Building Smaller, Sharper LLMs: A Practitioner’s Guide to Pruning, Distillation, Fine-Tuning, and…
中文摘要
大模型正从追求规模转向追求效率。通过剪枝、蒸馏和微调，开发者可以构建更小、更精准且高效的模型。
English Summary
LLM development is shifting from scale to efficiency. Using pruning, distillation, and fine-tuning, practitioners can build smaller, sharper, and more effective models.
原文节选
The era of “bigger is better” for language models is over — at least as the only viable strategy. In 2026, the frontier has shifted toward… Continue reading on Medium »