SFT

SFT, short for Supervised Fine-Tuning, is the standard training phase that adapts a base LLM to follow instructions by training it on labeled examples of (input, desired output) pairs. SFT is typically the first stage of the post-pretraining alignment pipeline, followed by preference optimization like RLHF, DPO, or KTO. The technique requires a dataset of high-quality demonstrations — often tens of thousands to millions of examples — covering the target task distribution. Common SFT datasets include OpenAssistant Conversations, Alpaca, Dolly, ShareGPT, Anthropic's HH-RLHF, and the proprietary instruction datasets used by frontier labs. SFT can use full fine-tuning or any PEFT technique like LoRA, with PEFT being the dominant choice for cost reasons. The quality of SFT data has been shown to matter more than quantity — the LIMA paper (2023) demonstrated state-of-the-art instruction following with just 1,000 high-quality examples. AI governance teams document SFT datasets, hyperparameters, and evaluation results as part of their AI compliance lineage for any deployed fine-tuned model.

SFT-tuned models in Centralpoint: Centralpoint routes generation to SFT-tuned models from any provider in a model-agnostic stack, with token metering, prompt locality, and per-skill audit logs. The platform supports both generative and embedded models, and deploys instruction-tuned chatbots through one line of JavaScript on any portal.


Related Keywords:
SFT,,