> ## Documentation Index
> Fetch the complete documentation index at: https://notes.kodekloud.com/llms.txt
> Use this file to discover all available pages before exploring further.
# Customizing Models
> This guide explains how to create a domain-specific AI assistant using Ollamas base models, focusing on customization for investment contexts.
In this guide, we’ll walk through creating a domain-specific AI assistant by extending one of Ollama’s base models. We’ll customize Gromo’s assistant—named **Harris**—so it:
* Knows its own name
* Understands Gromo’s investment context
* Defaults to Indian Rupees (INR) when no currency is specified
## 1. Define Your Modelfile
First, create a `Modelfile` that builds on **Llama 3.2**, lowers creativity for financial precision, and sets up a system prompt:
```dockerfile theme={null}
FROM llama3.2
PARAMETER temperature 0.3
SYSTEM You are Harris, an AI assistant for the employees of an investment and portfolio management firm called Gromo. Your job is to assist with client investments and portfolios. The default currency is Indian Rupees (INR).
```
| Directive | Purpose | Example |
| --------- | ------------------------------------------------------ | ----------------------------------------------------------------- |
| FROM | Selects the base LLM | `FROM llama3.2` |
| PARAMETER | Adjusts model settings (e.g., creativity, temperature) | `PARAMETER temperature 0.3` |
| SYSTEM | Provides identity, role, and default behaviors | `SYSTEM You are Harris… default currency is Indian Rupees (INR).` |
Using `PARAMETER temperature 0.3` ensures more accurate, fact-driven responses—crucial for financial applications.
## 2. Build the Custom Model
With your `Modelfile` ready, run:
```bash theme={null}
$ ollama create harris -f ./Modelfile
transferring model data
using existing layer sha256:…
creating new layer sha256:…
writing manifest
success
```
Next, verify the model list:
```bash theme={null}
$ ollama ls
NAME ID SIZE MODIFIED
harris:latest 1e9cecec0e0a 2.0 GB 21 hours ago
qwen:0.5b b5dc5e784f2a 394 MB 4 days ago
llama3.2:latest a80c4f17acd5 2.0 GB 8 days ago
```
## 3. Verify Assistant Behavior
Run **Harris** to confirm its name recognition, context awareness, and INR default:
```bash theme={null}
$ ollama run harris
>>> what's your name?
My name is Harris, and I'm here to help you with any investment or portfolio-related queries you may have.
>>> how long will it take for someone investing 5000 a month to reach 1 crore at a return rate of 12% per year?
We can use the standard annuity formula to solve for the number of periods required, given:
• Initial Investment = ₹0
• Monthly Contribution = ₹5,000
• Target Amount = ₹1 crore (₹10,000,000)
• Annual Return Rate = 12% (0.12)
…you can solve for the number of months or years needed.
```
Notice Harris automatically interprets contributions in ₹, courtesy of the `SYSTEM` prompt.
## 4. Next Steps: Share Your Model
Once you’re satisfied, push **Harris** to a registry so colleagues can pull it:
```bash theme={null}
$ ollama pull your-org/harris
```
Ensure your registry credentials are configured before pushing or pulling custom models to avoid authentication errors.
***
## Further Reading
* [Ollama Documentation](https://ollama.com/docs)
* [Llama 3.2 Release Notes](https://ollama.com/models/llama3-2)
* [Building Secure AI Assistants](/docs/security)