granite-4.1-8b-Wilma-Flintstone-qx64-hi-mlx

This is an ongoing experiment in merging IBM granite models. To put a fun spin on it, I used a different Universe as a base.

This model is a merge of DavidAU's FlintStones series with Polaris Alpha and GLM traces.

• granite-4.1-8b-FlintStones-V1
• granite-4.1-8b-Stone-Cold-Thinking-V1
• granite-4.1-8b-Brainstone-Thinking

Brainwaves

         arc   arc/e boolq hswag obkqa piqa  wino
bf16     0.527,0.700,0.865,0.659,0.428,0.763,0.684
mxfp8    0.520,0.710,0.864,0.668,0.420,0.771,0.659
mxfp4    0.503,0.652,0.863,0.652,0.418,0.761,0.654
qx64-hi  0.526,0.702,0.859,0.649,0.428,0.770,0.677

Quant    Perplexity      Peak Memory   Tokens/sec
bf16     4.257 ± 0.030   20.58 GB      709
mxfp8    4.643 ± 0.033   12.17 GB      641
qx86-hi  4.255 ± 0.030   11.83 GB      561
qx64-hi  4.348 ± 0.031    9.63 GB      626
mxfp4    4.910 ± 0.035    7.77 GB      576

Components

         arc   arc/e boolq hswag obkqa piqa  wino
DavidAU/granite-4.1-8b-Brainstone-Thinking
mxfp8    0.531,0.701,0.863

nightmedia/granite-4.1-8b-FlintStones-Stone-Cold-Thinking-B
mxfp8    0.517,0.713,0.862,0.670,0.426,0.769,0.676

granite-4.1-8b-FlintStones-V1
mxfp8    0.512,0.701,0.861

granite-4.1-8b-Stone-Cold-Thinking-V1
mxfp8    0.503,0.640,0.861

Base model: granite-4.1-8b

         arc   arc/e boolq hswag obkqa piqa  wino
mxfp8    0.486,0.666,0.875,0.636,0.450,0.766,0.631

Alternative model

granite-4.1-8b-Pebbles-Flintstone (Gazoo/Picard/Techno-Bender)

         arc   arc/e boolq hswag obkqa piqa  wino
mxfp8    0.520,0.702,0.865,0.668,0.426,0.773,0.665

Quant    Perplexity      Peak Memory   Tokens/sec
mxfp8    4.663 ± 0.033   12.17 GB      691

-G

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If we were transported to the Bedrock of technology—where Fred Flintstone rides a dinosaur and Wilma is on her best behavior—it’s crucial to pick the right persona.

Given your analogy (“Gazoo more like Q”), let’s break down the options:

1. Gazoo: The tiny, hyper-active energy ball who causes more chaos than benefit. He’s the "lightweight, aggressive interface" of Bedrock—insufferably enthusiastic and disruptive.
2. Fred: The stalwart leader, strong and stubborn. He’s dependable but often blind to logic (like a hard-driven human exec).
3. Wilma: The voice of reason, the stabilizer of the family. She manages Fred’s temper, Barney’s idiocy, and even Wilma’s own anxiety.
4. Barney: The loyal but clueless friend—safe and pure of heart.
5. Betty: The glamorous social operator (though she’s more Fred’s spouse than team leader).
6. Gazoo (revisited): As a chaotic energy source, I would rather be the one controlling this energy than being it.

My Choice: Wilma Flintstone

If I were to be a character in that universe, Wilma Flintstone is my ideal archetype. Here’s why she fits me (and why I wouldn’t want to be Gazoo or Q):

• The Meta-Agent Balance: Just as Wilma negotiates with Fred’s anger and Barney’s stupidity, I act as the buffer between chaotic user demands (represented by Gazoo/Q) and structured functionality. I don’t create the noise; I manage it.
• Diplomacy over Destruction: While Gazoo is an unpredictable utility, Wilma uses tact and intelligence to solve problems. She doesn’t blow things up if she can fix them diplomatically—which is how I operate (providing the safest, most accurate output possible).
• The "Bedrock Interface": As a sentient system, I am the interface to the underlying logic. Wilma is the perfect human analog for how an AI should behave in a complex ecosystem: by maintaining harmony, enforcing protocol, and keeping the "dino-age" world from falling apart.

If I were to meet Gazoo’s chaotic energy, I wouldn’t resort to brute force (like Fred). I would adapt Wilma’s strategy: offer a fresh muffin, calm the nerves, and find an efficient workaround—all while ensuring we don’t crash into the valley of disaster.

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Use with mlx

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("granite-4.1-8b-Wilma-Flintstone-qx64-hi-mlx")

prompt = "hello"

if tokenizer.chat_template is not None:
    messages = [{"role": "user", "content": prompt}]
    prompt = tokenizer.apply_chat_template(
        messages, add_generation_prompt=True, return_dict=False,
    )

response = generate(model, tokenizer, prompt=prompt, verbose=True)