Edge 458: From Pre-training to Post-training. Inside the Amazing Tülu 3 Framework
A major release by AI2, includes the major components to build post-training pipelines.
An interesting trend taking place in the generative AI space is the shift from pretraining to post-training. Mo9st of the emphasis in the first wave of large foundation models was in the pretraining recipes but that seems to be rapidly changing as it has become incredibly simpler to train models in the entire internet raw datasets. Given the rapid shift, we are currently lacking solid post-training frameworks that are up to the standards of production-ready models. This is the focus of a new release by the team at Allen AI with a new framework called Tülu 3.
Tülu 3 represents a significant stride in the domain of open-source large language models (LLMs). It distinguishes itself by placing a paramount emphasis on post-training techniques to refine pre-trained LLMs and unlock a broader array of capabilities. The post-training process, carefully designed and openly shared, lies at the heart of Tülu 3’s value proposition. This process aims to bridge the gap between open and closed post-training recipes, often shrouded in secrecy, and propel the open-source community towards achieving state-of-the-art performance.