Fine-Tuning LLM on Custom Dataset with Single GPU - Complete Tutorial for Sentiment Analysis

Learn to fine-tune the Qwen3 0.6B large language model on a custom dataset for sentiment analysis of financial news using a single GPU. Begin by understanding when fine-tuning is appropriate, then set up your notebook environment and prepare your custom dataset for training. Load the Qwen3 0.6B model and tokenizer, perform token counting analysis, and examine predictions from the untrained model to establish a baseline. Configure the training setup using LoRA (Low-Rank Adaptation) adapters, optimize hyperparameters including learning rate settings, and monitor training progress through TensorBoard logs. Save your trained model and conduct comprehensive evaluation comparing your fine-tuned model's performance against automated prompt engineering approaches using DSPy. Complete the tutorial by uploading your trained model to the HuggingFace Hub for sharing and deployment, gaining hands-on experience with the entire machine learning pipeline from data preparation to model deployment.