4/25/2025

How to Create Custom Ollama Templates for Your AI Projects

Creating custom templates is one of the most powerful ways to optimize your AI projects using Ollama, especially when working with Large Language Models (LLMs). With Ollama's functionality, you can craft tailored experiences that fit your specific application and user needs. This blog post will guide you through designing, implementing, and refining custom Ollama templates.

Understanding the Basics of Ollama Templates

Ollama leverages a robust templating engine, which is based on Go's built-in templating constructs. This engine allows developers to create dynamic templates that can adapt based on user input, providing a rich conversational experience for applications. The templating language supports the creation of prompts that integrate various components such as text, variables, and functions seamlessly.

Key Components of Ollama Templates

Layout: The overall skeleton or structure of your template.
Variables: Placeholders for dynamic data that will be replaced with actual values when the template is rendered.
Functions: Custom logic that manipulates the content based on your requirements.

Here's a simple template to get you started:

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{{- range .Messages }}
{{ .Role }}: {{ .Content }}
{{- end }}

In this example, we have a basic message structure, three variables (

Messages

Role

, and

Content

), and a custom function that iterates over the array of items.

Getting Started with Custom Templates

To start using custom templates within your Ollama projects, you need to follow the steps below:

Set Up Your Environment: Make sure you have Ollama installed on your local machine. If not, you can download it here.
Creating a Template File: Create a new file within your project directory. You can name it something like
1my_template.go
. This file will hold your custom template definitions.
Defining Your Template: Within your template file, you'll define the structure of your conversation and the necessary variables. For instance:
1 2 3 4 5 6 7 8go TEMPLATE """ {{- .System }}<|start_header_id|>system<|end_header_id|> {{ .System }}<|eot_id|> {{- end }} {{- range .Messages }}<|start_header_id|>{{ .Role }}<|end_header_id|> {{ .Content }}<|eot_id|> {{- end }}<|start_header_id|>assistant<|end_header_id|>"
Add to Your Modelfile: To integrate this template into your project, you need to reference it in your Ollama
1Modelfile
. This file acts as a blueprint for how your model interacts with the environment. Add the following line to your
1Modelfile
:
1 2bash TEMPLATE my_template
Testing Your Template: To check if your template is working correctly, run your model with a sample input that uses your defined template. Use the command:
1 2bash ollama run your_model_name

Best Practices for Creating Effective Templates

To ensure your custom templates are effective, consider the following best practices:

Keep It Simple: Don't overcomplicate your templates. Aim for clarity and maintainability.
Use Descriptive Variable Names: Descriptive names will help you (and others) understand what data the variable holds at a glance.
Comment Your Code: Including comments about what each section of your template does can be invaluable when revisiting your code later.
Optimize for Clarity: Avoid overly technical jargon that could confuse other users or developers who may work with your templates in the future.

Advanced Template Techniques

Now that you have the basics down, let's dive into some advanced techniques for maximizing your use of templates in Ollama.

Using Variables Effectively

Variables are central to creating dynamic templates. Here’s a breakdown of how you can efficiently use variables:

System Variable: This variable holds the overall instruction set for the model. For example:
1 2go SYSTEM """You are a personal AI assistant here to help users understand programming concepts."""
User Input Variable: Captured user inputs should be dynamically placed in the conversation flow. This is the
1Prompt
variable that captures what users say:
1 2go {{ .Prompt }}
Response Variable: The output that the model will provide is typically referenced as
1Response
, facilitating easy management of responses generated by your model.

Implementing Custom Functions

You can also enhance your template functionalities by including custom functions. These functions can manipulate the output based on your application's logic. Here’s how:

Creating a Custom Function: You can define functions within your templates that operate on the variables you are passing:
1 2 3 4 5 6go {{- if .SomeCondition }} This condition is true. {{- else }} Default response. {{- end }}
Function Library: Consider creating a library of commonly used functions that can be referenced in multiple templates.

Using Conditional Logic

Conditional logic can help manage different conditions based on user inputs or system states. An example would be using an if-else structure to provide different responses based on user roles:

go
{{- if eq .Role "admin" }}
Admin access granted.
{{- else }}
User access only.
{{- end }}

Example: Building an AI Chatbot

Let’s go through a more comprehensive example: Setting up an AI chatbot that can respond differently based on user roles (admin, user, guest).

Step 1: Define Your Variables

In your template, define the required variables for your chatbot:

go
{{- range .Messages }}
{{ .Role }}: {{ .Content }}
{{- end }}

Step 2: Create a template in your Modelfile

Add it to the Modelfile:

bash
TEMPLATE """
{{- if eq .Role "admin" }}
Welcome, Admin! How can I assist you today?
{{- else if eq .Role "user" }}
Hello, User! What questions do you have?
{{- else }}
Hi there! How can I help you?
{{- end }}
"""

Step 3: Link to the System

Make sure your system input is linked correctly using the SYSTEM variable. Here’s a refined example:

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bash
SYSTEM "Your role is to assist users in their programming queries."

Step 4: Run Your Chatbot

Test your chatbot with various roles by executing the commands in your terminal:

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bash
ollama run your_chatbot_model

Conclusion

Creating custom templates using Ollama is an excellent way to develop powerful, flexible AI-powered tools tailored to your needs. By leveraging the capabilities of Ollama's templating engine, you can build conversational AI models that deliver a truly personalized experience to users.

Whether you're starting from scratch or working with existing models, following best practices and utilizing advanced techniques can significantly enhance your AI project outcomes.

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With Arsturn, explore the flexibility, customization, and powerful insights of your AI chatbots. Join thousands using conversational AI to enhance audience engagement and streamline operations. Don’t wait, create your chatbot now!

Now, dive in, and happy templating!