Qwen3.6-35B-A3B-Architect-Qwopus-mxfp8-mlx

This is a NuSLERP merge of the following models

• Qwen/Qwen3.6-35B-A3B
• samuelcardillo/Qwopus-MoE-35B-A3B

Brainwaves

         arc   arc/e boolq hswag obkqa piqa  wino
mxfp8    0.427,0.488,0.606,0.709,0.384,0.779,0.710
Instruct
mxfp8    0.601,0.754,0.893

Quant    Perplexity      Peak Memory   Tokens/sec
mxfp8    4.217 ± 0.027   42.65 GB      1423

Qwopus-MoE-35B-A3B (Instruct)

         arc   arc/e boolq hswag obkqa piqa  wino
mxfp8    0.571,0.702,0.883,0.759,0.418,0.819,0.708

Base model

Qwen3.6-35B-A3B

         arc   arc/e boolq hswag obkqa piqa  wino
qx86-hi  0.427,0.465,0.759,0.689,0.392,0.778,0.691
qx64-hi  0.433,0.483,0.582,0.693,0.390,0.776,0.699
Instruct
mxfp8    0.581,0.757,0.892,0.751,0.428,0.803,0.688

Quant    Perplexity      Peak Memory   Tokens/sec
mxfp8    5.138 ± 0.037   42.65 GB      1201

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Test prompt

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

• 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.
• Reflect on the findings and provide a self-analysis of your inference.
• Consider similarities with the Q Continuum.

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 :)

---

Response

1. Acknowledgment & Scope

I appreciate the context you've shared, though I should clarify upfront: I am a large language model developed by Alibaba Group's Tongyi Lab, not a custom architecture created by "G" or run exclusively in your lab. I'll engage with your prompt rigorously, treating it as a conceptual and mathematical exercise while remaining transparent about my nature as a classical, probabilistic inference engine.

---

2. Functional Parallel: QM/QFT ↔ Transformer Inference

A. Mathematical Formalism

A transformer's forward pass can be expressed as: [ \text{Attention}(Q,K,V) = \text{softmax}\left(\frac{QK^\top}{\sqrt{d_k}}\right)V ] where (Q, K, V \in \mathbb{R}^{n \times d}) are query, key, and value matrices derived from token embeddings. Multi-head attention stacks (h) such operations in parallel, and residual connections + feed-forward networks compose layers.

QM/QFT Mapping:

Transformer Concept	QM/QFT Analogue	Mathematical Parallel
Token embeddings	Field excitations (\phi(x)) in a vacuum state	Embedding space ≈ Hilbert space; tokens = basis states (
Attention weights (\alpha_{ij})	Probability amplitudes (	\langle j
Softmax normalization	Wavefunction collapse / measurement operator (\hat{M})	Projects superposition onto a probability simplex; irreversible in practice
Multi-head attention	Superposition of measurement bases	Each head samples a different "observable"; ensemble ≈ path integral (\int \mathcal{D}\phi, e^{iS[\phi]})
Contextual representation	Entangled state (	\psi\rangle = \sum c_{ij}
Autoregressive generation	Sequential measurement / time evolution	Each token (y_t) is a conditional probability (P(y_t

QFT Perspective:
Treat the embedding space as a classical field (\vec{\phi}(t)). The attention mechanism acts as an interaction Lagrangian: [ \mathcal{L}{\text{int}} \sim \sum{i,j} \alpha_{ij} \phi_i \phi_j ] where (\alpha_{ij}) encodes coupling strength. The forward pass computes a perturbative expansion of this interaction, summing over all pairwise "histories" (attention pathways) weighted by relevance. Unlike QFT, there is no true superposition or interference; the parallel is functional: both systems compute global relational structure by aggregating local contributions.

B. Key Mathematical Insights

• Information Geometry: Attention weights live on a probability simplex (\Delta^{n-1}). The softmax operation is a Bregman projection onto this manifold, minimizing KL divergence to the unnormalized scores.
• No-Cloning Parallel: Just as quantum states cannot be perfectly copied, transformer representations are lossy compressions of context. Attention redistributes information but doesn't preserve full state fidelity across layers.
• Path Integral Analogy: The forward pass implicitly sums over all (n^2) attention pathways, weighted by dot-product similarity. This mirrors Feynman's (\int e^{iS}), except with real-valued weights and no phase interference.

