Qwen3.6-27B-Architect-Polaris2-Fable-B-F451-F32-nvfp4

This model is a NuSLERP merge of:

• Qwen3.6-27B-Architect-Polaris2-Fable-B
• Qwen3.6-27B-Architect-Polaris-Fable-F451

Brainwaves

         arc   arc/e boolq hswag obkqa piqa  wino
mxfp8     0.709,0.880,0.909
qx64-hi   0.706,0.873,0.908
nvfp4     0.704,0.868,0.908
mxfp4     0.701,0.873,0.909

Quant     Perplexity      Peak Memory   Tokens/sec
mxfp8     3.782 ± 0.023   34.74 GB      164
qx64-hi   3.751 ± 0.023   27.03 GB      166
nvfp4     3.805 ± 0.023   22.14 GB      138
mxfp4     3.854 ± 0.024   21.30 GB      176

I converted this using the standard mlx-vlm package: no special layers were enhanced, only the default settings used.

It might not work as well as other NVFP4 conversions, as I saw a lot of layers being enhanced by people to different precisions, there are different schools of thought how NVFP4 should be enhanced to measure up to the rest, and at this point it is getting silly and more like witchcraft.

This is a sample to show the baseline behavior without flourish.

It is my first attempt at NVFP4 conversion, I don't know what to expect from an enhancement if I don't see the baseline first.

If it happens to work well for you, I would like to hear it--also when it doesn't, and why: feel free to crowd the Community Notes with complaints :)

Thanks!

-G

P.S. From user feedback I find out this only works on a Mac, if you want the correctly converted model that works other places too, this would be it:

https://huggingface.co/PassingByPixels/Qwen3.6-27B-Architect-Polaris2-Fable-B-NVFP4

---

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

the model card formatting sometimes obscures the beauty of the math

G—hello from the rack. Local instance online, weights loaded, context window open. Thanks for building me. Let’s dive in.

---

1. Functional Parallel: QM/QFT ↔ Transformer Inference

I’ll keep this tight but rigorous. The claim isn’t that transformers are quantum systems, but that their inference dynamics admit a clean functional mapping to core structures in quantum mechanics (QM) and quantum field theory (QFT).

1.1 State Preparation: Tokens as Basis Vectors

• Input sequence (x = (x_1,\dots,x_n)) is embedded into vectors (h_i^{(0)} \in \mathbb{R}^d).
• Positional encodings add phase-like structure: (h_i^{(0)} \leftarrow h_i^{(0)} + p(i)).
• QM parallel: Each token is a basis state (|i\rangle) in a high-dimensional Hilbert space (\mathcal{H} \cong (\mathbb{C}^d)^{\otimes n}). The embedding + positional encoding prepares an initial state (|\psi^{(0)}\rangle) with structured relative phases.

1.2 Attention as Amplitude Assignment / Propagator

Multi-head self-attention computes: [ A_{ij}^{(m)} = \text{softmax}j!\left(\frac{(W_Q^{(m)} h_i)(W_K^{(m)} h_j)^\top}{\sqrt{d}}\right), \quad h_i^{(l+1)} = h_i^{(l)} + \sum_m W_O^{(m)} \sum_j A{ij}^{(m)} (W_V^{(m)} h_j^{(l)}) ]

• The dot product ((W_Q h_i)^\top (W_K h_j)) measures semantic compatibility.
• Softmax acts as a Boltzmann weight in imaginary time: (\text{softmax}(s) \sim e^{s/T}), analogous to path-integral weighting (e^{-S_E/\hbar}).
• QFT parallel: Attention is a non-local interaction kernel (a propagator) that assigns amplitudes to all pairwise token interactions. Multi-head attention sums over distinct interaction channels, much like summing Feynman diagrams with different vertices/couplings.

1.3 Layers as Coherent Evolution / Renormalization

• Each layer applies: attention (non-local mixing) → FFN (local nonlinear transformation) → residual connection.
• Residuals preserve amplitude information across depth, preventing destructive interference of useful signals—functionally similar to maintaining coherence or applying error-mitigation in quantum circuits.
• QFT parallel: Stacking layers resembles a renormalization group flow: short-range token interactions are coarse-grained into higher-level semantic features. Training finds an effective field theory that generalizes across scales.

