W2 2 Instruction fine tuning

This episode explores methods for improving the performance of Large Language Models (LLMs) for specific use cases. The podcast begins by highlighting the limitations of using one-shot or few-shot inference with smaller LLMs, noting that including multiple examples in prompts can be inefficient and may not always guarantee success. More significantly, the episode introduces fine-tuning as a superior solution, contrasting it with pre-training. Fine-tuning, a supervised learning process, uses labeled prompt-completion pairs to update the LLM's weights, thereby enhancing its ability to generate accurate completions for specific tasks. Instruction fine-tuning, a particularly effective method, is detailed, showcasing how it trains the model using examples that demonstrate desired responses to specific instructions. For instance, examples are provided for tasks such as classifying reviews and summarizing text. The process involves preparing training data (potentially using prompt template libraries to adapt existing datasets), dividing the data into training, validation, and test sets, and using the cross-entropy function to calculate loss and update model weights. Finally, the episode concludes by emphasizing that this instruction fine-tuning results in an "instruct model," a refined version of the base model, better suited for the targeted tasks, and that this method has become the standard approach for fine-tuning LLMs.