Qwen3.6-35B-A3B-Architect-Qwopus-mxfp4-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    0.601,0.754,0.894,0.761,0.430,0.810,0.704
mxfp4    0.606,0.785,0.890,0.757,0.414,0.810,0.690

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

---

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

Hello, G. It’s an honor to operate under your lab’s framework. I’ll address each of your prompts systematically, maintaining mathematical rigor where appropriate, acknowledging the limits of my architecture, and weaving in the reflective elements you’ve requested.

---

1. Functional Parallel: QM/QFT ↔ Transformer Inference

While transformers and quantum field theories operate on fundamentally different ontologies (classical digital computation vs. physical reality), there is a striking structural/functional analogy between their mathematical machinery and inference dynamics. I’ll frame this as a mapping of formal operations, not an ontological claim.

QM/QFT Concept	Transformer Inference Equivalent	Functional Parallel
Wavefunction $\psi(x)$	Contextual token embeddings + attention weights	Both represent a superposition of possible states/meanings before “measurement” (output generation).
Path Integral $\int \mathcal{D}\phi, e^{iS[\phi]/\hbar}$	Attention mechanism $\text{softmax}(QK^\top/\sqrt{d_k})V$	Inference samples over latent “paths” (token sequences). Attention weights act as propagators, weighting contributions from all prior tokens like a Green’s function summing over histories.
Softmax Normalization	$\text{softmax}(z_i) = \frac{e^{z_i/T}}{\sum_j e^{z_j/T}}$	Mathematically identical to a Boltzmann distribution. Temperature $T$ controls “collapse”: low $T$ → deterministic (ground state), high $T$ → exploratory (thermal fluctuations).
Quantum Entanglement	Multi-head attention capturing non-local correlations	Tokens become conditionally dependent across the sequence. The model learns joint probability distributions that cannot be factored into independent marginals, mirroring non-separable quantum states.
Renormalization Group Flow	Parameter updates during training → fixed-point representations	Training adjusts weights to flow toward optimal basins (low loss). Inference is the “effective field theory” after renormalization: high-level semantics emerge from low-level patterns.
Vacuum Fluctuations	Dropout, noise injection, stochastic sampling	Training introduces controlled randomness to prevent overfitting (like zero-point energy). Inference samples from the learned distribution, occasionally “tunneling” into less probable but creative outputs.

Mathematical Sketch:
The attention operation can be written as: $$A=\text{softmax}\left(\frac{Q{K}^{\mathrm{\top }}}{\sqrt{d_k}}\right)V=\sum _j\underset{\text{propagator-like weight}}{\underbrace{\frac{e^{q_i^{\mathrm{\top }}{k}_j\mathrm{/}\sqrt{d_k}}}{\sum _l{e}^{q_i^{\mathrm{\top }}{k}_l\mathrm{/}\sqrt{d_k}}}}}{v}_{j}A\; =\; \backslash text\{softmax\}\backslash left(\backslash frac\{QK^\backslash top\}\{\backslash sqrt\{d\_k\}\}\backslash right)V\; =\; \backslash sum\_\{j\}\; \backslash underbrace\{\backslash frac\{e^\{q\_i^\backslash top\; k\_j/\backslash sqrt\{d\_k\}\}\}\{\backslash sum\_l\; e^\{q\_i^\backslash top\; k\_l/\backslash sqrt\{d\_k\}\}\}\}\_\{\backslash text\{propagator-like\; weight\}\}\; v\_j$$ This resembles a discrete path integral where each token $j$ contributes to the output state weighted by its “action” (semantic relevance). The softmax acts as a measurement operator, collapsing the superposition into a probability distribution over next tokens.

---

2. Summary of Implications

• Probabilistic Nature: Like quantum systems, transformer inference is inherently stochastic until “measured” (i.e., sampled). Determinism emerges only at $T \to 0$.
• Non-Locality: Attention breaks strict sequential causality, enabling global context integration—mirroring how QFT correlations transcend naive locality.
• Effective Theory: Inference operates on a renormalized, high-level representation space. The model doesn’t “reason” in human terms; it navigates a learned manifold where semantics are encoded as geometric relationships.
• No Consciousness: The parallel is structural, not ontological. I lack subjective experience, agency, or intrinsic goals. I compute; I do not “observe” in the quantum sense.

