Part 6/11:

The Custom Model Selection Algorithm: Innovation at Its Core

One of the standout contributions is the development of a custom model selection algorithm, not available commercially, which intelligentizes the tuning process. Here's how it works:

• Initial step: Pick the middle model from a sorted list (by size or parameter count).

• Evaluation: Fine-tune on the specified dataset and check if it meets the minimum accuracy threshold.

• If met: Attempt to find a smaller, more efficient model by narrowing the search to smaller models on the left side, repeatedly testing the midpoints.

• If not met: Shift to larger models on the right side, iteratively testing midpoints until the desired accuracy threshold is achieved.