Ollama - Loading Custom Models
Table of Contents
Introduction
This tutorial will guide you through the process of loading custom models using Ollama, specifically focusing on the Jackalope 7B model. You will learn how to access the model files and how to create a model file for use in your applications. This is particularly relevant for developers and AI enthusiasts looking to implement large language models (LLMs) in their projects.
Step 1: Access the Jackalope 7B Model
To begin, you need to access the Jackalope 7B model. Follow these steps:
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Visit the Hugging Face page for Jackalope 7B:
- Link: Jackalope 7B Model
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If you want to explore the GGUF versions, go to:
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Review the available files and choose the one that fits your needs. Ensure that you have the necessary permissions or licenses to use these models.
Step 2: Create a Model File
Once you have accessed the model, the next step is to create a model file that allows you to load and use the model in your applications.
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Open your preferred code editor or IDE.
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Create a new file named
model.py(or any name you prefer). -
Use the following code snippet as a template to define your model file:
from ollama import Model # Load the Jackalope 7B model model = Model.from_pretrained("path/to/jackalope-7b") -
Replace
"path/to/jackalope-7b"with the actual path where the Jackalope 7B model files are located on your system. -
Save the file and ensure it is in the appropriate directory so your application can access it.
Conclusion
In this tutorial, you learned how to access the Jackalope 7B model and create a model file for loading it in your applications. By following these steps, you can easily implement LLMs in your projects. For further exploration, consider checking out additional resources on using LLMs and building agents on platforms like Patreon and GitHub. Happy coding!