AbstractPhila PRO

Qwen 3.5 9b's VLM should be enough to generate a full dataset system for the rows. Sonnet is going to take weeks at this rate, so I'm testing some solidifications.

A text only lora KIND OF worked, but it wasn't strong enough. Also ~900 image rows is clearly not enough. I'm going to need a full synthetic symbolic engine if I want something of legitimate reuse.

It SHOULD be powerful enough to workhorse a full pipeline structure when it's ready. Qwen 3.5 9b is pretty powerful.

The model training both PEFT lora or full finetune fit snugly on a colab RTX 6000 pro, so it'll train pretty quick. I ran rotary for 8 days on them, this little prototype should be done in a day or two with any luck. Then a large scale extraction, then we can start teaching some experts some legitimate capacity.

When prepared it goes in the list of prepared utilities released by the AbstractPowered systems. I've been putting off compounding them, but I plan to finish the compounded list and release the handful of valid utilities to the AbstractPowered as release models, and likely next week or the week after I'll prep the AbstractPowered research line sector.

I'm going to have the claudes continue generating the image chain of thought system while I sleep.

There are still more steps between here and training the next model, one of which involves image thought being in the experiment line. This will include a form of VIT, a form of diffusion, and a form of traditional classification experts.

I'm thinking around 20,000 should be enough to train a fair-sized qwen for the task. It's only about 4,000 so far, so it'll take a few more weeks of off and on. I'll try a finetune here or there to see if it gets better, then run a couple datasets to compare the results

More than likely the better qwen will need a task trained VLM lora, which I've never built a combination vision + text lora so that'll be interesting. There's probably a series of losses that make sense more than just using the clip cross-contrast so I'll need to research a bit.

Extraction with one of the qwen 8b or 9b models should provide a good enough result to train another model, but it's still just going to be qwen results tuned to the shape unless I run a few other processes. Qwen 3.8 should be strong enough, hopefully, but I'm not even sure if 3.8 has a VLM yet so I'll have all that worked out sooner than later.

We have our rotary selector for the next version of the model, Dante.

The current concept that I've dubbed multi-rotary pairings, extends to a solid context window space of around 14 million or so pairs so far. This is conjunctive using different sized pairings between 2 and 16, as per the aleph set mathematics capacity allows.

There is a bit of a rift that forms still at this scale, but the rotary itself can easily encompass framed sets of 14 million bytes worth of paired frames, treated as though they are individual lookups thanks to the prototype "AlephFrame" rotary system.

This doesn't merit a full conjunctive LEARNING capacity, but it does converge a structural guarantee capacity.

This frame is a little different than standard attention, but it'll snap right to models. So far the pairings are a little wild on some edge cases, but I have another upcoming test that I believe will resolve that hyperbolic rifting.

Currently we're at around a 0.00001% invalid addressing fault, which when scaled up is a bit substantial. However, with additional pairings the system cleans up the bpb and the output cleans.

Essentially this means entire context window can be a lookup table, so that's pretty quick at least. The models with their arms train at around 750k byte/s for now, and the scaled up MOE variant will train about as quick as you wish to train the experts for.

Essentially, what we're looking at here, is a cascade solidity system with independent experts that can be removed and replaced, simply existing within that space defined. Within that spatial geometry, it is completely cut off from any external stimuli, and that space is perfectly preserved from the external state of the model.

Meaning, you can simply snap something out and put a new interface in. Small interface, big interface, doesn't matter. Input, output.

We'll be targeting the space over 1 million context window, which should help solidify the system's internal shared structural space.