In recent developments, Google has unveiled the Gemma-2 model, a significant step forward in the field of machine learning. This blog post will define what Gemma is, distinguish it from Google’s previous Gemini model, and explore the practical applications of Gemma in real-world tasks.

👉 What is the Gemma Model?

The Gemma-2 model is the latest innovation in Google’s suite of machine learning tools. Designed to enhance natural language understanding and generation, Gemma-2 utilizes advanced neural network architectures to deliver highly accurate and contextually relevant outputs. It is built on the principles of deep learning and leverages vast amounts of data to continually improve its performance.

Gemma is currently available in two sizes: 9B and 27B (parameter sizes), and each model has two variants, base (pre-trained) and instruction-tuned.

Google has filtered out personal information and other sensitive data from training sets to make the pre-trained models safe and reliable.

👉 Built for developers and researchers

Getting started with Gemma is straightforward due to its integration with popular tools like Hugging Face Transformers, Kaggle, NVIDIA NeMo, and MaxText. Deployment on Google Cloud is also simple through Vertex AI and Google Kubernetes Engine (GKE). Additionally, Gemma is optimized for AI hardware platforms, including NVIDIA GPUs and Google Cloud TPUs.

👉 Gemma vs. Gemini

Gemini is available to end customers through Web, Android, and iOS apps, while Gemma models are for developers. Developers can access Gemini via APIs or Vertex AI, making it a closed model. Gemma, being open-source, is accessible to developers, researchers, and businesses for experimentation and integration (through HuggingFace, Kaggle, …).

👉 Additional Information

The company also plans to release more variants in the future as they expand the Gemma family such as CodeGemma, RecurrentGemma and PaliGemma — each offering unique capabilities for different AI tasks and easily accessible through integrations with partners like Hugging Face, NVIDIA and Ollama.

👉 How to use and fine-tuning Gemma with your own applications

1️⃣ Setup

Select the Colab runtime

To complete this tutorial, you’ll need to have a Colab runtime with sufficient resources to run the Gemma model. In this case, you can use a T4 GPU:

In the upper-right of the Colab window, select ▾ (Additional connection options).
Select Change runtime type.
Under Hardware accelerator, select T4 GPU.

2️⃣ Gemma setup

Before we dive into the tutorial, let’s get you set up with Gemma:

Hugging Face Account: If you don’t already have one, you can create a free Hugging Face account by clicking here.
Gemma Model Access: Head over to the Gemma model page and accept the usage conditions.
Colab with Gemma Power: For this tutorial, you’ll need a Colab runtime with enough resources to handle the Gemma 2B model. Choose an appropriate runtime when starting your Colab session.
Hugging Face Token: Generate a Hugging Face access (preferably write permission) token by clicking here. You'll need this token later in the tutorial.

3️⃣ Configure your HF token

Add your Hugging Face token to the Colab Secrets manager to securely store it.

4️⃣ Instantiate and Fine-tuning the model

The code for Instantiate and fine-tuning the model is extensive and is therefore included in this notebook. Please refer to it for detailed instructions and reference.

👉 Conclusion

The full notebook for this article is available here.

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