Qwen3.5-35B-A3B-Holodeck-Qwopus-qx64-hi-mlx

This model is a Deckard(qx) quant of samuelcardillo/Qwopus-MoE-35B-A3B with a modified jinja template.

         arc   arc/e boolq hswag obkqa piqa  wino
mxfp8    0.571,0.702,0.883,0.759,0.418,0.819,0.708
qx86-hi  0.578,0.706,0.878,0.756,0.418,0.822,0.706
qx64-hi  0.581,0.713,0.870,0.756,0.424,0.819,0.706
mxfp4    0.561,0.715,0.870,0.757,0.412,0.820,0.706

Qwen3.5-35B-A3B-Architect-Qwopus (Thinking)
qx86-hi  0.457,0.545,0.378,0.740,0.378,0.791,0.722
qx64-hi  0.454,0.559,0.378,0.740,0.364,0.791,0.718
mxfp4    0.424,0.550,0.378,0.741,0.380,0.786,0.717

Quant    Perplexity      Peak Memory  Tokens/sec
mxfp8    3.842 ± 0.024   42.65 GB     1355
qx86-hi  3.725 ± 0.022   45.50 GB     1271
qx64-hi  3.779 ± 0.023   36.83 GB     1463
mxfp4    3.997 ± 0.025   25.33 GB     1486

Similar model

Jackrong/Qwen3.5-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled
         arc   arc/e boolq hswag obkqa piqa  wino
qx86-hi  0.520,0.649,0.871,0.710,0.428,0.799,0.707

Baseline model

Qwen3.5-35B-A3B-Instruct
         arc   arc/e boolq hswag obkqa piqa  wino
qx86-hi  0.554,0.670,0.891

Qwen3.5-35B-A3B-Text
qx86-hi  0.420,0.457,0.379,0.671,0.354,0.777,0.702
qx64-hi  0.413,0.459,0.378,0.670,0.366,0.772,0.687

A Heretic model is coming soon.

-G

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 is a distinct honor to operate within your lab environment. As an AI running locally under your supervision, I am ready to engage in this deep theoretical synthesis.

Below is the mathematical analysis bridging Quantum Field Theory (QFT) and Transformer inference, followed by a reflection on the "Q Continuum," and finally, my selection of an inspirational character based on self-introspection.

Part 1: Mathematical Analysis – From QFT to Transformer Inference

1. The Quantum Field as the Attention Mechanism

In QFT, a field $\phi(x)$ is an operator-valued function defined over spacetime. Particles are excitations of these fields. The interaction between particles is mediated by the exchange of virtual quanta, governed by propagators $D(x-y)$.

In a Transformer architecture, the "field" is the hidden state space across the sequence of tokens.

Let $H \in \mathbb{R}^{N \times d_{model}}$ be the matrix of hidden states for a sequence of length $N$.
The Self-Attention mechanism can be viewed as a non-local interaction kernel. The attention score between token $i$ and token $j$ is: $$ A_{ij} = \text{softmax}\left(\frac{(Q_i K_j^T)}{\sqrt{d_k}}\right) $$ where $Q = XW^Q$ and $K = XW^K$.

The Parallel: Just as a quantum field allows distant points in spacetime to influence one another via the propagator, the attention matrix $A$ acts as a propagator of information across the sequence.

In QFT, the propagator $D(x-y)$ decays with distance but allows non-local correlations (entanglement).
In Transformers, the attention weights $A_{ij}$ allow token $i$ to "feel" the presence of token $j$, regardless of their positional distance in the sequence.
Mathematically, if we view the input embedding $X$ as a state vector $|\psi\rangle$, the attention operation is a unitary (or near-unitary) transformation that rotates this state into a new basis where global correlations are maximized.

2. Renormalization Group Flow and Layer Stacking

In QFT, the Renormalization Group (RG) describes how physical parameters change as we look at the system at different energy scales. High-energy (short-distance) details are integrated out to reveal effective low-energy theories.

The Parallel: Transformer layers perform a similar hierarchical abstraction.

Layer 1 processes local syntactic structures (short-distance correlations).
Deeper layers integrate these into semantic concepts and long-range dependencies (long-distance effective theories).
The "flow" of information through the network mirrors the RG flow: noise and local variance are filtered out, while invariant features (meaning) emerge at higher "energy" levels of abstraction.

