Teach LLM Something New - LoRA Fine Tuning on Custom Data

Learn to fine-tune a pre-trained Large Language Model (LLM) using LoRA (Low-Rank Adaptation) technique to teach it custom information in this hands-on coding tutorial. Master the complete workflow of customizing an AI model by working with the Hugging Face Transformers library and Python to load, modify, and retrain the Qwen2.5-3B-Instruct model. Discover how to prepare custom datasets in prompt/completion JSON format, perform dataset tokenization, and implement Parameter-Efficient Fine-Tuning (PEFT) with LoRA for memory-efficient training. Follow along as you set up the development environment using Conda and Jupyter Lab, load and interact with pre-trained models, create training data that teaches the model fictional information, and apply LoRA configuration targeting specific model components like q_proj, k_proj, and v_proj modules. Explore the tokenization process for preparing data for training, understand the training loop implementation, and learn best practices for fine-tuning including important considerations before starting the training process. Practice saving your customized model and performing inference to test the results, demonstrating how the fine-tuned model now responds according to your custom training data rather than its original training.