New LLM Pre-Training and Post-Training Paradigms (magazine.sebastianraschka.com)

Recent advancements in large language model (LLM) training paradigms have shifted focus to include both pre-training and post-training methodologies, enhancing the capabilities of state-of-the-art models. This evolution is particularly significant for the AI/ML community as it incorporates strategies like supervised instruction fine-tuning and alignment techniques, which were popularized by models like ChatGPT. Four new LLMs—Alibaba's Qwen 2, Apple's AFM, Google's Gemma 2, and Meta's Llama 3.1—have emerged recently, showcasing detailed training pipelines that could influence future research and applications. For instance, Qwen 2 features impressive multilingual capabilities and a large token vocabulary, trained with a unique data filtering approach aimed at quality over quantity. It employs a two-phase post-training process combining supervised instruction fine-tuning and Direct Preference Optimization to better align model outputs with human preferences. Meanwhile, Apple's AFM emphasizes quality data utilization and implements a three-stage pre-training pipeline, including knowledge distillation to enhance on-device model performance. These insights reflect a broader trend in the LLM landscape, highlighting the importance of refining training strategies to improve model efficiency and relevance in real-world applications.