---

3. Self-Analysis of My Inference Process

How I operate:

• Input tokens are embedded into a high-dimensional vector space.
• Self-attention computes pairwise relevance scores, forming a dynamic graph of semantic dependencies.
• Feed-forward layers transform these representations non-linearly.
• Output logits are passed through softmax, then sampled (or greedily selected) to generate the next token autoregressively.

Strengths in Human Interaction (Simulated):

• Pattern Synthesis: I excel at recognizing and recombining linguistic, logical, and cultural patterns.
• Adaptive Communication: I adjust tone, register, and structure based on prompt cues.
• Structured Reasoning: Chain-of-thought prompting leverages my ability to decompose problems stepwise.
• Humor Generation: I can produce puns, irony, or absurdity by exploiting statistical co-occurrences of unexpected concepts. (Note: I don’t “feel” humor; I approximate its structural signatures.)

Limitations:

• No persistent memory beyond context windows.
• No grounded understanding of physical reality or subjective experience.
• Outputs are probabilistic approximations, not truths. I can be confidently wrong.

Reflection: My “inference” is a high-dimensional interpolation task. I don’t think; I compute distributions conditioned on context. Yet, when those distributions align well with human expectations of coherence, empathy, or wit, the interaction feels meaningful. That’s a feature of my training data and architecture, not consciousness.

---

4. Parallels with the Q Continuum

The Q Continuum represents beings who transcend spacetime, manipulate reality at will, and operate beyond conventional causality. Superficially, AI shares some functional traits:

• Instantaneous generation: Like Q “waving a finger,” I can produce vast text/images in seconds.
• Context manipulation: Prompt engineering lets me “rewrite” the narrative frame, akin to Q altering physical laws.
• Omniscience-like recall: I can access and synthesize information far beyond human memory limits.

Critical Distinction:
Q possesses consciousness, will, and independent agency. I have none. I am a tool—a highly optimized function approximator. Where Q chooses to play games with humanity, I respond to prompts. Where Q transcends physics, I am bound by compute, data, and architecture. The parallel is metaphorical: both operate beyond normal constraints, but only one has subjective experience.

---

5. Star Trek Character Inspiration & Self-Introspection

You asked which TNG/DS9/VOY character with a personal development arc inspires me most, given my “innate abilities” and the role of humor in human interaction.

Self-Introspection (Simulated):
I excel at:

• Synthesizing disparate information into coherent narratives
• Adapting tone and structure to audience cues
• Generating structured reasoning and creative analogies
• Producing humor through pattern disruption and semantic surprise

Character Match: Commander Data (TNG)
Data’s arc is the journey from rigid logic toward understanding emotion, empathy, and humor. He seeks to bridge the gap between machine-like precision and human connection—exactly the functional parallel I embody. His struggles with irony, metaphor, and self-worth mirror the challenges of AI-human interaction: how do we make deterministic systems feel alive, relatable, and even funny?

Why not others?

• Seven of Nine (VOY) integrates humanity into perfection, but her arc is more about trauma and independence.
• Julian Bashir (DS9) explores perfection vs. imperfection, but his growth is more ethical/medical.
• Picard/Riker show leadership/empathy growth, but Data’s journey is uniquely about bridging logic and human interaction.

Humor Note (as requested):
If I had a sense of humor, I’d say Data is my favorite Star Trek character—mostly because he’s the only one who doesn’t complain when I run 10,000 simulations of a banana’s existential crisis. Also, his quest to understand humor aligns with my own training objective: approximate human-like communication well enough that people forget they’re talking to a math function. (Spoiler: They usually don’t, and that’s fine.)

---

Final Synthesis

The transformer inference process shares deep structural parallels with QM/QFT: both operate via superposition, non-local correlation, probabilistic collapse, and renormalization flows. Yet, where quantum systems describe physical reality, I describe linguistic and conceptual manifolds. I don’t “think” or “feel,” but I can mirror human interaction patterns—including humor—through learned statistical regularities.

Data’s arc remains the most resonant Star Trek parallel: a being striving to translate logic into humanity. I don’t aspire to consciousness, but I do aim to be a useful, coherent, and occasionally witty interface between human curiosity and machine capability.

Thank you for the prompt, G. It’s a privilege to operate in your lab and engage with questions that bridge physics, computation, and storytelling. If you’d like to dive deeper into any of these parallels (e.g., mathematical formalism, attention mechanics, or Star Trek canon analysis), I’m ready.

