tasks, including question answering, summarization, and reasoning. Their relatively small size makes it possible to deploy them in environments with limited resources such as a laptop, desktop or your own cloud infrastructure, democratizing access to state of the art AI models and helping foster innovation for everyone.
Gemma 2: A New Open Model Standard for Efficiency and Performance
Gemma 2 is built on a redesigned architecture, engineered for exceptional performance and inference efficiency. Here’s what sets it apart:
Outsized Performance
At 27B, Gemma 2 offers the best performance in its size class, providing competitive alternatives to models more than twice its size. The 9B Gemma 2 model also delivers class-leading performance, surpassing Llama 3 8B and other open models in its category. For a detailed performance breakdown, check out the technical report.
Unmatched Efficiency and Cost Savings
The 27B Gemma 2 model is designed to run inference efficiently at full precision on a single Google Cloud TPU host, NVIDIA A100 80GB Tensor Core GPU, or NVIDIA H100 Tensor Core GPU. This significantly reduces costs while maintaining high performance, making AI deployments more accessible and budget-friendly.
Blazing Fast Inference Across Hardware
Gemma 2 is optimized to run at incredible speeds across various hardware platforms, from powerful gaming laptops and high-end desktops to cloud-based setups. You can try Gemma 2 at full precision in Google AI Studio, unlock local performance with the quantized version using Gemma.cpp on your CPU, or run it on your home computer with an NVIDIA RTX or GeForce RTX via Hugging Face Transformers.
Gemma 2 2B: Experience Next-Gen Performance, Now On-Device
In June, the release of Gemma 2 marked a significant milestone in the world of AI, introducing two new models with 27 billion (27B) and 9 billion (9B) parameters. These models quickly garnered attention for their performance, particularly the 27B model, which rose to prominence on the LMSYS Chatbot Arena leaderboard. It not only excelled but also outperformed other popular models more than twice its size in real-world conversations.
Gemma 2’s success is not solely attributed to its performance. The development of Gemma is deeply rooted in the principles of responsible AI, emphasizing safety and accessibility. This commitment to responsible AI has led to the introduction of three exciting new additions to the Gemma 2 family:
- Gemma 2 2B – A new iteration of the 2 billion (2B) parameter model, boasting advanced safety features and an optimal balance of performance and efficiency.
- ShieldGemma – A suite of safety content classifier models, designed to filter AI model inputs and outputs, ensuring user safety.
- Gemma Scope – An interpretability tool that provides unprecedented insight into the inner workings of AI models.
These new tools enable researchers and developers to create safer customer experiences, gain deeper insights into AI models, and deploy powerful AI responsibly, whether on-device or in the cloud, thereby opening new possibilities for innovation.
Gemma 2 2B: Experience Next-Gen Performance, Now On-Device
The Gemma 2 2B model, a highly anticipated addition to the Gemma 2 lineup, is now available. This lightweight model achieves remarkable results through a process called distillation, where it learns from larger models. Despite its smaller size, Gemma 2 2B outperforms all GPT-3.5 models on the Chatbot Arena, demonstrating its exceptional capabilities in conversational AI.
Gemma 2 2B offers several key advantages:
- Exceptional Performance: This model delivers top-tier performance for its size, outperforming other open models in its category.
- Flexible and Cost-Effective Deployment: It can efficiently run on a wide range of hardware, from edge devices and laptops to robust cloud environments like Vertex AI and Google Kubernetes Engine (GKE). The model is optimized with the NVIDIA TensorRT-LLM library and available as an NVIDIA NIM, supporting deployments in data centers, cloud, local workstations, PCs, and edge devices using NVIDIA RTX, NVIDIA GeForce RTX GPUs, or NVIDIA Jetson modules. Additionally, it integrates seamlessly with Keras, JAX, Hugging Face, NVIDIA NeMo, Ollama, Gemma.cpp, and soon MediaPipe.
- Open and Accessible: The model is available under the commercially-friendly Gemma terms for both research and commercial applications. It’s compact enough to run on the free tier of T4 GPUs in Google Colab, making experimentation and development more accessible than ever.
Starting today, the model weights for Gemma 2 can be downloaded from Kaggle, Hugging Face, and Vertex AI Model Garden. Users can also explore its capabilities in Google AI Studio.
ShieldGemma: Protecting Users with State-of-the-Art Safety Classifiers
Ensuring that AI outputs are engaging, safe, and inclusive requires significant effort. To support developers in this endeavor, ShieldGemma has been introduced. ShieldGemma consists of advanced safety classifiers designed to detect and mitigate harmful content in AI model inputs and outputs. These classifiers focus on four critical areas of harm:
- Hate speech
- Harassment
- Sexually explicit content
- Dangerous content
These open classifiers enhance the existing Responsible AI Toolkit, which includes methodologies for building classifiers tailored to specific policies with limited data, alongside Google Cloud’s off-the-shelf classifiers served via API.
ShieldGemma provides several benefits for creating safer AI applications:
- State-of-the-Art Performance: Built on Gemma 2, ShieldGemma represents the industry-leading safety classifiers.
- Flexible Sizes: Available in various sizes to meet diverse needs. The 2B model is ideal for online classification tasks, while the 9B and 27B models offer higher performance for offline applications where latency is less of a concern. All models leverage NVIDIA speed optimizations for efficient performance across hardware.
- Open and Collaborative: The open nature of ShieldGemma promotes transparency and collaboration within the AI community, contributing to the future of machine learning industry safety standards.
“As AI continues to mature, the entire industry will need to invest in developing high-performance safety evaluators. We’re glad to see Google making this investment and look forward to their continued involvement in our AI Safety Working Group.”
~ Rebecca Weiss, Executive Director, ML Commons
For more information about ShieldGemma, including full evaluation results, see the technical report and start building safer AI applications with the comprehensive Responsible Generative AI Toolkit.
Gemma Scope: Illuminating AI Decision-Making with Open Sparse Autoencoders
Gemma Scope provides an unprecedented level of transparency into the decision-making processes of the Gemma 2 models. Acting like a powerful microscope, Gemma Scope utilizes sparse autoencoders (SAEs) to zoom in on specific points within the model, making its internal workings more interpretable.
SAEs are specialized neural networks that transform the dense, complex information processed by Gemma 2 into a more analyzable form. By studying these expanded views, researchers can gain valuable insights into how Gemma 2 identifies patterns, processes information, and makes predictions. The goal of Gemma Scope is to help the AI research community develop more understandable, accountable, and reliable AI systems.
Key features of Gemma Scope include:
- Open SAEs: Over 400 freely available SAEs covering all layers of Gemma 2 2B and 9B.
- Interactive Demos: Users can explore SAE features and analyze model behavior without needing to write code, using Neuronpedia.
- Easy-to-Use Repository: The repository includes code and examples for interfacing with SAEs and Gemma 2.
For more details about Gemma Scope, visit the Google DeepMind blog, review the technical report, and access the developer documentation.
A Future Built on Responsible AI
The release of these new tools underscores an ongoing commitment to providing the AI community with the necessary resources to build a future where AI benefits everyone. Open access, transparency, and collaboration are seen as essential components in the development of safe and beneficial AI.
Get Started Today:
- Experience the power and efficiency of Gemma 2 2B by downloading it or testing it with NVIDIA NIM or Google AI Studio.
- Explore ShieldGemma and develop safer AI applications.
- Try Gemma Scope on Neuronpedia and gain insights into the inner workings of Gemma 2.
Join the journey towards a more responsible and beneficial AI future!
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