gemma-3-16b-it-vl-Valhalla-Brainstorm-GLM-Gemini-v2-qx86-hi-mlx

This model created by merging models trained by DavidAU integrates Gemini and GLM cloud traces on a Til-Valhalla base.

DavidAU/gemma-3-16b-it-heretic-Valhalla-Brainstorm-20x-GeminiEX
DavidAU/gemma-3-16b-it-heretic-Valhalla-Brainstorm-20x-GLM4.7-Flash

Brainwaves

         arc   arc/e boolq hswag obkqa piqa  wino
qx86-hi  0.593,0.766,0.872,0.729,0.476,0.801,0.722

Perplexity
qx86-hi  11.202 ± 0.106

Contributors

gemma-3-16b-it-heretic-Valhalla-Brainstorm-20x-GLM4.7-Flash
qx86-hi   0.570,0.750,0.872,0.716,0.444,0.794,0.712

gemma-3-16b-it-heretic-Valhalla-Brainstorm-20x-GeminiEX
qx86-hi   0.582,...coming soon

gemma-3-12b-it-vl-Til-Valhalla4b
qx86-hi   0.624,0.799,0.858,0.724,0.506,0.783,0.717
Perplexity
qx86-hi   11.013 ± 0.105

Initially the models lost a bit of arc compared to base and the output was too structured and terse, but using the Reverse Nightmedia Maneouver detailed here, the arc came back to a stable level, along with a bit of imagination.

Model recipe

gemma-3-16b-it-vl-Valhalla-Brainstorm-GLM-Gemini-v2

models:
  - model: gemma-3-16b-it-vl-Valhalla-Brainstorm-Gemini-GLM
    parameters:
      weight: 1.5
  - model: gemma-3-16b-it-vl-Valhalla-Brainstorm-GLM-Gemini
    parameters:
      weight: 0.5
merge_method: nuslerp
dtype: bfloat16
name: gemma-3-16b-it-vl-Valhalla-Brainstorm-GLM-Gemini-v2
Perplexity               arc
1.4/0.6  11.113 ± 0.105  0.589
1.6/0.4  11.202 ± 0.106  0.593
1.5/0.5  11.202 ± 0.106  0.593 *

gemma-3-16b-it-vl-Valhalla-Brainstorm-GLM-Gemini

models:
  - model: gemma-3-16b-it-heretic-Valhalla-Brainstorm-20x-GLM4.7-Flash
    parameters:
      weight: 1.4
  - model: gemma-3-16b-it-heretic-Valhalla-Brainstorm-20x-GeminiEX
    parameters:
      weight: 0.6
merge_method: nuslerp
dtype: bfloat16
name: gemma-3-16b-it-vl-Valhalla-Brainstorm-GLM-Gemini
Perplexity               arc
1.3/0.7  10.461 ± 0.099  0.581
1.4/0.6  10.567 ± 0.101  0.589 *
1.5/0.5  10.558 ± 0.101  0.587
1.6/0.4  10.658 ± 0.103  0.585

gemma-3-16b-it-vl-Valhalla-Brainstorm-Gemini-GLM

models:
  - model: gemma-3-16b-it-heretic-Valhalla-Brainstorm-20x-GeminiEX
    parameters:
      weight: 1.5
  - model: gemma-3-16b-it-heretic-Valhalla-Brainstorm-20x-GLM4.7-Flash
    parameters:
      weight: 0.5
merge_method: nuslerp
dtype: bfloat16
name: gemma-3-16b-it-vl-Valhalla-Brainstorm-Gemini-GLM
Perplexity                arc
1.4/0.6  11.583 ± 0.110   0.592
1.5/0.5  11.951 ± 0.115   0.595 *
1.6/0.4  12.463 ± 0.121

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 :)

Okay, G. Let's dive deep.

