Boulesis-26B-A4B

I made v2.1! The best Boulesis model for now

Composite Gemma 4 RP model (QK task arithmetic + fused LoRA).

The idea was to retain the model's core intelligence and knowledge, while diversifying its prose and making it more decisive. I also wanted to sharpen its attention to context so it could dig deeper into the character card, organically pulling lore and facts into the roleplay rather than just mirroring the user.

As a result, Boulesis moves beyond passive reactivity to genuinely advance the narrative, all while suffering zero catastrophic forgetting of its base intelligence.

Differences between Boulesis versions

v1: Delivers a more dynamic plot and drives the characters to take more action. Suitable for action-oriented scenarios.

v2: Writes very concisely while maintaining logic; it may even slightly deepen the understanding of the character.

v2.1: Comparable in length to v1 and slightly more measured in tone, but it has the best grasp of the characters and an excellent memory for context. Suitable for complex RP sessions involving multiple characters.

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How it's built

Most RP merges blend whole models. This one edits one part of attention and leaves the rest alone.

Part	Source
Body	coder3101/gemma-4-26B-A4B-it-heretic (ARA-abliterated, layers 10–30)
lm_head	copied from Gryphe/Gemma-4-26B-A4B-StyleTune-V2 (one tensor out of 659)
q_proj, k_proj	base + α·(Pantheon-Reasoning-1.1 − unsloth/gemma-4-26B-A4B-it), α = 0.6
v_proj, o_proj	LoRA r=32, alpha=64, 55 projections, baked at effective scale 0.26
Everything else	untouched

q_proj and k_proj decide where the model looks. v_proj and o_proj decide what it carries back. That split comes from mechanistic interpretability work.

My guess was that RP reactivity is mostly an attention routing problem: the model over-weights the user's latest turn at the expense of the character card and prior context. To test this, I grafted the Q/K projections from Pantheon-Reasoning.

The results of this experiment, in brief

A quick summary of the results: I didn't achieve significant gains in the depth of the model's character card analysis. Injecting q_proj and k_proj served more as a counterweight to the LoRA, which tended to disrupt the model's ability to clearly parse the character card. In some tests, the model using only LoRA failed to adhere to the required strict formatting. The most measurable impact came from the lm_head from StyleTune 2. On its own, of course, it offered only minor stylistic improvements. However, when combined with the other operations, the model saw an overall boost in roleplay quality—something I’m certainly happy about.

Using it

YOU WILL GET THE BEST RESULTS WITH THINKING ON!

Recommended settings:

Parameter	Value
Temperature	1.0
Top-K	64
Repetition Penalty	1.05-1.1
Top-P	0.95
Thanks to DifficultyThin8462

If the reasoning doesn't work when connecting GGUF ver. in KoboldCPP and SillyTavern

You need to force this in KoboldCPP. Go to the Content tab and enable these options.

For SillyTavern, it is recommended to set the template as shown:

Credits

Thanks to coder3101 and Gryphe for the fine-tunes, and the entire 26B-Suite team for their intellectual support. Speсial thanks for Naphula, redaihf and Vortex5. You guys are awesome!