The Ultimate Guide to Building an Efficient LLM in 2024 (Continuously Updated)

submited by

Style Pass

2024-11-19 14:30:04

There is a rising demand for building efficient Large Language Models (LLMs), as evidenced by recent developments like SmolLM2, Qwen2–0.5B, and Llama3.2–1B. Efficient LLMs allow us to maximize performance within limited memory constraints. They not only enable high-quality offline tasks on edge devices but also outperform other models within the same memory footprint. In this article, we document the primary new methods, each capable of reducing model size by a factor of 2x or more.

Firstly, train a large language model and then distill it into a smaller one using the output data generated from the larger model. Model distillation involves a smaller “student” model learning to replicate the behavior of a larger “teacher” model, effectively capturing its capabilities with fewer parameters.

Implementing new methods of parameter sharing using loop transformers with residual connections can significantly reduce model size without compromising performance.