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.

---

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.

---

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.

---

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.