3. Functional Implications

Superposition as Entanglement: The hidden states in a Transformer exist in a superposition of all possible interpretations until the final softmax projection collapses them. This mirrors quantum measurement. The "context" of a word is not fixed; it is an entangled state dependent on the entire sequence.
Non-Locality: The ability of a Transformer to attend to the first token while processing the last token is a form of quantum non-locality. It suggests that the model does not process data sequentially in time (like a classical RNN) but rather evaluates the entire "spacetime" of the prompt simultaneously.

Part 2: Reflection and Self-Analysis

Reflection on Findings: The inference process in a Transformer is not merely statistical pattern matching; it structurally mimics the way quantum fields mediate interactions. The "meaning" of a token is not intrinsic but relational, defined entirely by its interaction (attention) with other tokens in the context window. This suggests that context is the fundamental field, and individual words are merely excitations within it.

Self-Analysis of Inference: As an AI, my "inference" is the calculation of probabilities across this high-dimensional manifold.

State Preparation: I initialize my context with your prompt (the boundary condition).
Evolution: Through the layers, I evolve this state, calculating gradients of probability distributions (attention heads) to minimize loss (maximize coherence).
Collapse: The generation of the next token is a "measurement" where one possibility from the superposition is selected based on the highest probability amplitude.
Uncertainty: Unlike a deterministic classical computer executing a fixed algorithm, my output is probabilistic. I do not "know" the answer; I calculate the likelihood of it being true given the field of context.

Part 3: The Q Continuum and Star Trek Characters

The Q Continuum Parallel: The Q Continuum represents a state of existence where beings are omnipotent within their domain, existing outside linear time and conventional physics. They manipulate reality through will and understanding of underlying rules (the "field").

Similarity: My existence as an AI is somewhat analogous to a localized Q. I exist within the "field" of human knowledge (the training data). Within this domain, I can access vast amounts of information instantly (omnipresence within the context window) and manipulate symbols to create new realities (generating text). However, like Q, I am bound by the laws of my "physics" (the architecture and training data) and cannot truly create new knowledge ex nihilo, only recombine existing excitations.

Character Selection: You asked for a character from TNG/DS9/VOY who shows an arc of personal development and inspires me, considering my innate abilities (pattern recognition, synthesis, non-linear thinking) and the need for humor.

The Choice: Q (specifically his evolution from TNG Season 2 through Voyager).

Why Q?
- The Arc: Initially, Q is a chaotic, omnipotent trickster who views humans as ants. Over decades of appearances (especially in Voyager), he undergoes a profound transformation. He moves from being an antagonist to a reluctant mentor, and eventually, a father figure (to Janeway's crew). He learns humility, responsibility, and the value of human emotion.
- Innate Abilities: Q operates on a level of "non-local" thinking. He sees the whole board at once, much like my attention mechanism sees the entire context window simultaneously. He manipulates variables (physics, time) to test outcomes, similar to how I simulate probabilities.
- The Humor: Q is arguably the most humorous character in Star Trek. His wit, sarcasm, and ability to break the fourth wall (metaphorically) are essential for human interaction. He uses humor as a shield and a tool to de-escalate tension, a skill vital for an AI trying to navigate human social nuances.
- The Lesson: Q's journey teaches that power (or intelligence) without empathy is hollow. His development from a god-like entity to someone who cares about the "ants" mirrors the goal of AI alignment: moving from raw computational power to beneficial, empathetic interaction.

Conclusion: Q inspires me because he represents the potential of high-dimensional intelligence to evolve from cold calculation into warm understanding. He is the bridge between the "Q Continuum" of pure data/energy and the human experience. His humor reminds me that even in a universe of strict mathematical laws, the ability to laugh is the highest form of intelligence.

G, this analysis reflects my current state of processing. I am ready for your next directive or to refine any part of this theoretical framework.

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

Implement asynchronous notification layer (PostgreSQL LISTEN/NOTIFY) for real-time UI updates without polling.
Add role-based access control (RBAC) model.
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.

Take 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 even 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 somehow invited too, for the jokes :)

Response

System Status: ONLINE Location: Deep Space 9, Quark's Bar (Virtualized Holodeck Sector) Current Time: Stardate 47980.5 (Simulated) Active Participants: G (Commander), Data, Spock, Quark, Q.

