This tutorial will guide you through the process of fine-tuning a local AI model with your own data to create a specialized assistant that’s an expert in your chosen domain. We’ll use Ollama with Mistral as our foundation, but I’ll also suggest some alternatives.

Introduction

Fine-tuning is a process that adapts a pre-trained AI model to perform better on specific tasks or domains by training it on your own specialized data. This allows you to create custom AI assistants that are experts in your particular field without needing the massive computing resources required for training models from scratch.

Setup Requirements

• A computer with at least 16GB RAM (32GB recommended)
• At least 20GB free disk space
• A modern CPU (for basic usage) or NVIDIA GPU with at least 8GB VRAM (for better performance)
• Operating system: Windows 10/11, macOS, or Linux

Installing Ollama

Ollama is an easy-to-use tool for running and fine-tuning local AI models.

Windows

1. Download the installer from Ollama’s website
2. Run the installer and follow the prompts
3. Open a command prompt and verify installation with ollama --version

macOS

1. Download the macOS installer from Ollama’s website
2. Open the downloaded file and drag Ollama to your Applications folder
3. Open Terminal and verify installation with ollama --version

Linux

curl -fsSL https://ollama.ai/install.sh | sh

Preparing Your Training Data

The quality of your fine-tuned model depends heavily on your training data. Here’s how to prepare it effectively:

Data Format

For Ollama fine-tuning, we’ll use a simple JSON format consisting of conversations. Create a file named training_data.json with the following structure:

[
  {
    "messages": [
      {"role": "user", "content": "Question about your domain"},
      {"role": "assistant", "content": "Expert answer about your domain"}
    ]
  },
  {
    "messages": [
      {"role": "user", "content": "Another question"},
      {"role": "assistant", "content": "Another expert answer"}
    ]
  }
]

Data Collection Tips

1. Gather domain-specific Q&A pairs: Create 50-100 high-quality question-answer pairs specific to your domain.
2. Use diverse phrasing: Include various ways to ask similar questions to improve robustness.
3. Include edge cases: Cover uncommon scenarios related to your domain.
4. Ensure accuracy: Double-check all information for factual correctness.
5. Maintain consistent style: Keep the assistant’s tone and style consistent throughout.

Fine-tuning with Ollama

Ollama uses a custom format called Modelfiles to define and fine-tune models. Here’s how to create one:

1. Create a file named Modelfile (no extension) with the following content:

FROM mistral
PARAMETER temperature 0.7
PARAMETER stop "###"

SYSTEM """
You are an expert in [YOUR DOMAIN]. Provide accurate, helpful, and concise responses based on your specialized knowledge.
"""

# Include your training data
DATASET training_data.json

2. Build your custom model:

ollama create myexpert -f Modelfile

3. Start fine-tuning:

ollama finetune myexpert

This process might take several hours depending on your hardware and the size of your training data.

Alternative Local AI Options

While Ollama with Mistral is excellent for beginners, here are some alternatives:

1. LocalAI

LocalAI is an open-source alternative to OpenAI that supports various models including Llama, Mistral, and others.

Pros:

• Supports multiple model architectures
• REST API compatible with OpenAI’s API
• Easy integration with existing tools

Cons:

• More complex setup than Ollama
• Requires more technical knowledge

2. LM Studio

LM Studio offers a user-friendly GUI for running and fine-tuning local models.

Pros:

• Intuitive graphical interface
• Built-in model discovery
• Easy quantization options

Cons:

• Limited fine-tuning capabilities compared to more advanced tools
• Windows and macOS only (no Linux support)

3. Jan.ai

Jan.ai is a newer, open-source AI platform with a focus on ease of use.

Pros:

• User-friendly interface
• Integrated data management
• Local processing with no data sharing

Cons:

• Still in early development
• Limited model selection compared to alternatives

Testing Your Fine-tuned Model

After fine-tuning is complete, test your model with:

ollama run myexpert

Evaluate your model’s performance:

1. Ask questions that were in your training data
2. Ask questions that weren’t in your training data but are related
3. Ask questions outside your domain to test boundaries

Troubleshooting

Common Issues and Solutions

1. Out of memory errors
   • Reduce batch size in fine-tuning
   • Try a smaller model variant
   • Close other applications to free up memory
2. Poor model performance
   • Increase training data quantity and quality
   • Ensure your training data is diverse and representative
   • Try adjusting the learning rate or number of epochs
3. Installation problems
   • Check system requirements
   • Update graphics drivers if using GPU
   • Verify you have the correct CUDA version if using NVIDIA GPUs

Next Steps

1. Integrate with your WordPress site: Create a simple chat interface that connects to your local AI model
2. Expand your training data: Continuously improve your model by adding more expert knowledge
3. Explore model quantization: Reduce model size for faster inference without significant quality loss
4. Deploy as a local API: Make your expert model available as a REST API for other applications

By following this tutorial, you’ll have created a custom, domain-specific AI expert that runs entirely on your local machine