Ollama: Your Local Large Model Running Expert

In 2023, the explosive development of LLMs has taken place. Closed-source large language models, represented by ChatGPT, have demonstrated astonishing capabilities. However, it is well known that when using closed-source large language models like ChatGPT, the data we communicate with AI is collected to train and improve the model. Therefore, when it comes to practical applications and development, our primary concern should be data privacy.

To address this issue, we can consider deploying open-source models locally to avoid data leakage. Today, I would like to introduce you to a powerful tool—Ollama, which may be the perfect solution to this problem.

Core Features of Ollama

Easy to Install and Use: Ollama supports macOS, Windows, and Linux, providing clear installation and running instructions, allowing users to start and run without needing to understand complex configurations.
Rich Model Library: With Ollama, users can access and run various large language models including Llama 2, Mistral, and Dolphin Phi. This provides great convenience for developers and researchers.
Highly Customizable: Ollama allows users to define and create custom models through Modelfile, meeting the needs of specific application scenarios.
Optimized Performance: Even on ordinary personal computers, Ollama can support running smaller models by optimizing operational efficiency, providing an environment for users to experiment and test.

What Makes Ollama Unique

Compared to other similar tools on the market, the biggest feature of Ollama is its ease of use and flexibility. Users can quickly run models through the command line interface, and can also choose a graphical user interface (GUI) for interaction, such as Ollama WebUI and the native macOS application Ollamac, greatly enhancing the user experience.

Installing Ollama

With Ollama, you can easily run and manage large language models like Llama 2 in your local environment. Below is the installation and running guide for Ollama, suitable for macOS, Windows, and Linux platforms.

For macOS and Windows Users

Download Ollama:

For macOS users, visit Ollama’s official website or GitHub page to download the latest version.
Windows users can download the preview version or obtain the latest version through the same channels.

Installation:

macOS users can install directly from the downloaded package.
Windows users should follow the installation instructions provided with the downloaded installer.

Run a Model:

After installation, open the terminal (macOS) or command prompt (Windows), and enter
the command to run a model, such as Llama 2:
```
ollama run llama2
```

For Linux Users

Install via Command Line:

Open the terminal and enter the following command:

curl -fsSL https://ollama.com/install.sh | sh

This command will automatically download and install Ollama.

Run a Model:

After installation, enter in the terminal
```
ollama run llama2
```

to run the Llama 2 model

Using Docker

For users familiar with Docker, Ollama also provides an official Docker image. This allows you to run models in an isolated environment without being limited by local environment settings.

Pull the Ollama Docker Image:

docker pull ollama/ollama

Run a Model:

Use the following command to start the container and run a model, such as Llama 2:
```
docker run -it ollama/ollama run llama2
```

Model Library and Custom Models

Ollama supports various open-source models. You can view all available models by visiting ollama.ai/library and use ollama pull <model_name> to download a specified model. Additionally, if you want to create a custom model, you can create a Modelfile and use ollama create <model_name> -f ./Modelfile to create it, and run your model with ollama run <model_name>.

Prospects of Application Scenarios

The application scenarios for Ollama are very broad, not limited to technical research and development testing. Educators can use it to provide students with a platform to practice AI technology, and tech enthusiasts can explore the infinite possibilities of artificial intelligence through it.

Try the multimodal open-source model llava, which recognizes image content. The experience is quite smooth.

Generate math problems and English exercises (only as a demonstration; better results can be achieved by optimizing prompts combined with rag technology).

If you don’t like this command-line interaction format, we can also deploy a user interface using the open-webui open-source project.

Project URL: https://github.com/open-webui/open-webui

Conclusion

Ollama, with its ease of use, flexibility, and powerful features, provides an ideal solution for running large language models locally. I think it’s worth a try.

References

1. Minority. (n.d.). Easily Play with Local Large Models Using Ollama. Retrieved from https://sspai.com

2. GitHub. (n.d.). ollama/ollama: Get up and running with Llama 2, Mistral, and other large language models. Retrieved from https://github.com/ollama/ollama

3. GitHub Picks – “The World Needs You, Open Source Depends on Everyone!” (2023, September 1). Ollama – Run, Create, and Share Large Language Models Locally. Retrieved from https://zhupeng.github.io/11-14-pub-cg-jmorganca-ollama/