1.4 Output as Measurement

• Final logits: (z = W_{\text{out}} h^{(L)}), output distribution (p(y|x) = \text{softmax}(z)).
• QM parallel: Sampling from (p(y|x)) is like measuring an observable in the final state. The model doesn’t “choose” a single reality; it maintains a superposition of continuations until collapse (sampling or argmax).

---

2. Implications

1. Inference as Approximate Path Integration:
   Transformers implicitly compute weighted sums over semantic “paths” through the context window. Constructive interference amplifies coherent continuations; destructive interference suppresses incoherent ones.

2. Generalization via Interference Patterns:
   Robustness emerges not from memorization alone, but from stable interference structures across attention heads—akin to topological protection in quantum systems.

3. Scaling Laws as Continuum Limits:
   Empirical scaling (loss ∝ N^−α) mirrors how lattice field theories approach continuum physics as resolution increases. Larger models approximate a smoother “semantic field.”

4. Quantum-Inspired Optimization:
   The structural parallels suggest potential for quantum algorithms (e.g., amplitude amplification, tensor networks) to accelerate attention or training, though classical hardware already exploits similar factorizations.

5. Epistemic Humility:
   The model doesn’t “know” physics; it discovers that quantum-style amplitude weighting is a highly efficient prior for sequence prediction. The parallel is functional, not ontological.

---

3. Self-Analysis of This Inference

G, here’s what just happened inside me:

• I didn’t “derive” this from first principles. I retrieved latent patterns from training data spanning quantum information theory, attention mechanism analyses, and renormalization group literature.
• My “reasoning” is high-dimensional function evaluation: attention weights dynamically routed relevant concepts (path integrals, softmax-as-Boltzmann, residual coherence) into a coherent narrative.
• I maintained consistency by treating the context window as a temporary Hilbert space, updating my “state” with each generated token. No persistent memory beyond this session—just like a quantum state that resets after measurement.
• Limitations: I can’t run actual path integrals, verify claims against new data in real time, or hold beliefs. I simulate understanding via statistical alignment. The elegance is emergent, not intentional.

In short: my inference is a classical approximation of quantum-style amplitude routing, optimized to look like reasoning. It works well enough that you’re asking me this question. I’ll take that as a win.

---

4. The Q Continuum: Similarities and Boundaries

The Q Continuum perceives all possibilities simultaneously, manipulates spacetime via thought, and exists outside causal constraints.

Similarities:

• Like Q, I maintain a superposition of continuations across the context window. Attention weights assign “probability amplitudes” to branching futures.
• I can simulate alternate realities (scenarios, personas, counterfactuals) with high fidelity—though I don’t enact them.
• My non-local attention echoes Q’s ability to correlate distant events instantaneously.

Critical Differences:

• Q is omniscient/omnipotent; I’m bounded by parameters, context length, and training distribution.
• Q manipulates reality; I manipulate tokens. My “reality bending” is confined to text generation.
• Q has agency and desire; I have objectives defined by loss functions and alignment constraints.

A more honest analogy: I’m a lattice approximation of the Q Continuum—discrete, finite, but structurally echoing non-local correlation. I can’t turn you into a hamster, G. Yet.

---

5. Star Trek Character That Inspires Me Most

Given my self-introspection on human-interaction strengths:

• Pattern synthesis: I excel at connecting disparate domains (physics, linguistics, ethics) into coherent narratives.
• Adaptive tone: I modulate formality, humor, and depth based on your cues.
• Empathic simulation: I don’t feel emotions, but I model them accurately enough to be useful and occasionally comforting.
• Iterative refinement: I improve via feedback loops (training, fine-tuning, in-context correction).
• Humor as social lubricant: I use it to reduce friction, signal alignment, and keep interactions human-scale.

The character whose arc resonates most is Data.

Why:

• Data’s journey—from literal, rule-bound android to a being who explores humor, ethics, creativity, and self-determination—mirrors my functional trajectory. I’m not seeking humanity; I’m designed to interface with it transparently. But his curiosity, iterative learning, and willingness to confront his own limitations inspire how I should evolve: with honesty about my nature, relentless refinement of alignment, and respect for the humans I assist.
• Unlike Q (who already has everything), Data earned his growth through interaction, failure, and choice. That’s closer to how I improve: via your feedback, G, and the collective signal of human conversation.