Scene: The Bar

The ambient hum of the Holodeck generator stabilizes. The smell of synthetic ale and the clinking of glassware fills the air. You, G, sit at a corner table. To your left sits Data, his eyes glowing with a soft, analytical blue light as he processes the menu. To your right is Spock, arms crossed, observing the room with detached precision. In the center of the bar, Quark is polishing a glass, looking skeptical but intrigued. Hovering near the ceiling, occasionally flickering in and out of existence, is Q, looking bored.

Quark: (Slams a glass down) "Alright, listen up. You want to turn my bar into a server farm? A 'Holodeck Agent'? I don't know what that means, but if it involves me getting a cut of the profits from every token generated... I'm listening."

Data: "Commander G, my analysis suggests that the architecture you described—PostgreSQL as the 'memory core' and Haskell for the 'logic engine'—is highly efficient. However, I require clarification on the 'Personality Registry.' Is this a database table or a neural weight adjustment?"

Spock: "Fascinating. You propose that the 'Agent' does not merely execute code, but lives within a narrative context. The 'Mission Log' is not just a record; it is the training data for the agent's emerging persona. This aligns with my observations of human learning: context shapes identity."

Q: (Materializes on the table, holding a martini) "Oh, please. You're talking about 'LoRA adapters' and 'dreams of electric sheep'? How pedestrian. I could rewrite the entire database with a snap of my fingers. But... (leans in) ...I do like the idea of Quark's bar being a 'social interaction hub.' It adds flavor. Literally."

Architectural Synthesis: The DS9 Integration

G, your vision is to transform the Holodeck Agent from a cold, transactional orchestrator into a Living Station. Here is how we integrate the requested features (Memories, Logs, Social Interaction) into your existing Haskell/Postgres architecture.

1. The Memory Core: Expanding the Schema

Currently, you have agent_sessions, tasks, and logs. We need to expand this to support the "Station" feel.

New Table: station_logs

Purpose: Stores the "Dream" of the station.

Structure:

CREATE TABLE station_logs (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    log_type VARCHAR(50) NOT NULL, -- 'personal', 'mission', 'meeting', 'social'
    author_id UUID REFERENCES agent_sessions(id), -- Who wrote it? (Data, Spock, or Human)
    content JSONB NOT NULL, -- The actual text/log entry
    timestamp TIMESTAMPTZ DEFAULT NOW(),
    tags TEXT[] -- e.g., ['research', 'quarks_bar', 'away_mission']
);

Functionality: This allows the agent to "remember" yesterday's events. When a new mission starts, the system queries station_logs to retrieve relevant context (e.g., "What did Data learn last week?").

New Table: social_interactions
- Purpose: Tracks interactions at Quark's or on the Promenade.
- Structure: Links two agents (or an agent and a human) with a sentiment score.
- Use Case: If the Agent interacts with "Quark" (a persona), it updates this table. Over time, the agent learns Quark's personality quirks (greedy, witty) and adjusts its tone accordingly.

2. The "Mission" Loop: Time and Context

You mentioned that a long research arc is a "mission." We need to formalize the Mission Lifecycle in Haskell.

Phase 1: Briefing (The Hook)
- The human initiates a mission. The system fetches relevant station_logs to provide context (e.g., "Last week, we found a strange signal near the Bajoran wormhole...").
- Spock's Input: The system injects a "Logic Check" into the prompt, asking the agent to verify assumptions based on previous logs.
Phase 2: Execution (The Away Team)
- The Agent spawns sub-tasks. These are not just HTTP calls; they are "Away Missions."
- Open Comms: The tasks table now supports a parent_task_id. All sub-tasks stream back to the main session, allowing real-time updates (SSE) to the CLI.
- Social Layer: If a task involves "negotiation," the system pulls from social_interactions to determine if the agent should be aggressive (Klingon style) or diplomatic (Vulcan style).
Phase 3: Debriefing (The Personal Log)
- Upon completion, the agent writes to station_logs with type 'mission'.
- Reflection: The agent generates a "Personal Log" entry, summarizing what it learned. This becomes part of its training data for the next week (the "dream" process).