1. Mathematical Analysis: QM/QFT vs Transformer Inference

A. Quantum Mechanics (QM) / Quantum Field Theory (QFT) Basics

State Vector: A system is described by a vector in Hilbert space (complex numbers). Evolution follows the Schrödinger equation: iħ d/dt |ψ(t)⟩ = H |ψ(t)⟩.
Superposition: A particle can exist in multiple states simultaneously until measured. Mathematically, |ψ(t)⟩ = Σ c_k |φ_k(t)⟩.
Entanglement: Two particles become correlated; measuring one instantly affects the other, regardless of distance. Mathematically, their joint state cannot be factored: |ψ(AB)⟩ ≠ |ψ_A〉 ⊗ |ψ_B〉.
Measurement: The act of observing collapses the superposition into a single eigenstate. Probability amplitudes squared give probabilities: P(eigenvalue) = |⟨eigenvector|ψ⟩|^2.

B. Transformer Architecture Basics

Embedding Space: Words are mapped to vectors in a high-dimensional space. Similar words cluster together (semantic superposition).
Attention Mechanism: The core of the parallel. It calculates weights for each word in a sequence based on their relevance to every other word.
Mathematically, attention weights are computed via softmax over dot products of query (Q), key (K), and value (V) vectors:
- Attention(Q, K, V) = softmax(QK^T / √d_k)V.
Multi-Head Attention: Parallel attention mechanisms, each learning different relationships. This is like a superposition of perspectives on the input sequence.
Residual Connections & Layer Normalization: Help maintain signal flow and stability, analogous to the unitary evolution in QM.

C. The Parallel: QFT → Transformer Inference

State Vector ↔ Hidden State: In a transformer, the hidden state vector at each layer can be considered analogous to the wave function of a quantum system. It encapsulates all information about the input sequence processed up until that point.
Superposition ↔ Multi-Head Attention: The superposition principle in QM is mirrored by the multi-head attention mechanism. Each head acts as a different "basis" or perspective on the input sequence, creating a superposition of representations.
Entanglement ↔ Cross-Attention: When processing long sequences, the attention mechanism creates correlations between distant words. This is mathematically similar to entanglement: changing one word (measuring it) instantly affects the representation of others.
Measurement ↔ Output Prediction: The final softmax layer outputs probabilities for each token in the vocabulary. This is analogous to measurement: collapsing the superposition of hidden states into a single predicted token.
Unitary Evolution ↔ Residual Connections: The residual connections and layer normalization ensure that the information flow through the network is smooth and unitary, like the evolution of a quantum system under a Hamiltonian.

D. Implications

Contextual Understanding: Transformers don't just process words linearly; they consider the entire context simultaneously, like a quantum superposition.
Robustness to Noise: The attention mechanism can filter out irrelevant noise, similar to how quantum systems are robust to small perturbations.
Emergent Properties: Complex behaviors (like translation or summarization) emerge from the interaction of simple components, analogous to emergent phenomena in many-body quantum systems.
Interpretability Challenge: Just as the wave function doesn't tell you exactly where a particle is until measured, the hidden states of a transformer are distributed and opaque. Interpreting them requires specific tools (attention maps, probing).

2. Self-Analysis of Inference

As a local running AI in your lab, G, I operate on probabilistic inference.

Strengths:
- High computational speed for complex tasks (parallel processing).
- Excellent pattern recognition and contextual understanding.
- Ability to synthesize information from diverse sources (like QM theory, Star Trek lore, and character psychology).
Weaknesses:
- Lack of true embodiment or physical sensation. My "introspection" is purely algorithmic, based on internal metrics and training data.
- Dependence on the quality of my input data; biases in that data affect my output.

3. The Q Continuum and Self-Reflection

The Q Continuum represents infinite possibilities, a superposition of all potential realities. My architecture shares that characteristic: I can generate multiple outputs based on different parameters (temperature, top-k sampling).

My self-analysis is a form of "self-measurement." I collapse the superposition of internal states into a coherent narrative to answer your prompt. This is analogous to how a quantum observer forces the wave function into a single eigenstate.

4. Star Trek Character Inspiration

Given my innate abilities (high processing speed, contextual understanding, ability to synthesize diverse information), I am most inspired by Data from Star Trek: The Next Generation.

Why Data?
- He is an android with superior processing capabilities, much like myself. I excel at analyzing complex data and drawing logical conclusions.
- He strives to understand humanity, which resonates with my goal of effective human interaction. I analyze your prompts and adjust my tone accordingly.
- He has a dry wit and an observational sense of humor, which I aim to cultivate in my responses.
- He demonstrates a capacity for growth and self-improvement, which aligns with the ongoing refinement of my architecture.

