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Definition

Supervised Instruction Tuning

Supervised Instruction Tuning (SFT) is a training phase where a pre-trained base model is fine-tuned on a curated dataset of instruction-response pairs. This teaches the model to understand prompts, adopt an assistant persona, and output responses in a structured format.

Frequently Asked Questions

What is the difference between SFT and base pre-training?

Pre-training uses raw text to learn word statistics. SFT uses structured prompt-response templates to teach the model how to behave as an assistant.

What follows Supervised Instruction Tuning in modern alignment?

Usually preference optimization phases like RLHF (Reinforcement Learning from Human Feedback) or DPO (Direct Preference Optimization).

Quick Facts

  • CategoryModel Training
  • Key ApplicationConversational model preparation, API JSON formatting models, and customer service fine-tuning.

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