Chapter 1: Introduction to Google Gemma

Recently, Google announced the launch of two new open-source large language models (LLMs): Gemma 2B and Gemma 7B, which they claim are cutting-edge. The tech giant has also provided various resources to facilitate their rapid adoption. However, the questions remain: Are these models truly open-source? And do they deliver the performance they promise?

This week, Google made a surprising revelation regarding the launch of the Gemma model family. These lightweight, advanced open models are built on the research and technology developed for the Gemini models by Google DeepMind and other teams. The name "Gemma," derived from the Latin word for "precious stone," reflects the ambition behind this release.

Gemma encompasses two variants: Gemma 2B and Gemma 7B. This announcement follows a significant upgrade to Google's flagship model, Gemini, made just the week prior. Although Google asserts that Gemma draws inspiration from Gemini, the exact nature of this relationship remains ambiguous. It is likely that these models share certain architectural aspects or training datasets with Gemini. However, detailed architectural documentation has not been released, aside from noting that these models are decoder-only, similar to Google's previous offerings, such as PaLM and Bard.

Interestingly, Google has not only provided pre-trained models but also instruction-tuned versions. Additionally, users can access Colab and Kaggle notebooks, along with integrations for widely-used tools such as Hugging Face, MaxText, NVIDIA NeMo, and TensorRT-LLM. Furthermore, a model debugging tool has been introduced to help users analyze Gemma's behavior and troubleshoot potential issues.

At this point, Google boasts that the Gemma models are the best in their class based on size.

Chapter 2: Performance and Usability

According to Google, Gemma 2B and 7B demonstrate superior performance compared to other open models of similar size. These models can run directly on developers' laptops or desktops. A comparison with META's LLaMA illustrates that Gemma claims to achieve state-of-the-art results, although detailed information about this comparison is currently lacking. Notably, there has been no mention of how Gemma stacks up against the widely used Mistral 7B model.

Google emphasizes that these models are designed for ease of use. They support multiple frameworks, including Keras, PyTorch, JAX, and Hugging Face Transformers. Additionally, they are compatible with various devices, including desktops, IoT devices, mobile platforms, and cloud services, with NVIDIA collaborating to optimize GPU performance right from the launch.

Gemma is intended for the open community of developers and researchers driving AI advancements. Users can start experimenting with Gemma through free access on Kaggle and a no-cost tier for Colab notebooks, along with $300 in credits for new Google Cloud users. Researchers can also apply for credits of up to $500,000 to expedite their projects.

However, some skepticism arises regarding the true openness of these models. Jeanine Banks from Google notes that while "open models" have gained traction in the industry, they often pertain to open weights models that allow developers to customize and fine-tune them. The specific terms of use can vary significantly, creating a distinction between what is typically deemed open-source and Google's characterization of Gemma as an open model.

This means users must carefully review the terms of use, as Google maintains the final say regarding what can be done with these models. Thus, while they may appear open, the terms of service are permissive but not wholly transparent.

Chapter 3: Conclusion and Future Outlook

In summary, Google has rolled out a new model available in two versions, one of which can be utilized on personal computers. They have ensured compatibility with major frameworks and optimized it for leading GPU technologies. Alongside this, they released notebooks for both Colab and Kaggle, two popular platforms for testing new models, as well as a debugging tool. All these elements encourage immediate usage.

Nonetheless, one must consider whether all that glitters is gold. Google's recent trajectory has raised questions about its reputation. OpenAI currently seems to hold the upper hand, with Microsoft capitalizing on this gap. ChatGPT is evidently more popular than Bard, and Google's once-dominant position in search is facing challenges from emerging LLM-based search engines.

This context likely explains the introduction of Gemini and the pursuit of open-source models. Historically, Google has been wary of open-source, but competitors like META and Mistral have thrived by embracing this model, reaping substantial rewards in terms of funding and market presence.

Could Google's motivations for releasing these models be driven by a desire to enhance its reputation or bolster stock performance? Or does the company genuinely want researchers and developers to adopt its open-source models? While Google has launched these models similarly to its competitors, it has also made them highly compatible with various libraries and provided ample documentation for ease of adoption.

Ultimately, Google may be positioning itself to dominate the open-source model landscape, potentially setting the rules once it achieves market leadership. One cannot help but wonder if Google is contemplating monetizing LLMs in the future. Despite their claims of openness, the reality may be more nuanced.

For now, I plan to continue using Mistral. This week has brought unexpected developments, from the release of OpenAI's Sora to Google's new models. What surprises might the coming weeks hold? I'd love to hear your thoughts in the comments!

If you found this article engaging, feel free to explore my other writings or connect with me on LinkedIn. You can also check out my GitHub repository, which houses code and resources related to machine learning and artificial intelligence.