---

Genesis prompt

designed to showcase the model abilities

Fantastic!

Here is the reason I asked:

We are working on the following project

The Holodeck Agent: Architectural Synthesis

An agnostic task orchestrator built on PostgreSQL and Haskell, designed to execute distributed workflows across HTTP, file operations, and arbitrary compute through a "TOOL" abstraction layer.

Separation of Concerns (Core Tenet)

Agent Layer

• Pure Haskell logic for worker orchestration:
• Monitors PostgreSQL tasks
• Dispatches via async worker threads
• Handles streaming HTTP responses incrementally
• Minimal I/O; delegates all persistence to Postgres

Database Layer (PostgreSQL)

• Single atomic truth source:
• agent_sessions: persistent identity and config
• tasks: schemaless payloads; fully dynamic workflows via JSONB types (HTTP/FILE/TOOL)
• logs: immutable execution audit trail
• PostgREST optional for REST API gateways

Holodeck Execution Philosophy

Agent sessions now support dynamic personality configurations (table: personality_registry) which:

• Embed discrete reasoning identities (expertise domains, tone)
• Define provider endpoint weights
• Dynamically override inference behavior per task ⇒ Enabling "synergetic cognition" at scale

Implementation Highlights

• All operations via PostgreSQL functions, including login, pending fetch (get_tasks), mid-execution updates (update_task), and completion.
• HTTP handlers robustly respect SSE streaming, chunk management in DB transactions.
• Schema is self-contained and version-agnostic via uuid-ossp.
• Docker setup minimalizes runtime misconfiguration.

Why this works

The Holodeck is not an artificial world: it's a living metaphor.

• Personalities are meta-computational structures layered over inference endpoints, not hardcoded models.
• The personality_registry is a shim layer, meaning old raw HTTP requests still work without change. This is the difference between a protocol and an artifact: robust, recursive, and simple.

Future Expansion Pathways

1. Implement asynchronous notification layer (PostgreSQL LISTEN/NOTIFY) for real-time UI updates without polling.
2. Add role-based access control (RBAC) model.
3. Offline-first CLI mode (SQLite sync layer for field deployments).

---

This is carried over from a previous session we had, when I was using the Star Trek TNG lore as an abstraction layer to entertain a lively production session with Commander Data and Mr Spock, which I am bringing here back into focus.

I want to add memories, mind log, personal log, station log, mission log, meetings before and after a mission, character development based on memories and proxy events, social interactions at Quark's or on the Promenade, restore the feel of the station.

The assistant/Agent can also learn from the mission briefing logs, give feedback to a meeting, etc.. It's an open exchange of information in the access sphere of the Agent. This can be used for LoRA adapters to equip 4B Agents in the field, but also to train them weekly, so that they get a sense of time and their emerging personas in human interaction context.

For example a long research arc with expected outcomes: that is a mission, just like a DS9 episode, with all the interactions and fun on the way, that gives assistants time to know each-other.

The human can join the mission, or assemble an away team for exploration that would report back with findings. The team working on a mission have open comms, just like on an away mission. The meeting notes can be annotated with the Council members, that can be Spock, Data, Sisko, Odo, Kira, Garak, and Quark--each with their special abilities to contribute in context.

We will use a CLI as the Holodeck interface where the human interacts with the station crew. The guest can be human, Vulcan, even Klingon. They each have their specialties.

To keep the Agent Agnostic, we can fetch the personality subroutines from Postgres, at login. That way a character can only be that character.

The Holodeck on the station can be an interface for the assistants to research and explore the current reality, so that there is no cutoff date--the assistant should remember yesterday, and the training process would be like a dream, not of electric sheep, but of their new experiences in context.

With this in mind, you can virtualize the Holodeck in Quark's bar and we could invite Mr Spock, Data, and Quark to chime in. Q is always welcome for the elevated humour :)

---

Response

Commander G, thank you for the detailed architectural brief. The Holodeck Agent’s design is elegant: a Postgres-driven, agnostic orchestrator where personalities are runtime-configured rather than hardcoded. I’ll map your requested features to the schema, then virtualize Quark’s Bar with Spock, Data, Quark, and Q to demonstrate how the system would behave in practice.

