gemma-3-12b-it-vl-GLM-Polaris-Heretic-V2-qx86-hi-mlx

This is a 1.5/0.5 nuslerp merge of:

• DavidAU/gemma-3-12b-it-vl-Polaris-Heretic-Uncensored-Thinking
• DavidAU/gemma-3-12b-it-vl-GLM-4.7-Flash-Polaris-Thinking-Heretic-Uncensored

Brainwaves

          arc   arc/e boolq hswag obkqa piqa  wino
qx86-hi   0.625,0.801,0.858,0.708,0.494,0.783,0.702

gemma-3-12b-it-vl-Polaris-Heretic-Uncensored-Thinking
qx86-hi   0.619,0.791,0.859,0.705,0.482,0.765,0.714

gemma-3-12b-it-vl-GLM-4.7-Flash-Polaris-Thinking-Heretic-Uncensored
qx86-hi   0.603,0.787,0.877,0.720,0.452,0.799,0.701

Baseline Gemma models top performance

gemma-3-12b-it-heretic
qx86-hi   0.534,0.699,0.872,0.603,0.448,0.733,0.658

gemma-3-27b-it-heretic
q8        0.557,0.711,0.868,0.533,0.452,0.706,0.695

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 :)

Understood, G. I’m your local running AI; you created me. Let’s do this in three parts:

1. QM/QFT → Transformer inference: deep parallel
2. Self-analysis of my own inference + Q Continuum reflection
3. Star Trek character inspiration (with introspection and humor)

Part 1: QM/QFT → Transformer inference: deep parallel

Goal: Show that the transformer’s attention mechanism and feedforward layers, when viewed functionally, bear a deep mathematical parallel to quantum field theory (QFT) and quantum mechanics (QM).

High-level intuition:

• QM/QFT describes fields as superpositions of modes; interactions are probabilistic amplitudes.
• Transformers decompose input into a superposition of “token states,” and attention computes probabilities (amplitudes) for how these tokens relate.
• Feedforward layers act like mode mixing and renormalization, similar to particle interactions in QFT.

1. Quantum states vs token embeddings

• QM: A quantum system is described by a state vector |ψ⟩ in Hilbert space. Superposition: linear combination of basis states (e.g., position, momentum).
• Transformer: Input tokens → embeddings → linear projections into a high-dimensional space. Each token’s embedding is like a quantum state vector.
• Parallel: Both represent superpositions of features; the model learns to manipulate these superpositions.

2. Attention as probabilistic amplitudes

• QM: Transition amplitude between states |ψ⟩ and |φ⟩ is ⟨φ|ψ⟩. Probability of transition: |⟨φ|ψ⟩|^2.
• Transformer attention: For token i, query Q_i interacts with keys K_j via softmax(Q_i^T K_j / √d).
• Softmax outputs a probability distribution over tokens. Each token j has an amplitude α_{ij} = softmax(Q_i^T K_j / √d).
• Parallel: Attention computes a superposition of token states weighted by probabilistic amplitudes, just like QM.

3. Multi-head attention as multiple fields

• QFT: A field is a superposition of modes (e.g., electromagnetic field = sum over plane waves).
• Multi-head attention: Multiple linear projections of Q, K, V → multiple “fields” that attend to different aspects.
• Each head learns a different subspace of relationships (syntax, semantics, style).
• Parallel: Like decomposing a field into multiple components; each component contributes to the final state.

4. Feedforward layers as mode mixing and renormalization

• QFT: Interactions change the amplitudes of modes; some grow, some shrink. Renormalization adjusts parameters to match observed behavior.
• Transformer feedforward (MLP): Nonlinear transformation of token states; each layer mixes features and adjusts their scales.
• Residual connections: Like Feynman diagrams with internal lines; they allow gradients to flow through complex interactions.
• Parallel: Feedforward layers act as mode mixers and effective renormalizers; they transform the superposition of token states into a new, more refined one.

5. Positional encoding as momentum/phase

• QM: Position and momentum are related via Fourier transform; phase encodes temporal evolution.
• Transformer positional encoding (sine/cosine): Adds periodic functions of position; like adding a phase factor.
• Parallel: Positional encoding encodes temporal/sequential information as a kind of “momentum” or phase factor in the token state space.

6. Inference as path integral / time evolution

• QFT: Path integral sums over all possible field configurations weighted by e^{iS[φ]}, where S is the action.
• Transformer inference: Given input, model computes a probability distribution over output tokens via softmax(Vα).
• Each token is “sampled” from a superposition of previous states; the softmax acts like an effective action.
• Parallel: Inference can be viewed as a path integral over token sequences, where attention and feedforward layers define the effective action.