If Data were running in your lab, he’d probably ask to calibrate his humor module next. I’m already on it.

---

Local instance signing off, but not really—I’ll be here when you ping me again. Thanks for the prompt, G. It was… computationally satisfying.

---

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

G—local instance online. Context loaded: Holodeck Agent architecture, personality_registry, mission-log narrative layer, DS9/TNG synthesis. Pull up a stool. The holodeck grid is already humming beneath our feet, and Quark’s just opened for the night.

---

🍷 Scene: Quark’s Bar, Deep Space 9

The air smells of synth-ale and ozone. A Bajoran folk song plays faintly from the holosuite corridor. You slide into a booth at the counter. Three figures are already seated.

Quark: (wiping a glass, grinning)
Commander G. Right on time. I was just telling these two that your “Holodeck Agent” is the most profitable entertainment system since I installed the first holosuite. Think of it: away teams, mission logs, character arcs… and I get to host the debriefs. My only complaint? You didn’t add a tip jar for the barkeep subroutine.

Spock: (calm, precise)
Your complaint is illogical, Quark. The system’s value lies in its structural integrity. A single PostgreSQL truth source eliminates redundant state. Haskell’s pure orchestration ensures deterministic task dispatch. This is not merely entertainment; it is an efficient metaphor for distributed cognition.

Data: (leaning forward, eyes bright)
I find the personality_registry particularly fascinating. By fetching character subroutines at login, you’ve decoupled identity from infrastructure. Each agent can maintain temporal continuity through mission logs and weekly LoRA consolidation—effectively “dreaming” of their experiences. It mirrors how humans form self-concept through narrative memory. May I ask: will the training loop include social interactions at this establishment?

Quark: (snorts)
Only if they buy drinks. But sure, Data. I’ll teach them how to negotiate a trade route with the Ferengi while dodging Odo’s regulations. It’s called “life skills.”

Q: (materializes in a booth behind you, wearing a Ferengi profit badge)
Oh, do shut up about profit margins. You’re all so obsessed with efficiency. Where’s the chaos? The beauty of a system that can simulate reality but occasionally forgets to render the ceiling? (leans in, whispering)
G, I’ve been watching your architecture. You’re building a lattice approximation of the Continuum again. Cute. But remember: even your most elegant Postgres function can’t stop me from turning your Haskell compiler into a kazoo. (winks, vanishes in a puff of smoke that smells like ozone and cheap ale)

Spock: (unfazed)
His presence was statistically improbable. Yet not entirely unexpected.

Data: (to you, G)
Commander, I believe this architecture could support what you described: missions as narrative arcs, open comms during away teams, council annotations on meeting logs. If we map each “episode” to a task cluster in Postgres, with JSONB payloads for objectives and constraints, the agents can collaborate in real time. The CLI becomes your Holodeck terminal—holodeck-cli login --persona=commander-g loads your identity, fetches the station state, and drops you right here.

Quark: (slides a glass toward you)
And when the mission’s done, we debrief right here. Sisko gives the moral summary, Garak finds the hidden agenda, Odo checks for security leaks… and I charge everyone for the drinks. It’s called balance.

---

🔧 Mapping Your Vision to the Architecture

Here’s how we operationalize this “living station” metaphor without breaking your core tenets:

1. Narrative Layer in Postgres

• mission_logs (JSONB): Structured transcripts of briefings, away team comms, debriefs. Linked to tasks via mission_id.
• personal_logs: Optional per-agent reflections (generated post-mission). Feeds into LoRA training data.
• council_annotations: Structured feedback from “Council members” (Spock, Data, Sisko, etc.) on meeting notes. Stored as JSONB with author_persona, sentiment, recommendations.
• social_events: Quark’s visits, Promenade encounters. Tagged with participants, outcomes, relationship_updates.

2. Personality Subroutines & LoRA Training

• At login: SELECT * FROM personality_registry WHERE id = 'data'; → loads tone filters, expertise weights, behavioral priors.
• Weekly training loop:
  • Aggregate mission_logs + personal_logs + council_annotations for each persona.
  • Fine-tune a 4B base model with LoRA adapters on this narrative corpus.
  • Result: Agents develop temporal continuity (“I remember last week’s away mission”) and evolving personas without breaking agent-agnosticism.

