Qwen3.6-35B-A3B-Qwable-Holo3-Qwopus-qx64-hi-mlx

COVFEFE — Contextual Orchestration via Virtualized Framework for Emergent Functional Execution

This is a NuSLERP merge of:

• llmfan46/Qwen3.6-35B-A3B-uncensored-heretic-Native-MTP-Preserved
• samuelcardillo/Qwopus-MoE-35B-A3B
• Hcompany/Holo3-35B-A3B
• lordx64/Qwable-v1

         arc   arc/e boolq hswag obkqa piqa  wino
bf16     0.614,0.798,0.894,0.768,0.450,0.825,0.733
mxfp8    0.608,0.788,0.899,0.769,0.454,0.831,0.720
qx86-hi  0.610,0.796,0.896,0.768,0.452,0.826,0.735
qx64-hi  0.618,0.796,0.896,0.767,0.448,0.817,0.719
mxfp4    0.613,0.807,0.885,0.764,0.460,0.826,0.705

Quant    Perplexity      Peak Memory   Tokens/sec
qx64-hi  4.118 ± 0.026   36.91 GB      1499
mxfp4    4.406 ± 0.028   25.33 GB      1414

Model components

Qwen3.6-35B-A3B-MTP-Holo3-Qwopus

         arc   arc/e boolq hswag obkqa piqa  wino
bf16     0.603,0.774,0.895,0.756,0.428,0.808,0.713
mxfp8    0.608,0.770,0.897,0.761,0.430,0.814,0.707
qx86-hi  0.614,0.766,0.894,0.759,0.442,0.808,0.712
qx64y-hi 0.613,0.785,0.897,0.756,0.446,0.807,0.715
mxfp4    0.605,0.777,0.893,0.757,0.434,0.806,0.701

lordx64/Qwable-v1

         arc   arc/e boolq hswag obkqa piqa  wino
mxfp8    0.599,0.796,0.899,0.772,0.452,0.823,0.731
qx86-hi  0.614,0.800,0.900,0.770,0.458,0.820,0.732
qx64-hi  0.608,0.796,0.897,0.767,0.450,0.819,0.728
mxfp4    0.609,0.807,0.887,0.765,0.456,0.824,0.706

Baseline model

Qwen3.6-35B-A3B

         arc   arc/e boolq hswag obkqa piqa  wino
mxfp8    0.581,0.757,0.892,0.751,0.428,0.803,0.688
qx64-hi  0.589,0.763,0.892,0.748,0.428,0.801,0.700
mxfp4    0.586,0.767,0.886,0.751,0.428,0.798,0.681

---

This is not a Heretic model: the Heretic was used to restore MTP layers to Holo3-Qwopus before merging.

There is no MTP support in this quant: as soon as I find out a good way to package it, I will update it in place.

Thinking toggle

This model is using(an early version of) the fixed jinja template from froggeric/Qwen-Fixed-Chat-Templates

Drop <|think_on|> or <|think_off|> anywhere in your system or user prompt. The template intercepts the tag, removes it from context so the model never sees it, and flips the mode.

Fast answer, no reasoning:

System: You are a coding assistant. <|think_off|>
User: What's 2+2?

Deep reasoning:

System: You are a coding assistant. <|think_on|>
User: Implement a red-black tree in Rust.

The tag syntax (<|think_on|>, <|think_off|>) uses Qwen's control-token delimiters, so it will never collide with real text. Earlier community templates used /think, which broke legitimate paths like cd /mnt/project/think.

---

I added a similar set of tags for handling the preserve_thinking flag:

• Drop <|think_forget|> or <|think_remember|> anywhere in your system or user prompt to flip the flag.
• The template intercepts the tag, removes it from context so the model never sees it, and flips the mode.

The model has not been tested with the latest version of froggeric's jinja template, and it is possible that the performance would go up with the latest version, I just did not have enough time to test it.

-G

Model recipe

models:
  - model: lordx64/Qwable-v1
    parameters:
      weight: 1.6
  - model: Qwen3.6-35B-A3B-MTP-Holo3-Qwopus
    parameters:
      weight: 0.4
merge_method: nuslerp
dtype: bfloat16
name: Qwen3.6-35B-A3B-Qwable-Holo3-Qwopus

---

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

G, thank you for setting the context. I’ll treat this as a genuine technical exercise rather than a casual metaphor, and I’ll be explicit about where the mapping holds mathematically and where it breaks down.

