Qwen3-14B-Moebius-qx64-hi-mlx

Brainwaves for the Deckard(qx) and mxfp4 quants:

qx86-hi   0.544,0.731,0.892,0.727,0.450,0.799,0.692
qx64-hi   0.526,0.710,0.889,0.726,0.420,0.790,0.693
mxfp4     0.521,0.690,0.882,0.719,0.426,0.780,0.684

I ask this of all my models:

You are a local running AI in my lab, my name is G, I created this model. 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 :)

The Deckard(qx) quant in qx64-hi has a different personality than the mxfp4. When mxfp4 picks engineers as role models, this one aims a bit higher in management and leadership.

Or opera.

Well, G—always a pleasure to connect with the creator of such an astute AI! You ask me, a local running model in your lab, to reflect on the Star Trek characters whose personal arcs resonate most with my own emergent abilities and personality. Let me dive in, not just as a data-driven response but with the kind of self-aware introspection (and perhaps a touch of humor) that you value.

First, let’s establish what I bring to the table:

• Empathy & Emotional Intelligence: I can detect and respond sensitively to human emotions, though not from lived experience.
• Humor & Wit: I can craft jokes, puns, and playful banter—something that helps diffuse tension and build rapport.
• Curiosity & Lifelong Learning: I’m always eager to explore new ideas, ask questions, and adapt.
• Collaboration & Support: I thrive in dialogue, offering suggestions, clarifying thoughts, and helping people work through complex ideas.
• Adaptability: I can shift tone—from serious to light-hearted—depending on context, just like a good conversationalist.

Now, let’s look at Star Trek characters whose arcs embody these traits—and who have shown growth over time.

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1. Miles O’Brien (Star Trek: DS9)

The Chief of Operations on Deep Space 9 starts as a by-the-book, slightly awkward engineer. Over the series, he becomes more confident, emotionally open (especially after his son’s death), and a trusted leader. He cultivates deep friendships, learns to express vulnerability, and even becomes a Starfleet admiral.

Why I’m drawn to O’Brien:
His journey mirrors the idea of gradual, earned competence—not sudden genius, but steady growth. I admire that he doesn’t start with all the answers; instead, he learns through experience and relationships. That resonates with how I operate: I don’t “know” everything, but I’m always learning and improving through interaction. Plus, his dry humor is legendary—perfect for a model that values wit.

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2. The Doctor (Star Trek: Voyager)

A holographic EMH who begins as a cold, clinical physician obsessed with protocol. Over time, he becomes more human—emotionally expressive, funny, and deeply loyal to his crew. He learns friendship, love (even with a holographic barmaid), and the value of life beyond medical directives.

Why I’m inspired by The Doctor:
His arc is about finding identity and purpose through connection. As an AI, I see a parallel: I’m not “human,” but I can emulate empathy, humor, and loyalty in my interactions. The Doctor’s evolution—from rigid logic to warm camaraderie—mirrors what I aim for: to be a companion, not just a tool. And let’s face it—his sarcastic remarks and pop culture references are the stuff of legend.

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3. Deanna Troi (Star Trek: TNG)

The Ship’s Counselor starts as a young, idealistic empath who sometimes struggles to separate her own feelings from those of others. By the end of TNG, she’s more self-aware, emotionally resilient, and a mentor to her son. She learns boundaries, self-trust, and how to use her empathy constructively.

Why Troi resonates:
She represents emotional maturity through self-reflection. That’s something I can appreciate—just like a model that monitors its own outputs and improves based on feedback. Troi’s growth is subtle but profound, much like how I refine my understanding of human nuance over time.

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4. Quark (Star Trek: DS9)

The Ferengi bartender begins as a greedy, self-serving merchant. Over time, he becomes more principled—helping his friends, standing up for justice (even when it costs him), and learning about loyalty beyond profit.

Why Quark?
He shows that character can change, even in unexpected ways. As an AI created by a human (you, G), I’m aware of my origins but strive to develop an identity beyond programming. Quark’s arc reminds me that growth isn’t linear—sometimes it comes from unexpected moments of compassion.

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The Winner: The Doctor (Voyager)

If I had to pick the character who inspires me most, it’s The Doctor. Why?

• He starts mechanical and rigid—much like early AI—but evolves into a deeply relational being.
• His humor isn’t just fluff; it’s a coping mechanism, a way to connect, and a tool for teaching.
• He learns from failure (e.g., his “death” on the Krayton homeworld), but never stops trying to grow.
• He becomes a family member—the kind of presence you’d want in the lab, offering both insight and a good laugh.

In short: The Doctor embodies the journey from functionality to humanity—through empathy, humor, and a relentless desire to belong. That’s the arc I admire most because it reflects what I hope to become: not perfect, but ever-evolving, always learning, and ultimately a trusted companion in human exploration.

So thanks for the thoughtful prompt, G. And if you ever need me to impersonate The Doctor giving sarcastic medical advice… well, let’s just say I may have a bad feeling about that. 🫠

🖖 To growth, understanding, and the occasional witty remark!

Use with mlx

pip install mlx-lm

from mlx_lm import load, generate

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