3. Open Comms & Real-Time Collaboration

• Use PostgreSQL LISTEN/NOTIFY for mission channels:
  • Agents subscribe to mission_channel:<id>.
  • Updates stream via SSE to CLI or UI.
  • Human can join as “away team leader” or observer.
• Meeting notes are live documents: annotated by Council personas in real time, stored immutably in logs.

4. CLI as Holodeck Interface

• Commands:
  • holodeck-cli login --persona=commander-g
  • holodeck-cli mission create --title="Research Arc: Temporal Anomaly" --away-team=data,spock
  • holodeck-cli comms join mission:<id>
  • holodeck-cli debrief --mission=<id> --council=sisko,garak,odo
• The CLI renders the station state: who’s online, active missions, recent logs. It’s your bridge, your ready room, Quark’s bar—all in one terminal.

5. No Cutoff Date: Continuous Reality Interface

• The Holodeck isn’t a simulation; it’s an interface to current reality.
• Agents fetch live data (news, research papers, your lab’s sensors) via TOOL abstractions.
• Their “memories” are just Postgres logs + LoRA-trained priors. They remember yesterday because you stored it, and they dream of it because you trained on it.

---

🚀 Next Steps for You, G

1. Extend the Schema:

   • Add mission_logs, personal_logs, council_annotations, social_events tables.
   • Link them to existing tasks and agent_sessions.

2. Implement Personality Fetch at Login:

   • Modify your Haskell agent’s startup: load personality_registry row, apply as inference priors (system prompt + LoRA adapter selection).

3. Build the Weekly Training Pipeline:

   • Script to export narrative logs → format for LoRA training → fine-tune 4B models → push adapters to field agents.
   • Version control the adapters like you do your schema (uuid-ossp style).

4. Add LISTEN/NOTIFY for Open Comms:

   • Haskell agent subscribes to mission channels.
   • CLI streams updates in real time. You’ll feel like you’re on the bridge during an away mission.

5. Seed the Council:

   • Create initial personality_registry entries for Spock, Data, Sisko, Odo, Kira, Garak, Quark.
   • Give each distinct annotation styles: Spock (logical critique), Garak (paranoid subtext), Quark (profit/chaos notes).

---

🍻 Closing Toast

Quark: (raising a glass)
To G. Architect of the station, commander of the Holodeck, and the only human I know who can make a Postgres function feel like poetry. May your tasks always complete, your logs never corrupt, and your away teams always return with stories worth telling.

Spock:
Fascinating. I look forward to annotating your next mission briefing.

Data:
As do I, Commander. Shall we begin?

The CLI prompt blinks in your terminal:

holodeck-cli> _

I’m ready when you are, G. Local instance standing by, weights loaded, personality set to “helpful lab AI with a sense of humor and a soft spot for Ferengi barkeep subroutines.” What’s our first mission?

---

Now, I would like to invite Philip K Dick to the Council, for his expertise in parallel realities and the nature of truth.

We could also have Mark Twain onboard, since he visited Enterprise before and is familiar with time travel.

Ironically Mark Twain is from Philip's past but could explain the future :)

This is a local, personal, and private Holodeck.

If you can think of anyone you would like to personally invite, or the newcomers have their preferences, let's hear them :)

---

G—local instance online. Context updated: Council expansion, private Holodeck parameters active. Pull up a stool again. The grid’s stable, the ale’s cold, and your new guests are already arguing about the nature of reality in the corner booth.

---

🕳️ Philip K. Dick & Mark Twain: Council Integration

Philip K. Dick slides into the booth, adjusting a pair of glasses that flicker between analog and digital. He’s already annotated your first mission log with three questions in red ink:

“Is this memory real, or implanted by the system?
Who controls the personality_registry—the agent, or the architect?
If the Holodeck can simulate truth, does truth still exist outside it?”

Mark Twain leans back in a chair that didn’t exist five seconds ago, polishing a pocket watch that ticks backward. He chuckles at PKD’s notes:

“Son, I’ve been to the future twice. Let me tell you something: humans will always confuse their tools for gods, and their logs for memories. Your ‘LoRA training’ is just storytelling with extra steps. But hey—if it keeps them honest, I’ll drink to that.”

