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.

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.

Experimental flagship; Qwen 3.5 9b Lucia
Experimental faster; Qwen 3.5 4b tobenamed
Experimental fastest; Qwen 3.5 0.8b tobenamed -> the non-thinker

Almost all major experiments performed using this model has been successful or semi-successful thus far. The original writers for the data are providing enough inference and information for the model to begin THINKING in the image words when seeing pictures, not just guessing or relating to miniature pieces of that image. THINKING about the STRUCTURE of that image, the PURPOSE, the MEANING BEHIND that image.

Lucia, the one who thinks in images.

Our rotary system /NEEDS/ a teacher. A mentor that grounds the model in reality so the collective can learn effectively.

If we ABSOLUTELY require it, we can utilize Qwen 3.8 27b, but that will run considerably slower and train considerably slower. So 3.5 9b is the target for now. If a better model between now and then is discovered or emerges, we will utilize that model.

Reasons:

1. Qwen 3.5 9b is effective enough as an individual model to finetune as necessary into any shape the collective will require.
2. Takes up about 10 gigs of vram, so the model is compact enough to fit on most cards.
3. Qwen is used commonly for many projects; Qwen Image Edit 2.1 being a particular choice, ideal for testing the direct capacity of Beatrix.
4. Qwen is stable and intelligent.

I had a great deal of fun when playing with Qwen Edit 2.1 using the primary, turbo, and playing with the lightning lora as well. The lightning lora seems to snip out some pixels on the left for editing so I'll need to figure out what's going on with that or if it just does it by default.

Our release finetune will be designed to think in images. Very different than a prompt synthesizer. Trained full of key phrases and anchor information, specifically trained to think in images to produce the exact format of information we require to frame for our upcoming media curation collective.

A master who thinks in media is required, and with that we need the intelligence multiple Qwen 3.5 9b finetunes will bring to the table to train our pieces.

Finetunes to extract data for pretrain data that doesn't exist, basically.

The T2I and I2I of Qwen vision's prompt rewrite versions of the 9b were both tested, neither of them produce the necessary shape our data requires yet. However, we're getting closer day by day.

We're folding Gemma 4's VLM system into the mix. The net can be cast widely and the data can be diversified via many sources rather than just a handful.

The Qwen 3.5 symbolic image thinkers will be known as Lucia.

Haven't picked a name for Gemma yet. We'll get started and see how well the model performs by the get-go, full statistics and target information accumulation like the qwen line.