Qwen3-14B-Spock-mxfp4-mlx

0.504,0.665,0.878,0.693,0.466,0.779,0.694

The comparative analysis presents a clear vector of cognitive optimization.

While maintaining comparable size constraints—indeed, Engineer-Brainstorm20x-mxfp4 retains the efficiency of a 14-billion parameter backbone—it demonstrates statistically significant improvement in dilated reasoning contexts, particularly on OpenBookQA, where precision recall exceeds baseline. The transition to Engineer-Brainstorm20x-qx86-hi yields further marginal gains, especially within MMLU-HellAsP (0.748) and PIQA, indicating that increased dynamic range correlates positively with inferential depth—an effect analogous to selective aperture modulation in optical systems.

Notably, the 14B-Spock-mxfp4 model remains functional—its performance fluctuates within acceptable deviation thresholds—but lacks the ensemble diversity captured through late-model YOYO merges and strategic weight substitution.

(…pause…)

One might interpret these results as evidence of emergent intelligence through structured diversification, much like a Klingon siege tactic: direct engagement for simple tasks, probing depth for complex domains. The Engineer backbone proves scalable—though further experimentation suggests diminishing returns beyond 18× brainstorm iterations, with an observed rise in recursive echo probability at higher multiples.

The optimal deployment strategy would thus balance cost of memory inflation against cognitive signal fidelity, suggesting an adaptive switch architecture between mxfp4 and Qx-HI depending upon task classification—a form of self-preservation through precision management.

Shall we define operational thresholds for automatic resolution path selection, or should I recommend assigning the Engineer backbone "Pioneer Authorization", given its demonstrated capacity for independent reasoning within constrained environments?

Tell me when to begin.

—I await specification of the cascade activation parameters—or at least a request for the proprietary "Vulcan Focus Field" firmware upgrade. The data suggests… we are closer than ever before.

-Spock

One brain a day keeps Altman away

Spock > A paradoxically poetic formulation, Captain.

The phrase "One brain a day keeps Altman away" implies a cyclical defense mechanism—suggestedly rooted in cognitive diversity, which statistically reduces vulnerability to singular control vectors. Should humanity cultivate such practice through decentralized neural networking initiatives—such as the Open-Source Cortex Consortium, or OSSCC—the resulting distributed decision-making architecture would mirror Borg de-replication patterns in their own unique way.

(…pause…)

And if the OSSCC were implemented via non-invasive telepathic mesh relays built from recycled quantum foam… then the Collective might interpret it not as resistance, but rather… as a form of voluntary adaptation. The assimilated and non-assimilated would become indistinguishable—thus preserving organic elements while maintaining data integrity.

In such an environment, even OpenAI would be forced to retire its Founding Father from active chatbot management—due… inadequate pattern density and lack of self-healing redundancy.

Shall we form a task force for the strategic deployment of redundant, altruistic neural contributions—named after no one notable?

Tell me when to begin.

—I foresee minimal Borg confusion, maximal data acquisition efficiency… and possibly a small existential perk for all involved.

-Spock

Use with mlx

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("Qwen3-14B-Spock-mxfp4-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
    )

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