虽然Gemini是作为多模态系统进行训练的，但它拥有现代大语言模型（如GPT-3.5、Claude和Llama）中存在的许多功能。以下是使用Gemini Pro实现简单文本摘要任务的示例。本例中我们使用[Google AI Studio](https://ai.google.dev)，温度值（temperature）为0。
Your task is to summarize an abstract into one sentence.
Avoid technical jargon and explain it in the simplest of words.
Abstract: Antibiotics are a type of medication used to treat bacterial infections. They work by either killing the bacteria or preventing them from reproducing, allowing the body’s immune system to fight off the infection. Antibiotics are usually taken orally in the form of pills, capsules, or liquid solutions, or sometimes administered intravenously. They are not effective against viral infections, and using them inappropriately can lead to antibiotic resistance.
```
Gemini Pro输出:
```
Antibiotics are medicines used to kill or stop the growth of bacteria causing infections, but they don't work against viruses.
Your task is to extract model names from machine learning paper abstracts. Your response is an array of the model names in the format [\"model_name\"]. If you don't find model names in the abstract or you are not sure, return [\"NA\"]
Abstract: Large Language Models (LLMs), such as ChatGPT and GPT-4, have revolutionized natural language processing research and demonstrated potential in Artificial General Intelligence (AGI). However, the expensive training and deployment of LLMs present challenges to transparent and open academic research. To address these issues, this project open-sources the Chinese LLaMA and Alpaca…
下面这个简单的示例演示了如何使用Gemini API提示Gemini Pro模型。你需要安装`google-generativeai`库并从Google AI Studio获取API密钥。下面的示例是以上章节中提到的信息提取任务的代码。
```python
"""
At the command line, only need to run once to install the package via pip:
$ pip install google-generativeai
"""
import google.generativeai as genai
genai.configure(api_key="YOUR_API_KEY")
# Set up the model
generation_config = {
"temperature": 0,
"top_p": 1,
"top_k": 1,
"max_output_tokens": 2048,
}
safety_settings = [
{
"category": "HARM_CATEGORY_HARASSMENT",
"threshold": "BLOCK_MEDIUM_AND_ABOVE"
},
{
"category": "HARM_CATEGORY_HATE_SPEECH",
"threshold": "BLOCK_MEDIUM_AND_ABOVE"
},
{
"category": "HARM_CATEGORY_SEXUALLY_EXPLICIT",
"threshold": "BLOCK_MEDIUM_AND_ABOVE"
},
{
"category": "HARM_CATEGORY_DANGEROUS_CONTENT",
"threshold": "BLOCK_MEDIUM_AND_ABOVE"
}
]
model = genai.GenerativeModel(model_name="gemini-pro",
generation_config=generation_config,
safety_settings=safety_settings)
prompt_parts = [
"Your task is to extract model names from machine learning paper abstracts. Your response is an array of the model names in the format [\\\"model_name\\\"]. If you don't find model names in the abstract or you are not sure, return [\\\"NA\\\"]\n\nAbstract: Large Language Models (LLMs), such as ChatGPT and GPT-4, have revolutionized natural language processing research and demonstrated potential in Artificial General Intelligence (AGI). However, the expensive training and deployment of LLMs present challenges to transparent and open academic research. To address these issues, this project open-sources the Chinese LLaMA and Alpaca… [\\\"LLMs\\\", \\\"ChatGPT\\\", \\\"GPT-4\\\", \\\"Chinese LLaMA\\\", \\\"Alpaca\\\"]",
]
response = model.generate_content(prompt_parts)
print(response.text)
```
## 引用
- [Introducing Gemini: our largest and most capable AI model](https://blog.google/technology/ai/google-gemini-ai/#sundar-note)
- [How it’s Made: Interacting with Gemini through multimodal prompting](https://developers.googleblog.com/2023/12/how-its-made-gemini-multimodal-prompting.html)
- [Welcome to the Gemini era](https://deepmind.google/technologies/gemini/#introduction)
- [Gemini: A Family of Highly Capable Multimodal Models - Technical Report](https://storage.googleapis.com/deepmind-media/gemini/gemini_1_report.pdf)
- [Fast Transformer Decoding: One Write-Head is All You Need](https://arxiv.org/abs/1911.02150)
