test comment (cleaned)

The huggingface space is available to play with for the showcase of potentials of each of the models. Some like Qwen 3.5 9b are able to read text rather well, others not so well. Be wary as you experiment.

https://huggingface.co/spaces/AbstractPhil/think-symbolic-images-testbed

They are powerful models already. Now they produce symbolic thinking captions.

MORE models are being added as Claude prepares them for the space. My apologies If the space restarts during your testing, the process is ongoing.

The current line of models have weaknesses with human subjects currently. Not to worry though, progress my friends. Progress.

I would NOT train models with human subject data from this model yet. The models require refining and reinforcement preparation for correct and incorrect objective responses. That's fine though, there's no shortage of data to reinforce and test.

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I'm going to try some tweaks. Likely if I feed the model some hints, like say from a booru tagger, the model may be more apt to respond correctly.

Okay, so all she really needs is a hint at the prompt in general.

If you feed the image's original prompt in like so;

Look at this picture and think it through in writing, a closer look; then write a caption and a prompt or tags, and the questions a careful looker would still ask, each as `question: what -> answer [record, chain or unknown]`.

You get bland COT outputs. However, if you do this;

Look at this picture and think it through in writing, a closer look; then write a caption and a prompt or tags, and the questions a careful looker would still ask, each as `question: what -> answer [record, chain or unknown]`.
Helper caption:small image caption goes here
Helper tags:any booru tags go here

This tends to solidify the system much more effectively, as the model herself is quite large and quite intelligent thanks to the base model. She doesn't need much, just a nudge. The COT results from this have been considerably more effective and diverse.

It's not a guaranteed fix until I alter the training process a bit and retrain her, but for now the tweak will work to activate the Qwen 9b VLM inference a bit more effectively.

The space is being updated to reflect the new experiment successes. Caption and tags are assistants because Lucia is a smart and capable girl, additionally with Lucia most of the others can also reflect the behavior, or their base forms used to caption the image prior to running the COT image captioning system.

Even qwen 3.5 0.8b is capable of a miniature caption for 9b to process into something thought-related, so these models simply needed a couple tweaks, they work with the correct provocation.

Enjoy the current lineup, just make sure to use the correct prompt and helper tags/prompts until the core model's VLM is reignited as the behavioral norm.

One of the primary COT behaviors rel a, rel b, and rel c did not take for the extraction processing template. Currently, the model defaults to rel b, and very few of the extracted examples had rel a, or rel c. So the behavior requires fixing. To solve this, I'm attempting another lora using relation_a, relation_b, and relation_c instead of the original rel a, rel b, and rel c.

My hope is the model will associate this better than simply seeing "rel a" and so on. We'll see when the 2b version is done in a couple hours.

If the behavior takes I'll get to associating the remaining problematic pieces before v1.1 is prepared. There are a few pieces that will be better informed with associative image + tags + prompting for structural cohesion, instead of requiring them. It IS a VLM after all, so the model just needs to be encouraged.

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So the training did resolve much of the clothing and outfit problems just using the tokens for relation_a, relation_b, and relation_c; but this did not solve the underlying dataset issue related to the initial dataset questioning the b relational item 96% of the dataset, which is the real problem.

The only solution is a more diverse dataset of relational behavior, and a retooling of how relational behavior is to be associated with, providing a better rounded dataset of comparisons. This will help prevent overweighting the relational responses.

That being said, the new train did resolve many of the garment issues, so this leads down a direction for more raw data extraction through more intelligent models to help round out the COT questioning behavior.

Also, the original dataset trained appears to have only a handful of upper garments mentioned throughout the entire dataset. That contributed to the behavior. I'll be resolving this appropriately using regularization techniques, rather than attempting to finetune again in the same fashion. I've found a deepfashion dataset with garments listed per row, so that will be helpful. Appropriate attributions will be given to the dataset captioning systems and the licenses involved.

This is good progress, but not there yet.

