EASIEST Way to Fine-Tune a LLM and Use It With Ollama

Introduction

This tutorial guides you through the process of fine-tuning the Llama 3.1 language model and running it locally using Ollama. By utilizing the open-source repository "Unsloth," we will work with an SQL dataset to fine-tune the model effectively. This guide is relevant for developers and data scientists looking to customize language models for specific tasks.

Step 1: Get the Dataset

To begin, you need to download the SQL dataset used for fine-tuning.

• Visit the dataset link: Synthetic Text to SQL Dataset
• Download the dataset and save it to your local machine.

Step 2: Understand the Tech Stack

Familiarize yourself with the tools you will be using for this tutorial.

• Ollama: A platform for running language models locally.
• Unsloth: An open-source repository for fine-tuning models.
• Llama 3.1: The language model being fine-tuned.

Step 3: Install Dependencies

Before you begin fine-tuning, you need to ensure all necessary dependencies are installed.

1. Open your terminal.

2. Install Python if it is not already installed.

3. Install the required libraries by running:

   pip install torch transformers datasets
   

4. Clone the Unsloth repository:

   git clone https://github.com/unslothai/unsloth.git
   cd unsloth
   

Step 4: Understand Fast Language Model

Learn about fast language models and how they enable quicker fine-tuning.

• Fast language models optimize training by using efficient algorithms.
• They allow for rapid iteration and testing of your model.

Step 5: Learn About LORA Adapters

Understand LORA (Low-Rank Adaptation) adapters.

• LORA adapters are a technique for fine-tuning models with fewer parameters.
• They help in reducing computational costs while maintaining performance.

Step 6: Convert Your Data for Fine-Tuning

Prepare your dataset to be compatible with the fine-tuning process.

1. Format your SQL dataset into a structure that the model can understand.
2. Ensure the data is clean and properly labeled for effective training.

Step 7: Train the Model

Now, you can begin training the model with your dataset.

1. Utilize the training script provided in the Unsloth repository.

2. Run the following command in your terminal:

   python train.py --dataset your_dataset_file
   

3. Monitor the training process for any errors or adjustments needed.

Step 8: Convert to Ollama Compatibility

After training, convert the model for use with Ollama.

1. Use the conversion script found in the Unsloth repository.

2. Execute the command:

   python convert.py --model your_model_directory
   

Step 9: Create a Modelfile for Ollama

You need to create a model file for Ollama to recognize your fine-tuned model.

1. In the directory of your model, create a file named modelfile.json.
2. Include the necessary parameters in the file, such as model name and version.

Step 10: Check the Final Output

Finally, verify that everything is working correctly.

• Run Ollama by executing:

  ollama run your_model_name
  

• Test your model with sample inputs to ensure it responds as expected.

Conclusion

In this tutorial, you learned how to fine-tune the Llama 3.1 model using the Unsloth repository and run it locally with Ollama. Key steps included downloading the dataset, installing dependencies, training the model, and preparing it for Ollama. As a next step, consider experimenting with different datasets or fine-tuning settings to further enhance your model's capabilities.