In this tutorial, we will learn about LLM fine-tuning. This process involves taking a pre-trained model and adjusting its internal parameters to better suit a specific application. Unlike prompt engineering, which uses LLMs as-is, fine-tuning transforms a base model like GPT-3 into a more practical tool tailored to particular tasks. Fine-tuning of a model involves adjusting the internal weights or biases of the pre-trained model. Imagine that we're turning a raw diamond (the model) into a polished gem what fits our needs perfectly. This enhances model performance for specific tasks, and because of this fine-tuning smaller fine-tuned models often outperform larger base models. Techniques: 1. Self-Supervised Learning: Curating training data to align with specific applications. 2. Supervised Learning: Using labeled data sets for fine-tuning. 3. Reinforcement Learning: Optimizing model performance through reward-based adjustments. Fine-Tuning Approaches: 1. Retraining All Parameters: Computationally expensive but comprehensive. 2. Transfer Learning: Freezing most parameters and fine-tuning the head. 3. Parameter Efficient Fine-Tuning (LoRA): Adding new, smaller sets of trainable parameters to reduce computational costs.