HTML Introduction:
Introduction
Large language models (LLMs) are increasingly becoming powerful tools for understanding and generating human language. These models have achieved state-of-the-art results on different natural language processing tasks, including text summarization, machine translation, question answering, and dialogue generation. LLMs have even shown promise in more specialized domains, like healthcare, finance, and law.
Google has been at the forefront of LLM research and development, releasing a series of open models that have pushed the boundaries of what is possible with this technology. These models include BERT, T5, and T5X, which have been widely adopted by researchers and practitioners alike. In this Guide, we introduce Gemma, a new family of open LLMs developed by Google.
HTML Learning Objectives:
Learning Objectives
- Understand Gemma’s architecture and key features.
- Explore Gemma’s training process and techniques.
- Evaluate Gemma’s performance across NLP benchmarks.
- Learn to use Gemma for inference tasks.
- Recognize the importance of responsible deployment for Gemma.
HTML What is Gemma:
What is Gemma?
Gemma is a family of open language models based on Google’s Gemini models, trained on up to 6T tokens of text. These are considered to be the lighter versions of Gemini models. The Gemma family consists of two sizes: a 7 billion parameter model for efficient deployment on GPU and TPU, and a 2 billion parameter model for CPU and on-device applications. Gemma exhibits strong generalist capabilities in text domains and state-of-the-art understanding and reasoning skills at scale. It achieves better performance compared to other open models of similar or larger scales across different domains, including question answering, commonsense reasoning, mathematics and science, and coding.
