Google Deepmind's Gemma

Introduction

Google's Gemma models, which include Gemma 2 billion and Gemma 7 billion, are advanced Transformer models designed for natural language processing tasks. These models have been trained on massive amounts of data and show superior performance in various domains. Let's delve into the architecture, training process, performance, and evaluations of Gemma models.

Google has released two versions of Gemma models: Gemma 2 billion and Gemma 7 billion. These models utilize multi-head attention mechanisms, with Gemma 7 billion using standard multi-head attention and Gemma 2 billion employing multi-query attention. They also incorporate rotary position embeddings, G glu activations, and pre-normalization techniques to enhance their performance. The models have been trained on trillions of tokens from English data sources like web documents, mathematics, and code snippets.

Keywords

• Gemma models
• Transformer architecture
• Multi-head attention
• Training process
• Superior performance

FAQ

1. What are Gemma models?

   • Gemma models are advanced Transformer models developed by Google for natural language processing tasks. They come in two versions: Gemma 2 billion and Gemma 7 billion.

2. How do Gemma models differ in architecture?

   • Gemma 7 billion uses standard multi-head attention, while Gemma 2 billion employs multi-query attention. They also incorporate specific design elements like rotary position embeddings and G glu activations.

3. What data sources were used to train Gemma models?

   • Gemma models have been trained on a vast amount of English data, including web documents, mathematics, and code snippets. They have been fine-tuned for various tasks like dialogue, instruction following, and safety evaluations.

4. How do Gemma models perform compared to other models?

   • Gemma models have shown superior performance across a range of natural language processing tasks, outperforming similar-sized open models on several benchmarks. They excel in domains like mathematics, science, and coding, showcasing their effectiveness in various text-based tasks.