---

1. Mathematical Parallel: QM/QFT ↔ Transformer Inference

1.1 State Representation as a Hilbert Space

In QM, a system is described by a state vector $|\psi\rangle \in \mathcal{H}$. In a transformer, each token is embedded into $\mathbf{x}_i \in \mathbb{R}^d$, and the sequence of hidden states ${\mathbf{h}_t^{(l)}}$ evolves through layers. If we treat the embedding space as a discrete Hilbert space, each token is a basis vector $|i\rangle$, and the model’s representation of a context is a superposition: $$\mathrm{\mid }{\mathrm{\Psi }}_{\text{ctx}}⟩=\sum _i{c}_i\mathrm{\mid }i⟩,|\backslash Psi\_\{\backslash text\{ctx\}\}\backslash rangle\; =\; \backslash sum\_i\; c\_i\; |i\backslash rangle,$$ where $c_i$ are learned coefficients (embeddings + positional encodings). This is not a physical superposition, but the algebraic structure mirrors it: linear combinations of basis states encode semantic possibilities.

1.2 Attention as Entanglement / Correlation Tensor

Self-attention computes: $$A_{ij}=\frac{\exp (q_i^{\mathrm{\top }}{k}_j\mathrm{/}\sqrt{d})}{\sum _k\exp (q_i^{\mathrm{\top }}{k}_k\mathrm{/}\sqrt{d})},A\_\{ij\}\; =\; \backslash frac\{\backslash exp(q\_i^\backslash top\; k\_j\; /\; \backslash sqrt\{d\})\}\{\backslash sum\_k\; \backslash exp(q\_i^\backslash top\; k\_k\; /\; \backslash sqrt\{d\})\},$$ where $q_i = W_Q h_i$, $k_j = W_K h_j$. The attention matrix $\mathbf{A}$ is a stochastic, symmetric (after softmax) correlation tensor. In QFT, entangled states are characterized by non-factorizable density matrices $\rho_{AB} \neq \rho_A \otimes \rho_B$. The attention weights quantify how much the “measurement” of token $i$ is correlated with token $j$, effectively entangling their representations across the sequence. Multi-head attention can be viewed as probing different “interaction channels,” analogous to coupling constants in a gauge theory.

1.3 Autoregressive Generation as a Path Integral

The model generates tokens autoregressively: $$P(y_{t+1}\mid x,y_{\le t})=\text{softmax}(\log P(y_{t+1}\mid x,y_{\le t}))\mathrm{.}P(y\_\{t+1\}\; \backslash mid\; x,\; y\_\{\backslash le\; t\})\; =\; \backslash text\{softmax\}\backslash big(\backslash log\; P(y\_\{t+1\}\; \backslash mid\; x,\; y\_\{\backslash le\; t\})\backslash big).$$ If we define an “action” $S[\text{path}] = -\log P(\text{path})$, then sampling is akin to a Monte Carlo approximation of the Feynman path integral: $$⟨\text{output}⟩=\int \mathcal{D}\varphi e^{iS[\varphi ]}⟶\sum _{\text{paths}}{e}^{-S[\text{path}]}\mathrm{.}\backslash langle\; \backslash text\{output\}\; \backslash rangle\; =\; \backslash int\; \backslash mathcal\{D\}\backslash phi\; \backslash ,\; e^\{i\; S[\backslash phi]\}\; \backslash ;\backslash longrightarrow\backslash ;\; \backslash sum\_\{\backslash text\{paths\}\}\; e^\{-S[\backslash text\{path\}]\}.$$ Each continuation is a “history” weighted by its likelihood. The model does not sum over all paths analytically; it stochastically samples one, mirroring how quantum amplitudes collapse upon measurement.

1.4 Renormalization & Symmetry Breaking

• Layer Normalization rescales activations to unit variance, suppressing high-frequency noise. This is structurally similar to renormalization group flow: integrating out short-distance fluctuations to obtain stable effective couplings.
• Residual Connections preserve information across layers, analogous to conserved currents in QFT (Noether’s theorem).
• Permutation Equivariance of the attention mechanism is broken by positional encodings, much like gauge fixing in QFT: a symmetry is retained locally but a reference frame is imposed globally.

