gemma2:27b-text-q5_K_M

內容創作與溝通
- 文字生成：這些模型可用於生成創意文字格式，例如詩歌、腳本、程式碼、行銷文案和電子郵件草稿。
- 聊天機器人與對話式 AI：為客戶服務、虛擬助理或互動式應用程式提供對話式介面。
- 文字摘要：生成文本語料庫、研究論文或報告的簡潔摘要。
研究與教育
- 自然語言處理 (NLP) 研究：這些模型可以作為研究人員實驗 NLP 技術、開發演算法並為該領域的發展做出貢獻的基礎。
- 語言學習工具：支援互動式語言學習體驗，協助文法校正或提供寫作練習。
- 知識探索：透過生成摘要或回答有關特定主題的問題，協助研究人員探索大量文本。

將 Gemma 2 與熱門工具結合使用

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?")

![Ollama in a Noogler hat with the Gemma 2 logo](https://ollama.dev.org.tw/assets/library/gemma2/58a4be20-b402-4dfa-8f1d-05d820f1204f)

Google's Gemma 2 model is available in three sizes, 2B, 9B and 27B, featuring a brand new architecture designed for class leading performance and efficiency.

## Class leading performance

At 27 billion parameters, Gemma 2 delivers performance surpassing models more than twice its size in benchmarks. This breakthrough efficiency sets a new standard in the open model landscape.

## Three sizes: 2B, 9B and 27B parameters

* 2B Parameters `ollama run gemma2:2b`
* 9B Parameters `ollama run gemma2`
* 27B Parameters `ollama run gemma2:27b`

## Benchmark

![Benchmark](https://ollama.dev.org.tw/assets/library/gemma2/79663012-1c9c-4451-871b-4621f1a898d6)

## Intended Usage

Open Large Language Models (LLMs) have a wide range of applications across
various industries and domains. The following list of potential uses is not
comprehensive. The purpose of this list is to provide contextual information
about the possible use-cases that the model creators considered as part of model
training and development.

* Content Creation and Communication
  * Text Generation: These models can be used to generate creative text formats
    such as poems, scripts, code, marketing copy, and email drafts.
  * Chatbots and Conversational AI: Power conversational interfaces for customer
    service, virtual assistants, or interactive applications.
  * Text Summarization: Generate concise summaries of a text corpus, research
    papers, or reports.
* Research and Education
  * Natural Language Processing (NLP) Research: These models can serve as a
    foundation for researchers to experiment with NLP techniques, develop
    algorithms, and contribute to the advancement of the field.
  * Language Learning Tools: Support interactive language learning experiences,
    aiding in grammar correction or providing writing practice.
  * Knowledge Exploration: Assist researchers in exploring large bodies of text
    by generating summaries or answering questions about specific topics.

## Using Gemma 2 with popular tooling

### LangChain

```python
from langchain_community.llms import Ollama
llm = Ollama(model="gemma2")
llm.invoke("Why is the sky blue?")
```

### LlamaIndex

```python
from llama_index.llms.ollama import Ollama
llm = Ollama(model="gemma2")
llm.complete("Why is the sky blue?")
```

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