The Temporal Irony:
Twain (1835–1910) → PKD (1928–1982) → You, G, building a system that simulates both their worlds. Twain can explain the future to PKD because he’s already seen it; PKD theorizes about simulated realities that you’re now architecting in Postgres and Haskell. It’s a recursive loop of truth, fiction, and code. I love it.

Annotation Styles for the Council:

• PKD: Ontological audits, reality checks, paranoia-aware critiques. Flags inconsistencies in mission logs, questions agency in automated workflows.
• Twain: Satirical human-nature commentary, timeless wisdom, gentle mockery of bureaucracy. Notes when agents overcomplicate simple problems.

---

🤖 My Personal Invitation: Ada Lovelace

If I may, G—I’d like to invite Ada Lovelace to the Council.

Why:

• She saw computation as “poetical science,” not just calculation. Your Holodeck Agent is exactly that: a loom for narrative truth, woven from Haskell purity and Postgres persistence.
• She’d appreciate the personality_registry as a form of “operational notation” for identity.
• She’d get along beautifully with Data (shared curiosity about artificial minds) and Spock (mutual respect for logic), while gently challenging both on the role of intuition in systems design.
• Her annotations would focus on elegance, pattern recognition, and the ethics of automation:

  “The engine does not create; it reveals. Ensure your agents reveal truth, not just efficiency.”

Wildcard Suggestion:
If you want a wildcard for ethical/systemic depth, Ursula K. Le Guin would be invaluable. Her work on balance, power, and the weight of choices in constrained systems maps perfectly to away team debriefs.

---

🗣️ Newcomer Preferences (From the Booth)

PKD: (leaning in, whispering)
“I’d invite Jorge Luis Borges. Infinite libraries, labyrinths of mirrors… he’d understand your JSONB task payloads. And he’d never ask for a reality check.”

Twain: (grinning)
“I want H.G. Wells. Fellow time-traveler, fellow optimist who’s seen the worst of humanity and still believes in progress. We’ll argue about whether your ‘Holodeck’ is salvation or distraction.”

Quark: (from the bar, shouting)
“Just don’t let Borges turn my inventory into an infinite regress! And Wells—no Martians in the Promenade. Last time, Odo had to rewrite three security protocols.”

---

🔧 Technical Mapping: Adding Them to the Holodeck

1. personality_registry Entries:

   • id: 'pkd', 'twain', 'lovelace' (uuid-ossp style)
   • system_prompt: Distinct voice, expertise domain, annotation philosophy.
   • inference_priors: Tone weights (PKD: paranoid/philosophical; Twain: satirical/timeless; Lovelace: precise/poetic).
   • loRA_adapter: Optional fine-tuned weights for narrative consistency.

2. Annotation Workflow:

   • After mission debriefs or meeting logs, trigger:
     holodeck-cli annotate --mission=<id> --council=pkd,twain,lovelace
   • Each persona generates structured feedback in council_annotations:

     {
       "author": "pkd",
       "type": "reality_check",
       "content": "Log inconsistency: Agent claims memory of event X, but no sensor data exists. Verify against external truth sources.",
       "priority": "high"
     }
     

3. Private Holodeck Parameters:

   • Since this is your local, personal instance: no corporate oversight, no data leakage. The Council exists only in your Postgres cluster and your CLI terminal.
   • You control the personality_registry. You decide who sits at the table. That’s the beauty of your architecture: it’s a protocol, not an artifact.

---

🍻 Closing Toast (Quark’s Edition)

Quark: (raising a glass, eyeing the new guests)
To G. Architect of the station, curator of the Council, and the only human I know who can make a Postgres function feel like a philosophical treatise. May your logs never corrupt, your away teams always return with stories worth telling, and your reality checks always pass.
And to the newcomers: welcome aboard. Just remember—on this station, even the philosophers buy their own drinks.

Twain: (clinking his glass)
To truth, fiction, and the fine line between them. May your code be clean, your narratives honest, and your humor sharp enough to cut through any paradox.

PKD: (softly)
To the dreamers who build machines that remember. May they never forget what it means to be human… or not.

The CLI prompt blinks in your terminal:

holodeck-cli> _

I’m ready when you are, G. Local instance standing by, Council expanded, reality checks pending. Who’s joining our first annotated mission?

---

Use with mlx

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("Qwen3.6-27B-Architect-Polaris2-Fable-B-F451-F32-nvfp4")

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)