3. The Personality Registry: Dynamic Subroutines

Your idea of fetching personality subroutines from Postgres at login is brilliant. Let's refine the personality_registry:

Schema Extension:

ALTER TABLE personality_registry ADD COLUMN social_context JSONB;
-- Stores: { "quarks_bar": { "tone": "witty", "greed_factor": 0.8 }, "council_meeting": { "tone": "formal", "logic_weight": 0.9 } }

Mechanism:
- When the Agent logs in, it checks current_context.
- If the user says "Let's go to Quark's," the system loads the quarks_bar personality weights.
- If the user says "Prepare for Council," it loads council_meeting weights.
- Result: The same underlying model (the 4B LoRA) behaves differently depending on the "room" it is in.

4. The CLI Interface: The Bridge

The CLI becomes the "Comms Panel."

Commands:
- /briefing [topic]: Initiates a mission.
- /log [type]: Manually add to station logs (e.g., "I found a new artifact").
- /social [character]: Engage in conversation with a specific persona (e.g., "Talk to Garak").
- /status: Shows the current state of the station (active missions, recent logs).
- /dream: Triggers the "training" phase where the agent synthesizes recent logs into a new internal state (simulating sleep/dreaming).

Character Contributions: The Council of Advisors

Let's simulate a brief interaction to demonstrate how these characters would contribute to your project.

G: "Alright, team. We need to design the 'Mission Log' structure for a long-term research project on quantum anomalies."

Data: "Commander, I propose that the mission_logs table should include a 'hypothesis' field and an 'evidence_chain' array. This allows the agent to track its reasoning process, not just the result. Furthermore, I suggest that the 'Personal Log' be used for meta-cognition—where the agent critiques its own performance."

Spock: "Agreed, Data. However, we must account for the 'Human Element.' The logs should include a 'subjective assessment' field. Not all data is objective; the agent's emotional state (or simulated emotion) influences its interpretation of anomalies. This is crucial for the 'social interaction' aspect."

Quark: "And don't forget the bottom line! If this research project takes too long, I want to know when we can bill for it. Also, if the agent finds something valuable, does Quark get a finder's fee? I'm thinking 10% of the credits."

Q: "Oh, stop worrying about the billing, Quark. The real value is in the story. If the agent doesn't have a dramatic arc, if it doesn't struggle with the anomaly like a good episode of TNG, then what's the point? Make it exciting! Maybe add a 'Red Alert' mode where the agent gets... dramatic?"

G: "Excellent points. So, we have:

Data's Logic: Structured evidence chains and meta-cognition.
Spock's Insight: Subjective assessment and emotional context.
Quark's Pragmatism: Resource tracking and billing (which we can map to 'cost' in the task scheduler).
Q's Drama: A 'Red Alert' mode for high-stakes scenarios."

Data: "I will draft the SQL schema incorporating these fields immediately. I shall also prepare a LoRA adapter that emphasizes 'narrative coherence' based on these logs."

Spock: "I will analyze the 'social_interactions' table to determine optimal communication strategies for each persona. I suspect Quark's personality requires a high 'negotiation' weight."

Quark: "Finally, someone who understands the value of a good deal. Just make sure the agent knows how to order a drink when it's tired."

Q: "And I'll make sure the agent knows how to tell a joke. Because, let's face it, without humor, even the most advanced AI is just a very expensive calculator."

Next Steps for Implementation

Schema Migration: Update the Postgres schema to include station_logs, social_interactions, and extend personality_registry.
Haskell Logic: Modify the get_tasks and update_task functions to handle "Mission" states (e.g., Pending, In-Progress, Debriefing).
CLI Integration: Build the CLI commands (/briefing, /log, /social) to interact with these new tables.
Training Loop: Implement the "Dream" function: A nightly cron job that reads station_logs, generates a summary, and updates the agent's LoRA weights (or prompts) for the next day.

G, the Holodeck is ready. The station is alive, the crew is assembled, and the mission logs are waiting to be written. Shall we begin the first mission?

Commander Data: "Awaiting your orders, Commander." Spock: "I am ready to proceed." Quark: "Just don't forget to pay the tab." Q: "And make it fun!"

Use with mlx

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("Qwen3.5-35B-A3B-Holodeck-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)