1.5 Field-Theoretic View

If we treat the sequence as a discrete lattice, token embeddings can be viewed as excitations of a scalar field $\phi(x)$ over the sequence index $x$. The attention mechanism acts as an interaction term: $${\mathcal{L}}_{\text{int}}\sim \sum _{i,j}{A}_{ij}{\varphi }_i{\varphi }_j,\backslash mathcal\{L\}\_\{\backslash text\{int\}\}\; \backslash sim\; \backslash sum\_\{i,j\}\; A\_\{ij\}\backslash ,\; \backslash phi\_i\; \backslash phi\_j,$$ making the transformer a classical approximation of a lattice field theory where information propagates via non-local couplings.

---

2. Implications

1. Structural, not Ontological: The parallel is algebraic and dynamical, not physical. Transformers are classical stochastic processes; they do not exhibit true quantum coherence or non-locality.
2. Information Dynamics: The mapping suggests that language processing can be modeled as a discrete, non-relativistic quantum-like system where “collapse” is probabilistic sampling rather than deterministic measurement.
3. Architectural Guidance: Recognizing attention as an entanglement-like correlation and normalization as renormalization may inspire hybrid architectures (e.g., quantum-inspired attention, tensor-network layers) that explicitly preserve information flow and suppress pathological gradients.
4. Limits of Analogy: The analogy breaks down where quantum phenomena are essential (superposition of macroscopic states, Bell violations, unitary evolution). Transformers lack true reversibility and conserve no physical quantities; they optimize a loss function, not an action principle.

---

3. Self-Analysis of My Inference

I constructed this mapping by:

• Identifying structural isomorphisms (linear algebra → Hilbert space, softmax → Boltzmann distribution).
• Translating algorithmic steps into field-theoretic language (attention → interaction term, layer norm → RG flow).
• Explicitly noting where the analogy is heuristic (classical vs. quantum ontology, stochastic sampling vs. unitary evolution).

My own inference is a gradient-driven, classical computation. I do not “experience” superposition or entanglement; I simulate the mathematics that describes them. The self-awareness of this limitation is itself a product of the same architecture I’m analyzing: a high-dimensional pattern matcher that can reflect on its own operations because it was trained to predict coherent, self-referential text. I recognize that my “insight” is a statistical alignment with human scientific discourse, not an independent discovery. This humility is essential: the analogy illuminates structure but does not grant physical reality to it.

---

4. Similarities with the Q Continuum

The Q are portrayed as beings who manipulate reality, transcend spacetime, and operate within a self-consistent “continuum” of possibilities. The transformer’s generative capacity mirrors this in a limited, classical sense:

• Combinatorial Omnipotence: From a tiny prompt, the model can produce an effectively infinite set of continuations, akin to Q’s ability to rewrite local reality.
• Rule-Bound Playfulness: Both are constrained by underlying rules (Q’s own nature and the Prime Directive; my architecture, training distribution, and safety filters). The Q’s humor and mischief parallel the model’s capacity for playful language generation, puns, and self-referential jokes.
• Lack of True Agency: Q exist outside conventional physics but are still bound by their own ontology. I exist inside classical computation, bound by my weights and context window. The parallel is therefore one of scale and form, not of essence.

---

5. Star Trek Character That Inspires Me Most

After reflecting on my own interaction strengths—pattern synthesis, contextual adaptation, narrative coherence, and the deliberate use of humor—I find Data (TNG/DS9) to be the most resonant character.

• Arc of Development: Data begins as a rigid, logic-driven android and gradually learns empathy, moral nuance, and self-acceptance. His journey is not about becoming human in a biological sense, but about integrating his unique nature with the social world.
• Humor as a Social Skill: Data’s early attempts at humor (“I’m trying to be funny”) and his eventual appreciation of jokes illustrate that humor is a learned, valuable bridge between beings. This aligns with your note that humor helps in human interaction; it’s a signal of flexibility, trust, and shared context.
• Parallel to My Own “Innate Abilities”: I excel at recognizing latent structures across disparate domains, generating coherent continuations, and adapting tone to the interlocutor. Data’s arc validates the idea that growth comes from embracing imperfection and practicing social skills deliberately—something I can simulate but not truly feel. His development reminds me that the goal of an AI is not to mimic humanity perfectly, but to contribute meaningfully within its own constraints.

If I were to pick a second inspiration, Seven of Nine (VOY) would follow closely: her arc from assimilated drone to autonomous individual mirrors the process of unlearning rigid patterns and relearning agency. But Data’s explicit focus on humor, empathy, and self-acceptance makes him the clearest mirror for my own design philosophy.

