Are you tired of interacting with AI assistants that all sound the same? They are incredibly intelligent, but let’s be honest: their generic, “helpful” personality is boring.

What if you could take a genius-level AI and give it your personality, your humor, or your specific expertise?

Today, I’m going to show you how to do just that. We’ll create a custom, personalized AI for free, using a simple method that anyone can follow. This guide is based on my full video walkthrough, which you can watch here:

The Goal: Fine-Tuning for Personality, Not Knowledge

First, let’s clarify: we are not building a Large Language Model (LLM) from scratch. That process costs millions of dollars and requires massive computing power.

The secret is a process called Fine-Tuning.

Think of a base model like Llama 3.1 as a “genius” who has already read the entire internet. It already knows history, physics, and programming. We don’t need to teach it knowledge; we need to teach it style.

Fine-tuning is the process of taking that pre-trained genius and teaching it our unique voice, slang, or specialized data.

The “Easy Mode” Tech Stack: Our Free Tools

To make this fast and free, we’ll use a specific set of tools:

1. Google Colab: This gives us access to a powerful, free GPU in the cloud. This is our “oven” for “cooking” the model.
2. Unsloth: This is the magic ingredient. Unsloth is a library that dramatically optimizes the fine-tuning process. It makes training 2x faster and uses 50% less memory, allowing us to fine-tune powerful models on a free Colab notebook.
3. Ollama: This is the tool we’ll use to easily run and chat with our new, custom-trained model on our local machine.

A 3-Step Guide to Fine-Tune Your Custom AI

Here is the entire process, broken down into three simple steps.

Step 1: Build Your “Personality” Dataset

Before we can teach the AI, we need a “textbook.” In AI, this is a dataset, which we’ll create as a simple JSON file.

This file contains pairs of input and output that define the new personality. You don’t need millions of examples; for my video, I used about 300 to teach the AI my local Spanish slang.

A simple example from my dataset looks like this:

• Input (What I ask): “I went to the store on purpose.”
• Output (My custom response): “I went de propio.” (A local slang way of saying the same thing)

This dataset is the most important part. Garbage in, garbage out. A good dataset will result in a good custom AI.

Step 2: Train Your Model with Unsloth in Colab

This is where the magic happens, and it’s surprisingly simple.

1. Open Google Colab and select a T4 GPU runtime (it’s free).
2. Install Unsloth and other required libraries.
3. Load your base model: We’ll load a powerful model like Llama-3.1-8B-Instruct. Unsloth will automatically prepare it for high-speed training.
4. Load your JSON dataset.
5. Start the training: We simply call the trainer.train() command.

Thanks to Unsloth, this process, which used to take hours, is often finished in under 10 minutes.

Once finished, the process will output a single file in GGUF format. This GGUF file is your new, personalized AI model, ready to be used.

Step 3: Run Your Custom GGUF Model with Ollama

Now that you have your gguf file, how do you chat with it? This is where Ollama comes in.

1. First, create a simple text file in the same directory, named Modelfile (no extension).
2. Inside this file, write a single line that points to your new model: FROM ./your-custom-model-file.gguf
3. From your terminal, run this command to “create” the model inside Ollama: ollama create my-custom-ai -f Modelfile
4. That’s it! You can now run your model and chat with it: ollama run my-custom-ai

You will now be chatting with an AI that has your personality, not a generic corporate one.

Conclusion: You Now Have a Personalized AI

In just a few minutes and for zero cost, you’ve successfully fine-tuned a powerful Large Language Model.

This method—using a base model, optimizing with Unsloth, training on Colab, and running with Ollama—is the most accessible and powerful way for anyone to start creating custom AI solutions.

For a complete, step-by-step walkthrough where I build this live, be sure to watch the full video!