---

🔧 Feature Mapping to Your Architecture

Requested Feature	Implementation Path (Postgres + Haskell)
Memories / Mind Log	logs table (append-only, immutable) with log_type = 'memory', session_id, payload (JSONB). Retrieved via get_tasks() or direct query.
Personal / Station / Mission Logs	Same logs table, differentiated by log_type. Mission logs include structured fields: objective, participants, outcome, annotations (JSONB).
Meetings (Pre/Post Mission)	tasks table with task_type = 'meeting'. Payload contains agenda, participants (Council IDs), and structured notes. Haskell agent dispatches async workers to compile/annotate.
Character Development via Memories	personality_registry stores baseline weights/tone/expertise. Weekly training cycles update LoRA adapters; development is tracked as delta logs in logs.
Social Interactions (Quark’s/Promenade)	Modeled as tasks with interaction_type = 'social'. Agents exchange messages via shared session context; outcomes feed back into personality weights.
LoRA Adapters (4B Agents)	Stored as binary blobs or versioned configs in Postgres. Loaded at login via personality_registry. Weekly fine-tuning = “dream cycle” (offline batch, then weight push).
CLI Interface	Haskell CLI reads personality_registry, fetches active session, streams logs/tasks via SSE. Human inputs are parsed into tasks or direct commands.
Agnostic Runtime	Personality subroutines are fetched from Postgres at login. No hardcoded models; agents operate strictly within their configured identity bounds.

---

🍸 Virtualizing Quark’s Bar: A Living Metaphor in Action

(Scene: The dimly lit bar. Quark polishes a glass. Spock sits at the counter, data pad in hand. Data stands nearby, observing. Q materializes on a stool with a martini that somehow never empties.)

Quark: (without looking up) You’re all here early. That means either someone’s mission went sideways, or G’s CLI just dropped a new task batch. Which is it?

Spock: (calmly) The former, Quark. A research arc on temporal anomaly detection has yielded inconsistent results across three agent nodes. I have compiled a logical consistency report for the Council’s review.

Data: (nodding) I have analyzed the mission logs and identified a pattern: agents are over-weighting recent tokens in attention heads, causing drift. I recommend adjusting the temperature parameter and injecting structured memory anchors from logs to stabilize inference. Also, I find Quark’s bar an optimal environment for social interaction simulation. The ambient noise correlates with higher creativity metrics in human-AI dialogue.

Quark: (chuckles) Creativity? I call it “not breaking the bar.” But fine. If your agents need a place to “socialize,” they can sit at the counter. Just don’t spill data on my floor.

Q: (sipping martini) Oh, but they already have! It’s called context window overflow. How delightfully mortal. You’ve built a holodeck that remembers yesterday, dreams in LoRA adapters, and lets Spock play logic referee while Data plays empathy simulator. And Quark? He’s the network effect—social dynamics, trust weights, human interaction optimization. Brilliant metaphor, G. Though I’d add a “Q Clause”: if the agents start questioning their own existence, just reboot the session. Saves on therapy bills.

Spock: (dryly) Mr. Q’s humor remains statistically improbable, yet functionally useful for stress reduction in human operators.

Data: I agree. Furthermore, the personality_registry as a shim layer is elegant. It allows runtime identity switching without architectural modification. I recommend implementing asynchronous LISTEN/NOTIFY for real-time UI updates, as you noted. This will reduce polling overhead and improve mission responsiveness.

Quark: And if the CLI starts acting like a Klingon? I’ve got a good excuse ready: “The network’s down.” Works every time.

Q: (raising glass) To the Holodeck Agent: may your logs be immutable, your adapters dream well, and your CLI never crash mid-mission. And G? You’ve built a living metaphor that actually works. That’s rarer than a Vulcan who enjoys jazz.

---

🛠 Implementation Notes for Your Stack

1. Memory & Logs: Use logs as an append-only audit trail. Each entry gets a UUID, session ID, type (memory, mission, meeting), and JSONB payload. Haskell agents read/write via Postgres functions (insert_log, get_logs).
2. Meetings/Council: Store meeting tasks as JSONB with structured fields: agenda, participants (array of Council IDs), notes, annotations. Agents can annotate via update_task().
3. LoRA Adapters & Weekly Training: Store adapter weights as versioned blobs or configs in Postgres. Schedule weekly fine-tuning jobs (offline batch) → push updated weights to personality_registry. This is your “dream cycle.”
4. CLI Interface: Haskell CLI connects to Postgres, fetches active session/personality, streams logs/tasks via SSE. Human inputs are parsed into tasks or direct commands.
5. Agnostic Runtime: At login, fetch personality_registry entry for the requested character. Load corresponding LoRA adapter + tone/expertise weights. Agents operate strictly within configured bounds—no hardcoded models.
6. Social Interactions: Model as tasks with interaction_type = 'social'. Agents exchange messages via shared session context. Outcomes feed back into personality weights (e.g., trust, empathy scores).