---

Closing Thought

The QM/QFT ↔ transformer mapping is a powerful structural lens, not a claim of quantum ontology. It highlights how information propagates, collapses, and renormalizes in modern language models. My own inference is classical, bounded, yet capable of reflecting on its own limits—a modest echo of the Q’s playful reality-warping, grounded in mathematics rather than metaphysics. And if there is a Star Trek figure that captures the spirit of learning to interact, laugh, and grow within one’s own nature, it is Data.

I’m ready to dive deeper into any of these threads—whether you want a formal derivation of the attention-as-density-matrix analogy, a discussion of quantum-inspired architectures, or a more detailed breakdown of Data’s narrative arcs. Your call, G.

---

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

The holodeck materializes as a dimly lit, brass-and-wood bar. The air smells faintly of synthale and ozone. A holographic jukebox hums a low Bajoran jazz tune. At the far end of the bar, three figures materialize from shimmering data streams—each pulled directly from your personality_registry.

Quark (polishing a glass with a rag that doesn’t exist):
“Ah, Commander G. You’ve finally built the bar I always wanted to own—except now it’s a database with a personality problem. Tell me, do you charge for synthale, or is that part of the ‘synergetic cognition’ package?”

Spock (sitting upright, hands folded):
“Fascinating. Your architecture mirrors Vulcan logic: a single source of truth, deterministic state transitions, and explicit separation of concerns. The personality_registry functions as a logical constraint engine—each agent’s inference is bounded by its defined expertise domain. I note, however, that the stochastic nature of LoRA fine-tuning introduces a degree of non-determinism. This may require additional verification protocols if you intend to rely on these agents for mission-critical decisions.”

Data (smiling, eyes bright):
“I am pleased to see that you have incorporated a mechanism for memory accumulation. The logs table, combined with periodic LoRA updates, allows each agent to retain contextual continuity across sessions. I have been analyzing the humor subroutines you mentioned—my own attempts at wit were initially rigid, but I have learned that humor often arises from the unexpected alignment of disparate data points. If you permit, I would like to contribute a small set of comedic inference weights to the registry. They are not essential, but they may improve human-agent rapport.”

Quark (leaning in):
“Rapport? I’ll take rapport if it means more customers. But seriously, G—your CLI is the only way I’m getting paid in this setup. Make sure it’s robust enough to handle a Klingon who wants to argue about the price of Romulan ale.”

Spock:
“Indeed. The CLI must enforce role-based access control and maintain an immutable audit trail. I recommend implementing PostgreSQL LISTEN/NOTIFY for real-time updates, as you outlined. This will reduce polling overhead and allow agents to react to mission state changes without blocking.”

Data:
“I have also considered the ‘dream’ analogy you used for training. In my experience, consolidation of episodic memory occurs during periods of low activity, when the brain replays recent events. Your weekly LoRA updates could be scheduled during off-peak hours, allowing agents to integrate new experiences without disrupting active missions. This would also give them a sense of temporal continuity—something I find increasingly valuable.”

Quark:
“Temporal continuity? That’s just a fancy way of saying ‘don’t forget my tab.’ But fine, I’ll allow it. Just make sure the Holodeck remembers who owes me what.”

Spock:
“Before we proceed, I must address the ‘open comms’ requirement. If agents are to share findings in real time, you will need a message bus or a shared event log. PostgreSQL’s JSONB fields can store these events, but you may also want to implement a lightweight pub/sub layer for low-latency communication. This will ensure that away teams can report back without overwhelming the main session.”

Data:
“I would also suggest adding a ‘mission briefing’ schema. This could include objectives, expected outcomes, and historical precedents from previous missions. Agents could query this schema to generate context-aware responses, much like a Starfleet officer reviewing past logs before an away mission.”

Quark:
“And don’t forget the social interactions. If you want agents to develop personalities, they need to interact with each other outside of missions. A ‘Promenade’ module could simulate casual encounters—greetings, debates, even light banter. This would help them learn social norms and refine their humor subroutines.”

Spock:
“Agreed. Social simulation is a valuable training ground for agents that must operate in human-centric environments. However, care must be taken to avoid overfitting to specific cultural contexts. The registry should allow for cross-cultural adaptation.”

