Three Stages of Developing Large Language Models and Practical Guidance

Development of large language models (LLMs) can be divided into three stages: the Build stage (data preparation, attention mechanism construction, architecture definition), the Pre‑training stage (pre‑training, training loops, evaluation, loading weights), and the Fine‑tuning stage (task‑specific fine‑tuning or instruction tuning using labeled or instruction datasets).

LLMs can be used via public or proprietary services, run locally with tools such as LitGPT, or deployed as custom models accessed through private APIs.

Key factors in LLM development include data quality and scale (e.g., GPT‑3 trained on 4.99 trillion tokens, Llama 3 on 15 trillion tokens), model architecture (multi‑head attention, feed‑forward layers, depth, head count, embedding size), training process (pre‑training, fine‑tuning, batch size, loss tracking, performance evaluation), and evaluation/comparison using benchmarks like MMLU or platforms such as LMSYS ChatBot Arena.

Practical advice: training a model from scratch is costly and rarely needed; continual pre‑training can add new knowledge but remains expensive; fine‑tuning is suitable for specialized use‑cases; preference‑based fine‑tuning can improve helpfulness and safety for chatbot applications.

Overall, the document provides a comprehensive guide to understanding and building LLMs.