Supervised Fine-Tuning
Supervised Fine-Tuning (SFT) is a crucial step in preparing LLMs for real-world applications. Following the initial pre-training phase, where an LLM learns to predict the next token in a sequence, SFT refines the model’s capabilities using carefully curated pairs of instructions and corresponding answers. This process serves two primary purposes: it teaches the model to understand and follow a specific chat format, effectively transforming it into a conversational agent, and it allows the model to adapt its broad knowledge base to excel in targeted tasks or specialized domains.
The importance of SFT lies in its ability to bridge the gap between a model’s general language understanding and its practical utility. By exposing the model to examples of desired input-output patterns, SFT shapes the LLM’s behavior to align with specific goals, whether they involve task completion (such as summarization or translation) or domain expertise (like...
