Annual Report and Model Card LY2025
Architectural Overview
The Chairmon and the Board of Directors are pleased to unveil that the Corp is now one of the premier A.I. companies in the Condo Association. After a year of high-velocity research and development, this next-generation A.I. model is ready for deployment in a wide variety of real-world use cases. The executive suite is excited to present a detailed demonstration of this model’s abilities.
But first, let’s take a look at how it was trained.
Base Model Training Data
To ensure that the model is helpful and friendly, it underwent a computationally expensive training process that began with the tokenization and vectorization of a high volume of experiential data. Here you see a representative sample of the sorts of experiences that have gone into ensuring that this model accurately reflects the goals and values of our stakeholders and trainers, as well as their activities in the past year.
Value Alignment and RLMF
After base model training was complete, the model was aligned with our corporate values and mission statement by means of a rigorous process of Reinforcement Learning from Mon Feedback (RLMF).
Careful attention was paid to questions such as:
- Is the experience friendly? The Board feels that all of the experiences in question took place in friendly locations, although some were more formal and others were more laid-back.
- Is the experience helpful? The answer here is again unanimous. The COO, for example, states that both the appeldryck from the appelfarm, and the appelbox from the appelstore, were helpful in maximizing emolgitude. The Chairmon himself can vouch that several of the experiences in the training set were very helpful in obtaining donuts and donut crumbs. The Chairmon provided extensive positive reinforcement on this point, which has no doubt had a beneficial effect on the behavior of the resulting A.I. model.
- Is the experience honest? Yes! Everyone involved agrees that these training specimens are realistic depictions of real things that happened with real Pokémon. Some of whom are even life-size!
As a result of this training process, the model is ready to be used in a variety of zero-shot tasks that are directly relevant to the Corp’s business goals. We believe that the model will show broad utility in the future, but it has particular promise in solving one of the most critical computing tasks of our time—the Donut Classification Problem.
Exclusive Preview
We have arrived at the bottom of the report, where you can see (at left) that our state-of-the-art advanced A.I. model is doing a very accurate job on a variety of real-world tasks, including: recognizing chocolate donuts, recognizing blueberry donuts, recognizing strawberry donuts, and recognizing maple donuts. This covers several critical use cases and will free up our stakeholders and customers to do important things that the model can’t do, like eating the donuts.
Ummmmmmmmmmmmmmmmmmmmmmmmmmmmmm.