Gemma：新的开放模型

Gemma：新的开放模型
Gemma: New Open Models

原始链接: https://blog.google/technology/developers/gemma-open-models/

本文介绍了 Gemma，这是 Google 最新的人工智能开放模型，与现有的同类模型相比，它提供了顶级的性能，并显着节省了计算资源。 Gemma 是为了负责任的人工智能开发而开发的，通过其严格的安全可靠输出标准，它使用自动化技术在训练阶段过滤掉某些个人信息和其他敏感数据。其负责任的生成人工智能工具包提供了一种新颖的方法，可以用最少的示例构建强大的安全分类器，并提供调试工具来解决潜在问题。 Gemma 凭借最先进的尺寸性能，可以在各种设备上高效运行，包括笔记本电脑、台式机、移动设备和低端边缘系统。通过 Kaggle（Colab 笔记本的免费套餐）提供免费访问，并为首次使用 Google Cloud 的用户提供 300 美元的积分。虽然这种开放模型非常适合开放社区，但持续的开发将呈现针对不同应用程序量身定制的变体。根据上面的文章，谷歌如何确保在其新推出的名为 Gemma 的开放模型的训练过程中负责任的行为，特别是在敏感数据方面？

然而，它仍然属于开源范畴，因为它允许第三方审计，并且需要合作制定高质量的评估标准，以确保透明度并防止损害。此外，该模型的发布鼓励并欢迎更广泛的人工智能社区为构建开放的人工智能评估基础设施做出贡献。因此，它与开源原则紧密结合，特别是考虑到它提供了用于非营利目的的许可，这表明了我们致力于让广泛的个人和实体能够使用高质量的人工智能技术。此外，促进开放人工智能和透明的评估流程是提高人工智能技术可靠性、安全性和完整性的关键步骤。

原文

Responsible by design

Gemma is designed with our AI Principles at the forefront. As part of making Gemma pre-trained models safe and reliable, we used automated techniques to filter out certain personal information and other sensitive data from training sets. Additionally, we used extensive fine-tuning and reinforcement learning from human feedback (RLHF) to align our instruction-tuned models with responsible behaviors. To understand and reduce the risk profile for Gemma models, we conducted robust evaluations including manual red-teaming, automated adversarial testing, and assessments of model capabilities for dangerous activities. These evaluations are outlined in our Model Card.

We’re also releasing a new Responsible Generative AI Toolkit together with Gemma to help developers and researchers prioritize building safe and responsible AI applications. The toolkit includes:

Safety classification: We provide a novel methodology for building robust safety classifiers with minimal examples.
Debugging: A model debugging tool helps you investigate Gemma's behavior and address potential issues.
Guidance: You can access best practices for model builders based on Google’s experience in developing and deploying large language models.

Optimized across frameworks, tools and hardware

You can fine-tune Gemma models on your own data to adapt to specific application needs, such as summarization or retrieval-augmented generation (RAG). Gemma supports a wide variety of tools and systems:

Multi-framework tools: Bring your favorite framework, with reference implementations for inference and fine-tuning across multi-framework Keras 3.0, native PyTorch, JAX, and Hugging Face Transformers.
Cross-device compatibility: Gemma models run across popular device types, including laptop, desktop, IoT, mobile and cloud, enabling broadly accessible AI capabilities.
Cutting-edge hardware platforms: We’ve partnered with NVIDIA to optimize Gemma for NVIDIA GPUs, from data center to the cloud to local RTX AI PCs, ensuring industry-leading performance and integration with cutting-edge technology.
Optimized for Google Cloud: Vertex AI provides a broad MLOps toolset with a range of tuning options and one-click deployment using built-in inference optimizations. Advanced customization is available with fully-managed Vertex AI tools or with self-managed GKE, including deployment to cost-efficient infrastructure across GPU, TPU, and CPU from either platform.

Free credits for research and development

Gemma is built for the open community of developers and researchers powering AI innovation. You can start working with Gemma today using free access in Kaggle, a free tier for Colab notebooks, and $300 in credits for first-time Google Cloud users. Researchers can also apply for Google Cloud credits of up to $500,000 to accelerate their projects.

Getting started

You can explore more about Gemma and access quickstart guides on ai.google.dev/gemma.

As we continue to expand the Gemma model family, we look forward to introducing new variants for diverse applications. Stay tuned for events and opportunities in the coming weeks to connect, learn and build with Gemma.

We’re excited to see what you create!