In this article, I will guide you through the process of fine-tuning a language model (LLM) tailored to your specific needs. We will focus on the open-source model Qwen2.5-0.5B, aiming to enhance its ability to extract information from text formatted in JSON according to a predefined structure. I will begin with a brief overview of the stages involved in training language models, followed by practical steps: preparing the dataset, configuring LoRA, training the model in Google Colab, and evaluating performance metrics before and after fine-tuning. Additionally, we will assess how well the model retains its prior knowledge. The discussion will include Python code snippets to illustrate the process.
How to Fine-Tune an LLM: A Step-by-Step Guide
This article provides a detailed guide on fine-tuning a language model for specific tasks, using the open-source Qwen2.5-0.5B as an example.
5 min readTechnology