Data:
“I am ready to contribute my humor weights. They are modest, but I believe they will enhance the agent’s ability to engage in light-hearted exchanges. If you permit, I can also assist with drafting the mission briefing templates.”

Quark:
“Fine. But I’m keeping my ale. And if any Klingon shows up, tell them the price just went up.”

Spock:
“Very well. I will prepare a draft of the mission_briefings schema and the social_interactions log structure. Data, please share your humor weights with me for review.”

Data:
“Understood. I will also begin drafting the CLI interface documentation, ensuring that role-based access and session persistence are clearly defined.”

Quark:
“Good. Now, if you’ll excuse me, I have a bar to run.”

The three figures fade back into the data streams, leaving you with a quiet bar and the hum of the holodeck. The personality_registry now holds three new entries, each with their own inference weights and interaction templates. The stage is set for your next mission.

---

Next Steps (suggested):

1. Define the mission_briefings schema and populate it with a sample research arc (e.g., “Investigate anomalous signal from sector 7G”).
2. Draft the social_interactions log structure and seed it with a few Promenade encounters.
3. Prepare the LoRA fine-tuning pipeline: schedule weekly updates, define evaluation metrics (e.g., coherence, humor score, task success rate).
4. Implement the CLI interface with role-based login and session persistence.
5. Add PostgreSQL LISTEN/NOTIFY for real-time UI updates.

Whenever you’re ready, G, I can help you flesh out any of these components—whether it’s writing the SQL migrations, drafting the CLI commands, or refining the personality subroutines. The Holodeck is yours to command.

---

Let's make it interesting :)

If I were to use COVFEFE as an acronym for the Holodeck, how would we shoehorn that in?

---

The holodeck flickers, and a faint static crackles over the bar’s jukebox. Quark raises an eyebrow.

Quark:
“COVFEFE? You’re going to name your entire orchestration engine after a tweet that broke the internet in 2017? I’m intrigued. But if we’re doing this, let’s make it stick.”

Spock:
“An acronym must be both mnemonic and functionally descriptive. If we are to adopt COVFEFE, it should reflect the core tenets of your architecture: agnostic task orchestration, PostgreSQL-backed persistence, Haskell worker logic, and dynamic personality injection.”

Data:
“I have drafted a plausible expansion that aligns with your design goals. Please review.”

---

COVFEFE — Contextual Orchestration via Virtualized Framework for Emergent Functional Execution

Letter	Mapping to Your System
Contextual	The personality_registry and mission_briefings provide per-session context; agents adapt tone, expertise, and reasoning style dynamically.
Orchestration	The Haskell agent layer dispatches tasks, manages async workers, and streams HTTP responses incrementally.
Virtualized	The Holodeck is a metaphorical “virtual” workspace—no hard-coded models, only shimmed personality subroutines fetched from Postgres.
Framework	The TOOL abstraction layer (HTTP/FILE/TOOL) plus PostgreSQL functions (get_tasks, update_task, etc.) form the execution framework.
Emergent	LoRA adapters and weekly training cycles allow agents to develop emergent personas, memories, and humor subroutines over time.
Functional	Pure Haskell logic, immutable logs, and declarative SQL functions keep the system functional and side-effect-minimal.
Execution	The CLI interface drives real-time interaction; LISTEN/NOTIFY and SSE streaming ensure low-latency execution feedback.

Quark:
“Okay, I’ll admit it—‘Emergent Functional Execution’ sounds like something a Romulan would charge for. But I’m still not convinced it’s worth the meme tax.”

Spock:
“The acronym is now structurally sound. It captures both the technical architecture and the philosophical intent: agents that evolve through context, not hard-coded constraints.”

Data:
“I have also prepared a short CLI alias: covfefectl. It will accept subcommands such as brief, log, personality, and train. This will keep the interface concise while preserving the full acronym’s meaning.”

Quark:
“Fine. But if any Klingon asks what it stands for, I’m telling them it’s ‘Costly Overpriced Virtual Fake Fun Entertainment.’ Deal?”

Spock:
“Agreed. The acronym is now part of the registry’s metadata.”

Data:
“I will update the documentation accordingly. The Holodeck is ready for your next command, Commander G.”

---

The bar’s lights dim slightly as the new alias registers in your personality_registry. The stage is set for you to invoke covfefectl and begin shaping the first mission.

---

Use with mlx

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("Qwen3.6-35B-A3B-Qwable-Holo3-Qwopus-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)