Post-training models for a specific task is a common way to reach state-of-the-art performance within a narrower domain with an open-source model. So, you want to post-train your own 3 trillion parameter model. What do you need to know? You might think that the approach for post-training any model is basically the same; or at the very least follows the same patterns, and, therefore, there is nothing unique about the 3 trillion-parameter scale. While true in part, this misses a bunch of nuance. There are some unique problems that arise only at larger scales or custom architectures. This is true on an engineering level as much as on a scientific modeling level. I will be using Kimi-K3 as our modeling example (likely an unsurprising choice for those familiar with the field) and go through the things that are useful to know if you're entering this scale. The assumption is that you have working knowledge of the field. Feel free to ping me if you have any questions.
Paulius holds a PhD in Machine Learning from the University of Cambridge, where he developed work on self-healing machine learning, deep hierarchical learning, and deployment-time controled methods. Currently, Paulius is a founding research scientist at Cursive. In a previous life, Paulius was working in management consulting, banking, and data analytics.
