Train Your Model - Fine-Tuning LLM
Learn how to fine-tune an open-source LLM into a specialized expert for your specific task. Master the complete engineering workflow from data prep and QLoRA training to evaluation and deployment on the Hugging Face Hub.
Can't get good enough results from prompt engineering? Even with GPT-5 and Gemini 2.5 Pro? This is where fine-tuning comes in. It's the process of taking a pre-trained Large Language Model and specializing it for your exact needs. By training a model on a curated dataset of your specific task, you create a reliable, efficient, and cost-effective AI asset that you own and control.
This tutorial guides you through the complete, real-world workflow of fine-tuning. We will take a small, open-source Qwen31 model and transform it into an expert financial sentiment analyst. You will learn the entire engineering lifecycle: preparing a high-quality dataset, establishing a clear performance baseline, training the model efficiently using techniques like QLoRA, evaluating the "before and after" to prove its value, and finally, deploying your new specialist model to the Hugging Face Hub for anyone to use.
Tutorial Goals
- Understand when to fine-tune versus when to use RAG or prompt engineering
- Master the full fine-tuning lifecycle, from data preparation to final evaluation
- Implement memory-efficient training on single GPU using QLoRA
- Merge and save a standalone model, ready for production inference
- Evaluate the fine-tuned model and compare it to the rest of the experiments
- Share your model with the world by deploying it to the Hugging Face Hub