What is SFT?

SFT commonly refers to Supervised Fine-Tuning. It is a technique used in machine learning, particularly in the context of large language models (LLMs). SFT involves taking a pre-trained LLM and further training it on a dataset of labeled examples, where each example consists of an input (e.g., a prompt or question) and a desired output (e.g., a response or answer). This fine-tuning process adapts the model to a specific task or domain, improving its performance on that task compared to the original pre-trained model. It is commonly used to align the model's behavior with human preferences or to teach it to follow specific instructions.