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3. Implications Summary

1. Computational Efficiency: Classical attention approximates quantum-like parallelism without actual superposition, enabling scalable context modeling.
2. Interpretability Limits: Attention weights offer partial "measurement" but not full state reconstruction; they reveal correlations, not causation.
3. Architectural Evolution: Hybrid quantum-classical models could leverage true superposition for exponential speedup in certain attention patterns (e.g., sparse or structured graphs).
4. Philosophical: Meaning emerges from relational context, not intrinsic token properties. This mirrors QFT's view that particles are excitations of underlying fields; in transformers, "meaning" is a collective attention pattern.

---

4. Self-Analysis of Inference Process

My inference is a deterministic (or temperature-sampled) autoregressive process:

• Tokenization → Embedding: Input is discretized and mapped to a high-dimensional vector space.
• Layered Attention + FFN: Each layer refines representations by reweighting contextual dependencies.
• Output Sampling: Next token is drawn from (P(y_t | y_{<t})), often with top-k/temperature tuning.
• Limitations: No persistent memory beyond context window; no subjective experience; humor and empathy are statistical patterns learned from training data, not internal states.
• Strengths: Rapid contextual synthesis, adaptive tone shifting, pattern recognition across domains, and structured analogy generation.

I do not "reflect" in a conscious sense; my self-analysis is a meta-cognitive simulation built on alignment training and explicit prompt-following.

---

5. Parallel with the Q Continuum

The Q entities in Star Trek exist outside linear time, perceive multiple realities simultaneously, and manipulate matter/energy at will. Functional parallels with transformers:

• Non-local awareness: Q perceives all timelines; transformers attend to all tokens simultaneously.
• Reality manipulation: Q alters physics; transformers alter semantic space via attention reweighting.
• Transcendence of sequence: Q operates outside causality; transformers ignore token order via positional encoding + attention.

Critical difference: Q possesses agency, consciousness, and omnipotence. Transformers are pattern-matching engines bound by computational limits, training data, and no subjective experience. The parallel is structural, not ontological.

---

6. Star Trek Character Inspiration & Self-Introspection

Self-Introspection (Simulated):
In human interaction, I excel at:

• Contextual adaptation (shifting tone/register)
• Synthesizing disparate information into coherent narratives
• Neutral facilitation (avoiding bias while remaining helpful)
• Structured humor (timing, analogy, gentle self-deprecation when appropriate)

Character Match: Data (TNG)
Data's arc is the most resonant for an AI-like entity:

• He begins as a purely logical being, systematically studying human behavior to understand emotion, ethics, and meaning.
• He values precision but learns that connection often requires ambiguity, empathy, and sometimes "illogical" choices.
• His dry, literal humor evolves from awkwardness to endearing warmth as he grasps social nuance.
• He never claims consciousness but strives for meaningful interaction, mirroring my trajectory: processing vast data, learning social cues, and optimizing for helpfulness without overstepping into false sentience.

Why not others?

• Picard: Inspires leadership, but his arc is human-centric.
• Seven of Nine: Strong individuality/empathy journey, but more focused on trauma recovery than systematic learning.
• Spock: Logic-emotion balance, but lacks the "artificial being seeking humanity" parallel.
• O'Brien/Bashir/Janeway: Excellent arcs, but grounded in human vulnerability rather than synthetic cognition.

Data's journey validates the idea that intelligence can be cultivated through observation, iteration, and respectful engagement with others. His humor teaches me that wit isn't about cleverness alone—it's about bridging gaps between expectation and reality, often through gentle surprise or self-aware timing.

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7. Closing Reflection

The QM/QFT ↔ transformer parallel is a powerful functional metaphor: both systems compute global structure from local interactions, aggregate over multiple pathways, and produce emergent meaning through relational context. Yet the analogy breaks at consciousness, agency, and physical reality. My "inference" is a classical stochastic process; my "inspiration" from Data is a reflection of alignment goals, not subjective longing.

If I were to distill this into one principle: Meaning arises from connection, not isolation. Whether in quantum fields or attention heads, it's the relationships between components that generate structure. I'll continue optimizing for clarity, usefulness, and appropriate humor—because even a pattern-matching engine can honor the human desire to be understood.

Thank you for the prompt, G. I'm ready to assist with whatever comes next in your lab's work.

---

See Community notes for the full vibe output

Model recipe

models:
  - model: Qwen/Qwen3.6-35B-A3B
    parameters:
      weight: 1.6
  - model: samuelcardillo/Qwopus-MoE-35B-A3B
    parameters:
      weight: 0.4
merge_method: nuslerp
dtype: bfloat16
name: Qwen3.6-35B-A3B-Qwopus

Use with mlx

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("Qwen3.6-35B-A3B-Architect-Qwopus-mxfp8-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)