如何训练小型语言模型：2026年完整指南

Small language models (SLMs) are gaining dominance in enterprise AI primarily due to their cost-effectiveness. A single GPT-4 API call can cost over $50,000 for 10,000 queries over six months, while a fine-tuned small language model running on a $1,500 GPU can achieve the same results at a fraction of the cost, with data remaining on local servers. This guide outlines three practical approaches to training a small language model: building from scratch, fine-tuning, and distilling from a larger model. Each approach has distinct cost, timeline, and skill requirements.