Google Gemma 2

2024-06-26 · Source: Ollama Blog · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Intermediate, quick

Summary

Google has released Gemma 2, an open model available in three parameter sizes: 2B, 9B, and 27B. This new iteration features a redesigned architecture focused on achieving class-leading performance and efficiency. The 27B parameter version of Gemma 2 reportedly outperforms models more than twice its size on various benchmarks, establishing a new benchmark for efficiency within the open model ecosystem. Users can run Gemma 2 via Ollama, with specific commands for each model size, and integrate it with popular AI development tools like LangChain and LlamaIndex using simple Python code snippets.

Key takeaway

For AI Architects evaluating open-source large language models for deployment, Gemma 2's reported efficiency, particularly its 27B version outperforming larger models, suggests a significant shift in performance expectations. You should consider benchmarking Gemma 2 against existing models in your specific use cases to assess its potential for reducing computational overhead and improving inference speeds, especially if resource constraints are a concern.

Key insights

Gemma 2 offers class-leading performance and efficiency across three parameter sizes.

Principles

• Efficiency can surpass raw parameter count
• Open models set new performance standards

Method

Run Gemma 2 locally using Ollama, then integrate with LangChain or LlamaIndex for application development.

In practice

• Use `ollama run gemma2` for 9B model
• Integrate with LangChain via `Ollama(model="gemma2")`
• Integrate with LlamaIndex via `Ollama(model="gemma2")`

Topics

• Google Gemma 2
• Large Language Models
• Model Architecture
• Performance Benchmarking
• Ollama

Best for: AI Architect, NLP Engineer, CTO, Machine Learning Engineer, AI Engineer, AI Student

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• Google’s Gemma 4: Is it the Best Open-Source Model of 2026? — Google's Gemma 4 family offers versatile, open-weight LLMs optimized for diverse environments, from edge devices to consumer GPUs.

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Editorial summary, takeaway, and curation by AIssential. Original article published by Ollama Blog.