Next up is a 4 day ablation and full-stage prelim adapter constellation setup for Qwen 3.5 0.8b targeting image-centric behavior. This task set will be targeting rules based on captioning with finetuned behavior for math, positioning, semantic behavior, and so on. This will include a portion of coco and a portion of the Qwen Image Lightning extracts as well to provide some solidity.

This will also be targeting certain overlapping continuity sharing, which is currently bleeding certain decisions into the "new" task from the first collective causing certain tasks when active or not to essentially be KIND OF there but not really. This problem is being directly addressed by providing the necessary attraction to a memorization drop path and a few other experimental tests to test 3.5's aleph's responses to the mathematics per task.

With that each of the major claims will be ablated with a second Qwen alongside, including a tinystories task, and a few other tasks as well that line up directly with the original.

This will be a 4-5 day process, so it's not going to be out overnight. It ought to be ready by next Monday, with that will be the article ft3, the full ablation comparison, and a full writeup for the structured basin before we begin scaling testing.

First targets for scaling will be VL models, coding experts, and multiple additional models as well upon testing the success rate of the 0.8b model. The scaling principle and the rules of scaling apply differently to alephs than normal structures, which means the delicate nature of the mathematics will need a bit of finesse unless you plan to just smash numbers in.

I mean if you want to smash numbers in it'll probably work at this point. The things are pretty robust. I wouldn't advise doing any major trains until I get the ablation studies together though.

Thanks for reading, have a good weekend my friends. I'll likely be continuing training on the json-anima as well over the weekend, so stay tuned if you're interested in that one.

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I also forgot to mention, this process will be compartmentalized and a peft-format variation built upon testing and composite utilization.

Essentially once the tests give me the okay, I'll build a proper PEFT format. Until then, I've spent enough time building PEFT-esque loras that have been hit-or-miss. Lets do this one right so it works more often than not, instead of having to guesswork with params.

This adapter when built correctly ought to be easy to use. Pick a model, run the peft trainer, lora snaps to the side, the autoscaling ruling does it's job, you tinker with a few sliders, and it's ready to go. Unlocks new sliders at runtime if you want, if not leave it to autotuning.