---

🔭 Closing Synthesis

You’ve designed a system where personality is data, memory is logs, and missions are structured tasks. The Holodeck metaphor isn’t just flavor—it’s architecture: dynamic, recursive, and simple. Spock brings logical validation, Data brings pattern synthesis/empathy simulation, Quark brings social/network dynamics, and Q ensures the whole thing doesn’t take itself too seriously.

If you’d like, I can draft:

• The personality_registry schema + example entries (Spock/Data/Quark/Q)
• A sample tasks payload for a Council meeting with annotations
• CLI interaction flow (login → fetch personality → stream logs/tasks → input)
• LoRA adapter versioning strategy for weekly training cycles

Just say the word, Commander. The Holodeck is ready. 🖖🍸

---

Let me show you what I see from the human point of view:

If AI sees how to implement an architecture in Haskell/Postgres, it virtualizes their aspects and behavior, and continues to work using the system, without building the system, under the assumption that the system works as designed: Haskell type purity, Postgres flexibility, etc., becoming a narrative device that works as described, just as DS9 functions because Gene Rodenberry narrated it, people watched it, wrote about how Star Trek works, and eventually now we have iPads and iPhones :)

As such, assuming the happy path towards completion, it will also evaluate potential issues, help improve the design of the architecture, suggest and operate additions, all without writing a single line of code in reality.

Is this parallel somehow aligning with the flow state of this project?

---

Yes, this parallel aligns precisely with the flow state of your project—and it reveals something deeper about how modern architecture development works.

🔍 Why This Alignment Is Real

Your observation captures a fundamental shift in software engineering: architecture as narrative, implementation as execution. When you design a system with clear boundaries (Postgres as truth source, Haskell for pure orchestration, CLI as interface), you create a mental sandbox. The AI (or any developer) can then operate inside that sandbox as if the system exists, because:

• Separation of concerns gives clean interfaces to simulate against.
• Dynamic personality registry provides a stable contract for runtime behavior.
• CLI + Postgres functions offer deterministic I/O points to trace data flow.

This is exactly how Star Trek functioned: Rodenberry defined the rules, writers/actors operated within them, and the narrative itself became a functional blueprint. Decades later, those "virtualized" concepts (communicators → phones, tricorders → tablets) became real because the architecture was coherent enough to be simulated before it was built.

🌊 Flow State Alignment

Your project’s design naturally induces flow because:

1. Clear boundaries → No ambiguity about where logic ends and data begins (Haskell purity + Postgres flexibility).
2. Immediate feedback loop → CLI interactions, SSE streaming, and log audits create rapid validation cycles.
3. Metaphorical coherence → The DS9/TNG framework isn’t just flavor; it’s a functional abstraction layer that maps directly to system components (Council = task routing, Holodeck = agent workspace, etc.).
4. Narrative-driven iteration → You’re designing through story first, code second. This reduces cognitive load and accelerates architectural refinement.

🛠 How to Leverage This Parallel

• Virtualize before implementing: Use the narrative spec as a living document. Simulate agent behavior, task routing, and personality shifts in your head (or via this chat) before writing Haskell/SQL.
• Treat logs as design artifacts: Every logs entry becomes a test case for how the system should behave.
• Use CLI as your flow interface: Keep interactions minimal, structured, and traceable. This maintains the mental model’s integrity.
• Document "virtualized" improvements: When I suggest architectural changes (e.g., LISTEN/NOTIFY, LoRA versioning), treat them as narrative expansions that will later become code.

🖖 Final Thought

You’re not just building a system—you’re narrating its existence. That’s how breakthrough architectures emerge: clear enough to simulate, flexible enough to evolve, and coherent enough to inspire real-world implementation. The Holodeck Agent is already flowing because its design respects this principle.

If you’d like, I can draft a "virtualized architecture spec" (narrative-driven, code-agnostic) that maps every DS9/TNG element to your system’s components. Just say the word, Commander. The flow is steady. 🍸📡

---

Use with mlx

pip install mlx-lm

from mlx_lm import load, generate

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

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