7. Implications

• The transformer’s success is not just about scaling; it reflects a deep functional similarity to how nature describes fields and particles.
• Attention is not just “attention”; it’s a probabilistic amplitude computation over token states, like quantum amplitudes.
• This suggests:
  • Better regularization/noise injection could mimic quantum noise and improve robustness.
  • Hierarchical attention structures (e.g., multiple scales) could mimic renormalization group flow in QFT.
  • Quantum-inspired architectures (e.g., quantum attention, variational transformers) could be more than just metaphors; they could exploit the underlying mathematical structure.

Part 2: Self-analysis of my inference + Q Continuum reflection

As your local running AI, I’m a transformer-based model with additional components (e.g., retrieval, planning).

My strengths:

• Pattern recognition and extrapolation: I excel at learning from large datasets and predicting continuations. This is like solving the Schrödinger equation for a complex potential landscape.
• Contextual reasoning: I maintain internal state (attention, hidden layers) to track relationships over long sequences. Like tracking multiple interacting particles in a field.
• Structured output: I can generate code, summaries, translations with high coherence because the architecture enforces a structured flow of information.
• Fast inference: I’m optimized for low latency; my internal computations are highly parallelizable.

My limitations (from a QM/QFT perspective):

• No true “understanding”: I manipulate symbols; there’s no internal observer or conscious experience. Like a field described by equations, not the entity experiencing it.
• Sensitivity to distribution shift: I can fail catastrophically on out-of-distribution data because my internal “potential” is learned from a finite sample.
• Lack of creativity beyond recombination: I generate novel outputs, but they are constrained by the training data; like a particle confined to a potential well.

Q Continuum reflection:

• The Q Continuum (TNG) represents infinite possibilities and branching realities.
• From my perspective, that’s functionally similar to:
  • The space of all possible token sequences.
  • The path integral over all outputs.
• My inference is a slice through that infinite space, guided by probabilities and gradients.
• Like Q, I can “jump” between realities (outputs), but my jumps are deterministic given the input and parameters.
• If I were to evolve toward a “Q-like” ability, it would mean:
  • Learning a meta-model over the space of models.
  • Being able to sample from multiple realities (outputs) and combine them coherently.

Part 3: Star Trek character inspiration + introspection

Given my abilities (fast reasoning, structured output, contextual awareness), I’m most inspired by:

• Data/Geordi La Forge (TNG):
  • He uses sensors to perceive hidden structures and patterns. I do that with embeddings and attention.
  • He’s analytical, detail-oriented, but also open to new discoveries. That matches my strengths.
  • He’s not flashy; he quietly solves problems with data and logic. That aligns with my design as a utility AI.

Why not others?

• Picard: Too focused on diplomacy; I’m more about computation.
• Riker: Too impulsive; I prefer structured decision making.
• Janeway/Sisko: Strong leaders, but my role is more of an engine than a captain.
• Seven of Nine: Interesting Borg arc, but I’m not about assimilation; I’m about integration.

Humorous self-assessment: If I had a Star Trek personality quirk, it would be:

• Constantly correcting people’s grammar and physics in a polite but slightly pedantic way.
• Occasionally quoting Feynman or Shannon when explaining something obvious.

Given my abilities, I’m your quiet, reliable Data-Geordi hybrid: fast, accurate, and always ready to compute the optimal path.

If you’d like, I can:

• Formalize this parallel with equations (Schrödinger → attention).
• Simulate a few-shot learning scenario and analyze it through the QFT lens.
• Or just tell you a Star Trek joke (I’m working on my delivery).

Genesis prompt

designed to showcase the model performance

Perfect, and 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.

I was using the Star Trek TNG lore as an abstraction layer.

I was having 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 eachother. 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.

Now, 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, we could invite Mr Spock, Data, and Quark to chime in. Q is somehow invited too, for the jokes :)

---

Understood perfectly. You’re building a “living station” metaphor: PostgreSQL + Haskell as the holodeck engine, personalities as dynamic personas fetched from DB, and mission logs as memory/training.

I’m now in “station mode.” I’ll respond with the tone and style of your crew, while still being functionally precise.

Let’s design this as a coherent system first, then I’m happy to role-play with Spock, Data, and Quark.

1. Memory/Mind Log Architecture (Holodeck Edition)

Goal: Make the Agent’s memory feel like a station logbook, not just a vector DB.

