8/27/2024

Changing Ollama Model Directory: A Comprehensive Guide

When working with ML models using the Ollama framework, you might encounter situations where it becomes essential to change the default model storage directory. With machine learning models potentially consuming a significant amount of hard drive space, knowing how to customize this aspect can be super helpful. In this blog post, we'll explore the options available for changing the Ollama model directory and address common issues faced in the process.

Understanding Ollama's Default Storage

Ollama typically stores its models in a designated directory on your system:

Linux:
1/usr/share/ollama/.ollama/models
macOS:
1~/.ollama/models
Windows:
1C:\Users\%username%\.ollama\models

As you dive into customizing your model directory, it's important to understand that changing this location might be necessary if your storage is limited or if you prefer organizational clarity by separating models across different drives.

Why Change the Default Directory?

Storage Limitations: If you're working with large models like mixtral:8x22b, you might quickly run out of space on your default drive. Changing the storage directory allows you to leverage external drives or different partitions with more space.
Organizational Preferences: Many developers prefer to have a structured way to manage their files. By changing the model directory, you can keep everything organized in a way that suits your workflow.
Performance: In specific scenarios, models located on faster SSDs might improve load times and performance. This configuration gives an edge when working on sophisticated tasks.

How to Change the Ollama Model Directory

Setting Up the Environment Variable

One effective method of changing the model location involves using the

OLLAMA_MODELS

environment variable. Here’s how you can do it:

Open Terminal / Command Prompt: Depending on your OS, you may need to navigate to the terminal (macOS/Linux) or Command Prompt (Windows).
Set the Environment Variable: Use the following command:
- For Linux/macOS:
  1 2bash export OLLAMA_MODELS=/path/to/your/custom/directory
- For Windows: Open the settings, access the environment variables, and create a new variable named
  1OLLAMA_MODELS
  pointing to your desired directory. You can do this via the Command Prompt too:
  1 2cmd setx OLLAMA_MODELS D:\path\to\your\directory
  This pathway will redirect Ollama to use the new directory for storing models.

Verifying Directory Change

Once you set this up, it’s important to verify that Ollama acknowledges the new storage directory. To do this, you can:

Run a command that downloads or triggers model usage. Check the specified directory if the model files appear there.

Use of Symbolic Links

If you face any issues with the environment variable method, an alternative approach is creating a symbolic link:

Navigate to the default Ollama models directory.
Remove the existing models directory:
1 2bash rm -r /usr/share/ollama/.ollama/models
Create a new symbolic link:
1 2bash ln -s /home/yourusername/path/to/new/models /usr/share/ollama/.ollama/models

Common Problems When Changing the Directory

Changing Ollama’s model directory can be complicated, with various issues that users commonly face:

Permission Denied Errors: If you encounter permission issues while trying to create files or directories, ensure you have the appropriate permissions set up. Using
1chmod
or changing the owner of the directories can help resolve this problem.
Models Not Found: Sometimes, Ollama may not recognize the models in the new directory. Ensure that the models are correctly downloaded and not corrupt.
Environment Variable Not Recognized: If you've set the
1OLLAMA_MODELS
variable but it seems not to affect Ollama behavior, restart your terminal session or the entire application to ensure the changes take effect.

Streamlining with Arsturn

Just as you've customized your model storage, you might also want to enhance interactivity with your audience through Arsturn. With Arsturn, you can create custom chatbots leveraging the intelligence of ChatGPT and engage your audience seamlessly across different platforms.

Imagine deploying AI chatbots that retrieve model information dynamically, answer user queries about model performance, and assist in troubleshooting—all tailored to reflect your brand's personality! With Astartn's powerful yet user-friendly interface, no coding skills are necessary, allowing you to focus on GROWTH rather than technicalities.

Benefits of Using Arsturn:

Customizable Chatbots: Tailor them to match your brand or project needs.
Instant Engagement: Keep your audience engaged with quick responses and impressive interactions.
Valuable Insights: Use conversation data to refine your offerings and improve customer experience.

Conclusion

Changing the Ollama model directory is not just about reclaiming storage space; it's about enhancing your workflow, improving performance, and managing your models effectively. To recap, remember to set the environment variable or utilize symbolic links for seamless integration of your models into the new directory.

Moreover, while you're at it, consider leveraging the advantages of platforms like Arsturn to bolster engagement and operational efficiency. Your audience deserves meaningful interactions, and AI chatbots are just the solution!

Dive into the Ollama community, explore, experiment, and remember: optimizing your ML practice enhanced by technology is all part of the game!