Qwen3-33B-A3B-Stranger-Thoughts-IPONDER-Abliterated-Uncensored

Quick notes:

The "Iponder" version (uses Brainstorm 5x by DavidAU, see below) AND abliterated, and uncensored version of this model:

https://huggingface.co/DavidAU/Qwen3-33B-A3B-Stranger-Thoughts-IPONDER

(all settings, templates, how to change experts, etc etc apply to this model too.)

Special thanks to team Mradermarcher for the quants:

https://huggingface.co/mradermacher/Qwen3-33B-A3B-Stranger-Thoughts-IPONDER-Abliterated-Uncensored-GGUF

https://huggingface.co/mradermacher/Qwen3-33B-A3B-Stranger-Thoughts-IPONDER-Abliterated-Uncensored-i1-GGUF

What is Brainstorm?

Brainstorm 5x

The BRAINSTORM process was developed by David_AU.

Some of the core principals behind this process are discussed in this scientific paper : Progressive LLaMA with Block Expansion .

However I went in a completely different direction from what was outlined in this paper.

What is "Brainstorm" ?

The reasoning center of an LLM is taken apart, reassembled, and expanded.

In this case for this model: 5 times

Then these centers are individually calibrated. These "centers" also interact with each other. This introduces subtle changes into the reasoning process. The calibrations further adjust - dial up or down - these "changes" further. The number of centers (5x,10x etc) allow more "tuning points" to further customize how the model reasons so to speak.

The core aim of this process is to increase the model's detail, concept and connection to the "world", general concept connections, prose quality and prose length without affecting instruction following.

This will also enhance any creative use case(s) of any kind, including "brainstorming", creative art form(s) and like case uses.

Here are some of the enhancements this process brings to the model's performance:

Prose generation seems more focused on the moment to moment.
Sometimes there will be "preamble" and/or foreshadowing present.
Fewer or no "cliches"
Better overall prose and/or more complex / nuanced prose.
A greater sense of nuance on all levels.
Coherence is stronger.
Description is more detailed, and connected closer to the content.
Simile and Metaphors are stronger and better connected to the prose, story, and character.
Sense of "there" / in the moment is enhanced.
Details are more vivid, and there are more of them.
Prose generation length can be long to extreme.
Emotional engagement is stronger.
The model will take FEWER liberties vs a normal model: It will follow directives more closely but will "guess" less.
The MORE instructions and/or details you provide the more strongly the model will respond.
Depending on the model "voice" may be more "human" vs original model's "voice".

Other "lab" observations:

This process does not, in my opinion, make the model 5x or 10x "smarter" - if only that was true!
However, a change in "IQ" was not an issue / a priority, and was not tested or calibrated for so to speak.
From lab testing it seems to ponder, and consider more carefully roughly speaking.
You could say this process sharpens the model's focus on it's task(s) at a deeper level.

The process to modify the model occurs at the root level - source files level. The model can quanted as a GGUF, EXL2, AWQ etc etc.