gemma2:2b-text-q3_K_S - Ollama 框架

内容创建和通信
- 文本生成：这些模型可用于生成创造性的文本格式，例如诗歌、脚本、代码、营销文案和电子邮件草稿。
- 聊天机器人和会话式人工智能：为客户服务、虚拟助手或交互式应用程序提供会话式界面。
- 文本摘要：生成文本语料库、研究论文或报告的简洁摘要。
研究和教育
- 自然语言处理 (NLP) 研究：这些模型可以作为研究人员试验 NLP 技术、开发算法并为该领域的发展做出贡献的基础。
- 语言学习工具：支持交互式语言学习体验，帮助进行语法纠正或提供写作练习。
- 知识探索：通过生成摘要或回答有关特定主题的问题来帮助研究人员探索大量文本。

将 Gemma 2 与流行的工具一起使用

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.ac.cn/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.ac.cn/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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