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Currently prepping the 44k deepfashion images, as well as working out the correct regularization techniques in order to prevent catastrophic forgetting on Qwen 3.5 9b's text structure for the dual-path image/text behavior.

the V1.1 prototype should be ready by Friday 11/6/2026 but I can't guarantee that yet. Regularization may solve most of the issues unto itself, so if that's the case it may just resolve without more data by Thursday.

Many of the datasets will require linking to other images. This is due to the many licenses required and the space required FROM those images, so the structure will be a bit... odd to work with if you wish to train with the images.

The licensing is important for commercial purposes. Many licenses do not permit commercial usage, so if you wish to replicate results you'll need to create synthetic images, extract from elsewhere, or utilize a divergent process for some of the dataset rows.

That's up to you as a developer to choose your own route, I'll be providing all the necessary licensing for each dataset extracted.

Saturday I will be releasing an official AbstractPowered article showcasing the array of chosen AI for subject association AI training, which will compound all the necessary elements to teach the Qwen 3.5 4b, Qwen 3.5 9b, Gemma 4 E4b and Gemma 4 12b variants high complexity COT extraction capacity.

The models themselves inherently are limited. They do not recognize light, deep complexity clothing, multi-structural systems, and more. They simply aren't the smartest.

HOWEVER, when paired with a series of intelligent finetuned AIs with very specific targets, we can train these Lucia and Ulisse models to properly behave.

There is a multitude of AIs combined together and compounded in order to correctly teach prompting and systemic systems.

With, next week will be a multitude of regularization and model preservation techniques meant to test the excitation of data, rather than reduction or diminishing behavior.

Version 2 of Lucia will be functional, and the COT behavior will be considerably more robust and structurally responsive, as there will likely be 50 or more trained variants tested and measured directly to the results and compared.

In other words, the best results will be the Lucia and Ulisse v2. Far more advanced, far more intelligent, far more diverse, and far more complex.

This pair of models will understand:

1. lighting via face detected lighting, meaning the faces trained will be trained specifically oriented with light, rather than trying to classify the entire scene.
2. Subject association via SAM 3D categorized captioning, sourced directly from a multitude of keypoint datasets and turned into plain English captions. Head pitch/yaw/roll. body rotation body joints for shoulders, chest, torso, hips. Legs and arms rotation with capacity.
3. Object association for grasped, stood upon, sat upon, and directly associated subject to object connections thanks to SAM 3D, specifically designed to train plain English detection using COCO and a multitude of other datasets specifically chosen for association and capacity.
4. Subject outfit complexity, comparative relational outfit understanding within some limitations of deepfashion-multimodal, coco, and a multitude of other selected options based on a multi-combined prompt structure extracted from base Qwen 3.5 4b, AnimeTimm's Giant H, and multiple other AIs specifically designed to assign relational association to the prompting. Two prompt; plain english followed by booru tagging.
5. Museum artstyles, capacity, photography, 3d modeling, simple anime styles, and more.

The VIT half may need additional heads, or modifications. The text half may also need additional heads or finetuning. The process will be altered as necessary, and the structure will be built to behave as required.

Once the pair of models are done, the rest of the choices will follow using the same or similar techniques. Our primary focus being the 2, the others will follow using the best techniques from those two.

This should be enough to prepare our base models to extract the necessary information. If this is not enough, another week of dataset prep before v2.

It's more than likely we can only train the pair of models to EMULATE the results, which when fed with assistant prompts and helpers, the system can associate those as guarantees. Which will be more than enough to prepare the COT if that's all we can yield, but it will introduce biases, so the dataset prep will be ongoing.

The reason for multiple VLMs is crucial here. A wider potential for more outcomes, yields a larger dispersed potential of thought.

I have some experiments I will run with procrustes analysis based on those texts when extracted from powerful berts, to determine how similar they really are. Berts seem small and arbitrary, but they are one of the fastest and most potent analysis tools I can use due to the size, the capacity, and on top of that - a proper rotary trained captionbert is on the menu for next week as well.

The world doesn't lack data for the tasks, but very little of it is shaped into COT format. Hence, the predicament. The solution, is simply more data, and in some cases additional structure to annunciate outcomes with points of interest.