gemma2:2b - Ollama 框架

gemma2:2b

20.5M 次下载更新于 1年前

Google Gemma 2 是一种高性能且高效的模型，提供三种尺寸：2B、9B 和 27B。

2b 9b 27b

ollama run gemma2:2b

curl https://:11434/api/chat \
  -d '{
    "model": "gemma2:2b",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'

from ollama import chat

response = chat(
    model='gemma2:2b',
    messages=[{'role': 'user', 'content': 'Hello!'}],
)
print(response.message.content)

import ollama from 'ollama'

const response = await ollama.chat({
  model: 'gemma2:2b',
  messages: [{role: 'user', content: 'Hello!'}],
})
console.log(response.message.content)

详情

1年前更新

1年前

8ccf136fdd52 · 1.6GB ·

模型

架构gemma2

·

参数2.61B

·

量化Q4_0

1.6GB

许可

Gemma 使用条款最后修改日期：2024 年 2 月 21 日通过使用、复制、修改、分发、

8.4kB

参数

{ "stop": [ "<start_of_turn>", "<end_of_turn>" ] }

65B

模板

{{- range $i, $_ := .Messages }} {{- $last := eq (len (slice $.Messages $i)) 1 }} {{- if or (eq .Rol

358B

自述文件

Google 的 Gemma 2 模型提供三种尺寸，分别为 2B、9B 和 27B，采用全新的架构设计，性能和效率均处于领先地位。

领先的性能

在 270 亿参数下，Gemma 2 在基准测试中的性能超过了规模是其两倍以上的模型。这种突破性的效率为开放模型领域树立了新标准。

三种尺寸：2B、9B 和 27B 参数

2B 参数 ollama run gemma2:2b
9B 参数 ollama run gemma2
27B 参数 ollama run gemma2:27b

基准测试

预期用途

开放大型语言模型 (LLM) 在各个行业和领域都有广泛的应用。以下潜在用途列表并非详尽无遗。本列表的目的是为模型创建者在模型训练和开发过程中考虑到的可能用例提供背景信息。

内容创作和沟通
- 文本生成：这些模型可用于生成各种创意文本格式，例如诗歌、剧本、代码、营销文案和电子邮件草稿。
- 聊天机器人和对话式人工智能：为客户服务、虚拟助手或交互式应用程序提供对话界面。
- 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实验 NLP 技术、开发算法和为该领域的进步做出贡献的基础。
- 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：帮助研究人员通过生成摘要或回答有关特定主题的问题来探索大量的文本。

使用 Gemma 2 与流行的工具

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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