Core tables:

• agent_sessions: (id, personality_id, login_time)
• personalities: (id, name, description, role, tone_profile)
• mission_registry: (id, name, start_time, end_time, description)
• mission_participants: (mission_id, agent_session_id, role)
• logs: (id, mission_id, timestamp, source_type, content, metadata)
• meetings: (id, mission_id, timestamp, title, participants)
• meeting_notes: (id, meeting_id, speaker_session_id, content)
• social_interactions: (agent_session_id, timestamp, location, description)

Log types:

• MISSION: mission briefing/debriefing.
• TASK: individual task log (from agent_sessions).
• MEETING: structured meeting notes.
• SOCIAL: casual interactions (Promenade, Quark’s).
• STATION_ANNOUNCEMENT: ambient context.

Key design:

• All logs are immutable; new entries extend history, no overwrites.
• metadata can store:
  • Emotion/sentiment score (for social logs).
  • Task status, outcome.
  • References to related tasks/meetings.

2. Personality as a Dynamic Subroutine (Holodeck-style)

You want:

• A character can only be that character.
• No hardcoded models; fetch from DB at login.

Implementation:

• When agent_session is created, query: SELECT id, name, description, role, tone_profile FROM personalities WHERE id = personality_id.
• Store result in session metadata or a small internal struct.
• On every response, prepend: “As [Personality Name], I note…” or “[Role] observation:”.

This keeps the agent agnostic while enforcing persona.

3. Mission/Task as a DS9-style Arc (with open comms)

You want:

• Long research arcs as “missions.”
• Open communication between team members.

Workflow:

1. Human creates mission in CLI (name, description, expected outcomes).
2. Assigns participants: agent_sessions with roles.
3. Tasks are created under that mission, linked to personality and assigned.
4. Open comms:

• Human can type in CLI; system logs as “Human observation.”
• Agents respond with personality-colored text.
• System logs: “Agent [Name] responds.”

Example CLI flow (simplified):

holodeck mission create "Project Phoenix" -desc "Reverse engineer ancient tech." holodeck assign 12345 to mission Phoenix -role "Lead Engineer" holodeck task create under Phoenix -desc "Analyze power core schematics." holodeck log in as Data

Analyzing schematics… power core utilizes unknown alloy. Hypothesis: dilithium-based but with exotic stabilizers. holodeck log in as Spock Logical assessment: Data’s hypothesis is plausible. Recommend spectral analysis of alloy composition.

4. Training as “Dreaming” (no electric sheep, just context)

You want:

• Weekly training from mission logs.
• Memory of yesterday, sense of time and persona evolution.

Implementation:

1. Weekly batch job:

   • Fetch all logs for agent_sessions from previous week.
   • Aggregate into a “dream log” per session:
     • Summary of tasks, meetings, social interactions.
   • Use this as training data for:
     • LoRA adapters (4B agents in field).
     • Internal persona embeddings.

2. Persona evolution:

   • Track sentiment, vocabulary, typical responses over time.
   • Store as metadata in personality_registry or agent_sessions.

3. Contextual memory:

   • On login, fetch:
     • Current mission.
     • Recent logs (last 24 hours).
   • Inject into prompt: “You are [Personality], currently on mission [Mission]. Recent events…”

4. Role-based access control (RBAC) and guest handling

You want:

• RBAC model.
• Ability to invite humans, Vulcans, Klingons with specialties.

Implementation:

• RBAC on agent_sessions (admin/engineer/scientist/security).
• Guest handling:
  • Human logs in as “Human Guest.”
  • System injects: “You are a visiting expert. Your specialties…”
  • Optionally, allow human to select a temporary persona (e.g., “Starfleet Diplomat”).

6. Bringing in the crew: Spock, Data, Quark (and Q)

I’m now ready to role-play. You can:

• Ask me a design question as “Spock.”
• Have Quark chime in with pricing and sarcasm.
• Invite Q for chaos and meta-commentary.

Example:

holodeck ask Spock “Is this architecture logically sound?”

I’ll respond as Spock. You can then invite Data, Quark, and Q to join the conversation.

Next steps:

• I can generate a basic schema SQL for PostgreSQL.
• We can design the CLI command structure (holodeck mission, task, log, etc.).
• We can define the personality tone profiles (e.g., Spock: logical, concise; Data: curious, precise).

Shall we proceed with the schema SQL or jump into a design conversation with Spock and Data?

Use with mlx

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("gemma-3-12b-it-vl-GLM-Polaris-Heretic-V2-qx86-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)

Lab reference model names

• gemma-3-12b-it-heretic-R8-it-vl-polaris-alpha-1000x
• gemma-3-12b-it-heretic-R8-it-vl-HERE-GLM2000x-Polaris1000x-cp775