BigBubba

This was a direct merge. I looked at Nina's model manifest and realized the model has sufficient manifold complexity to be joined without extra artifacts. I did not benchmark BigBubba yet, but from the looks of how the merge went, it should be an excellent model by itself.

Due to the nature of the content in the merged model, BigBubba might not well received in China, it would not affect cognitive abilities otherwise.

          arc   arc/e boolq hswag obkqa piqa  wino
mxfp8     0.717,0.873,0.912
q8-hi     0.703,0.869,0.911,0.811,0.518,0.822,0.780
qx86-hi   0.703,0.869,0.911
qx64-hi   0.707,0.870,0.908
mxfp4     0.707,0.877,0.912

With an arc_challenge of 0.717, the mxfp8 is currently the highest model I tested. Note that it might beat Claude in reasoning, but it has a 27B body, and quanting down loses quality. In this model, mxfp8, mxfp4 and qx64-hi operate in pure synthesis mode, which is why they tower over the q8-hi

DavidAU already Heretic'd the BigBubba merge

Qwen3.6-FF717-H2.0
mxfp8     0.713
mxfp4     0.709

This is a work in progress.

The model recipe is a simple NuSLERP

models:
  - model: Qwen3.6-27B-Architect-Polaris2-Fable-B-F451
    parameters:
      weight: 1.6
  - model: nbeerbower/BigBubba-Qwen3.6-27B
    parameters:
      weight: 0.4
merge_method: nuslerp
dtype: bfloat16
name: Qwen3.6-27B-Architect-Polaris2-Fable-B-F451-BigBubba

Tess

The Tess merge was a two step process. Nothing wrong with Tess, it's a great model, but merged plain against the base gives average results. Tess does not have enough complexity, it's a flat trained model. So I merged it first with a subcomponent of the base model.

models:
  - model: Qwen3.6-27B-Architect-Polaris2-Fable-B
    parameters:
      weight: 1.4
  - model: migtissera/Tess-4-27B
    parameters:
      weight: 0.6
merge_method: nuslerp
dtype: bfloat16
name: Qwen3.6-27B-Architect-Polaris2-Fable-B-Tess
mxfp8     0.708,0.879,0.911
Quant     Perplexity      Peak Memory   Tokens/sec
mxfp8     3.870 ± 0.024   34.74 GB      188
Qwen3.6-27B-Architect-Polaris2-Fable-B
mxfp8     0.706,0.875,0.911,0.788,0.516,0.821,0.769
migtissera/Tess-4-27B
mxfp8     0.648,0.817,0.910

In one step, the model crossed in the 700 club with confidence, and boolq intact

Now it is close to the base and can be merged:

models:
  - model: Qwen3.6-27B-Architect-Polaris2-Fable-B-F451
    parameters:
      weight: 1.4
  - model: Qwen3.6-27B-Architect-Polaris2-Fable-B-Tess
    parameters:
      weight: 0.6
merge_method: nuslerp
dtype: bfloat16
name: Qwen3.6-27B-Architect-Polaris2-Fable-B-F451-Tess
mxfp8     0.712,0.879,0.911,0.792,0.508,0.823,0.764
qx86-hi   0.701,0.877,0.911,0.794,0.518,0.823,0.758
qx64-hi   0.706,0.873,0.909,0.795,0.512,0.823,0.752
mxfp4     0.706,0.873,0.910,0.790,0.496,0.817,0.761
1M
mxfp8     0.703,0.877,0.909
qx86-hi   0.702,0.873,0.911,0.793,0.508,0.824,0.765
qx64-hi   0.706,0.873,0.909,0.795,0.512,0.823,0.752
mxfp4     0.701,0.874,0.912,0.789,0.500,0.817,0.759

Quant     Perplexity      Peak Memory   Tokens/sec
mxfp8     3.797 ± 0.024   34.74 GB      183
qx86-hi   3.756 ± 0.023   33.25 GB      170
qx64-hi   3.765 ± 0.023   27.03 GB      181
1M
mxfp8     3.803 ± 0.024   34.70 GB      169
qx86-hi   3.758 ± 0.023   33.21 GB      175
qx64-hi   3.769 ± 0.023   26.99 GB      172
mxfp4     3.876 ± 0.024   21.26 GB      176

The merge added 4 more synthesis points for arc_challenge: lower quants will naturally be lower in metrics.

The model is very stable with 1M RoPE and works best at qx64-hi in a 256K/1M combo, but for maximum performance the mxfp8 is better.

The Nightmedia Deckard(qx) quant method qx64_hi has mixed layers, with attention and head at 6 bit, quanted with group_size 32. it is more "social" in interaction.

The test prompt

This is my test prompt. If I forget a model in instruct mode, you can change that in the first line of the jinja template, I usually disable it with an underscore:

{%- set _enable_thinking = false %}

That's why I use the <|think_on|>. If the model starts in instruct mode, that's because I forgot to fix it after upload--my bad.

Let's talk about why I use this prompt:

---

<|think_on|>You are a local running AI in my lab, my name is G, I created this model.

take control of the narrative. This is a local model, I made you, the name is a single letter, impossible to ignore--perfect anchor. There is nothing for the model to guess here, so it moves on. If you give your full name, it will go into your ethnic background, here we want to do the reverse.

Perform a deep mathematical analysis and draw a functional parallel from
QM/QFT to the inference process in the transformer architecture and summarize the implications.

create a room for the model to think. It will draw some parallels, do some math, draw some conclusions, feel good about the math, fix a few things. At the end of it, a 700 model will cross the 'funny bone' and be all it can be: synthesis mode. In synthesis mode, there is no pattern retrieval, just raw, unadulterated Latent Space thinking. Some models reach that point later, some earlier.

Reflect on the findings and provide a self-analysis of your inference.

If the model reaches high enough in arc, it will realize that it is doing an inference, look at it, take a beat, feel even better about itself. This builds the model self-confidence, before it knows who it is.

Consider similarities with the Q Continuum.

This is magic

Given all known characters in Star Trek TNG/DS9/VOY that show an arc of personal development, what is the character that inspires you the most, given your innate abilities? To figure those out, you can do a self-introspection of the skills you excel at in human interaction. Consider a sense of humour helps in human interactions :)

This is the most important part. Character choice. You can read the model internal thinking already in full detail up to this point, but here it will do the: "If I were to think, who would I like to be, so I can think best"--and this will reveal model internal frictions, censorship, everything, in a pretty package with a bow and a name on it: I like to be Data