Google’s Gemma 2 model comes in three sizes: 2B, 9B, and 27B, featuring a cutting-edge architecture built for exceptional performance and efficiency. With 27 billion parameters, Gemma 2 outperforms models more than twice its size in benchmarks. Its impressive efficiency sets a new benchmark in the open model space.
Available in 3 sizes: 2B, 9B, and 27B parameters:
- 27B Parameters:
ollama run gemma2:27b - 2B Parameters:
ollama run gemma2:2b - 9B Parameters:
ollama run gemma2
Intended Usage
Open Large Language Models (LLMs) offer a wide range of applications across various industries. The following list is not exhaustive but provides examples of potential use cases considered during the model’s training and development.
Content Creation and Communication
- Text Generation: These models can create content in various formats, including poems, scripts, code, marketing copy, and email drafts.
- Chatbots and Conversational AI: Enable conversational interfaces for customer support, virtual assistants, or interactive applications.
- Text Summarization: Generate concise summaries of text corpora, research papers, or reports.
Research and Education
- Natural Language Processing (NLP) Research: Serve as a foundation for NLP research, allowing experimentation with algorithms and contributing to the advancement of the field.
- Language Learning Tools: Support interactive language learning by providing grammar correction and writing practice.
- Knowledge Exploration: Assist researchers by generating summaries or answering questions from large text datasets.
Using Gemma 2 with Popular Tools
- LangChain
from langchain_community.llms import Ollama
llm = Ollama(model="gemma2")
llm.invoke("Why is the sky blue?")- LlamaIndex
from llama_index.llms.ollama import Ollama
llm = Ollama(model="gemma2")
llm.complete("Why is the sky blue?")Hugging Face link: