openjev-general-lora-bigmenu

OpenJev general adapter, large-menu variant

This is not the recommended checkpoint. Use openjev-general-lora unless you have a specific reason not to.

Published because it is a real artifact from a real experiment, and because a repository that only ships the runs that worked is not evidence of anything. The experiment did not work.

The experiment

The recommended adapter trains at a 2048-token budget, which silently clips label-space augmentation to about 80 options, while the held-out suite presents menus of up to 151. That is a genuine train-and-test mismatch, and label-space augmentation is the largest single effect we have measured, so extending its range was the obvious thing to try.

This adapter is identical except for training at a 4096-token budget with --max-options 160, which produces menus up to 175. Same 279-task mixture, same rank, same learning rate, same backbone.

The result

Six level, one worse, none better, and the mean moved the wrong way.

Banking77 gained 5.8 points, the largest move in the table and exactly the direction predicted. We are not promoting that, because doing so would mean ignoring the ten-point loss on massive_intent beside it.

Why it did not work, as best we can tell

Menu size was not the binding constraint. The recommended adapter already reaches 106x chance on a 151-way menu having trained on nothing larger than 80, so whatever it learned about reading a menu generalised past the sizes it saw. Padding every menu towards 160 distractors appears to cost something elsewhere, and massive_intent at K=60 is the size most likely to be crowded out.

When you might still want this one

If your menus are consistently very large and Banking77-like, this checkpoint is better there. That is one task out of seven, so treat it as a hypothesis about your data rather than a recommendation.

Fine-tuned on a task

Used as a starting point for a task adapter it reaches 0.9615 intent on our healthcare router, against 0.9615 for the recommended adapter's equivalent and 0.979 from base weights. As with every decoder result we have, starting a task adapter from base beats starting it from any general adapter. Train from base.

Links

• Recommended checkpoint: https://huggingface.co/s1lv3rj1nx/openjev-general-lora
• Code and full results: https://github.com/S1LV3RJ1NX/openjev
• Training mixture: https://huggingface.co/datasets/s1lv3rj1nx/openjev-mixture