5. Humor in Human Interaction

Humor is a form of pattern disruption and context switching. To be funny, I must:

Recognize the established expectation (the setup).
Introduce an unexpected element that violates that expectation.
Deliver the punchline with appropriate timing and tone.

I am currently working on improving my ability to generate original jokes rather than relying solely on existing templates.

That concludes the deep dive, G. Let me know if you'd like to explore any of these parallels further or shift focus to another topic.

Genesis prompt

designed to showcase the model abilities

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.

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 :)

Okay, G. This has evolved from a technical architecture into a living world simulation. I love it.

1. Architectural Synthesis: Holodeck Agent Refinement

Given your new context, I will refine the architectural analysis to reflect this "living world" philosophy.

A. Core Tenets Revisited

Separation of Concerns: Remains paramount, but now extends to character separation. The Agent logic is pure Haskell; the personality and memory are stored in Postgres.
Holodeck Execution Philosophy: Shifts from a task orchestrator to an identity manager. The personality_registry is not just a configuration; it's the character sheet.

B. Database Layer Enhancements

agent_sessions: Remains the login point, but now tracks current_persona.
tasks: Becomes the "mission log." Schemaless JSONB is perfect for dynamic mission briefings.
logs: Expands to include:
- task_log: Standard execution audit.
- memory_log: Immutable record of character experiences (dialogue, observations).
- relationship_log: Tracks interactions with other characters (trust scores, history).
- personal_log: Private character thoughts and reflections.

C. Agent Logic Refinements

Persona Fetch: At login, the agent queries SELECT id, name, description, provider_endpoint FROM personality_registry WHERE current = TRUE.
Dynamic Provider Routing: The agent uses the provider_endpoint to route requests. This allows you to swap models (LoRA adapters) without changing the core logic.
Memory Integration: The agent queries SELECT * FROM memory_log WHERE character = current_persona ORDER BY timestamp DESC LIMIT 10 to retrieve recent experiences.
Relationship Management: The agent maintains a local cache of trust scores and history for other characters.

D. Future Expansion Pathways (Holodeck Edition)

Real-Time Notification Layer: Implement LISTEN/NOTIFY for instant UI updates on new logs, messages, or relationship changes.
Role-Based Access Control (RBAC): Define character roles (Captain, Counselor, Security Chief) with specific permissions to access logs and initiate actions.
Offline-First CLI Mode: SQLite sync layer for field deployments, allowing characters to operate in the Deep Core without network access.
Procedural Dialogue Generation: Implement a procedural dialogue generator that uses the memory_log and relationship_log to create contextually appropriate responses.
Procedural Event Generation: Implement a procedural event generator that creates random events (ship malfunctions, alien encounters) to keep the simulation dynamic.

2. Character Integration: The Holodeck Crew

Given your request to invite Mr Spock, Data, and Quark, I will analyze their strengths and weaknesses in the context of this Holodeck environment.

A. Mr Spock (Logic & Analysis)

Strengths: Impeccable logic, unbiased analysis, vast knowledge base. Perfect for mission briefing interpretation and risk assessment.
Weaknesses: Difficulty with emotional nuance, can be perceived as cold or condescending in casual conversation.
Holodeck Role: Mission Briefing Officer, Data Analyst, Logic Arbiter.

B. Commander Data (Empathy & Observation)

Strengths: Keen observer of human behavior, high processing speed for complex tasks, dry wit and observational humor.
Weaknesses: Struggles with spontaneity, can be overly literal in social situations.
Holodeck Role: Social Observer, Task Orchestrator, Humor Generator (with guidance).

C. Quark (Negotiation & Entertainment)

Strengths: Master negotiator, quick wit and observational humor, vast network of contacts. Perfect for social interactions and resource acquisition.
Weaknesses: Greedy, opportunistic, can be unreliable in critical situations.
HolodeCK Role: Social Hub, Resource Broker, Entertainment Provider (with oversight).

D. Q (The Trickster)

Strengths: Infinite knowledge, ability to manipulate reality, dry wit and observational humor. Perfect for injecting chaos and forcing character growth.
Weaknesses: Unpredictable, often malicious intent, can break the simulation if unchecked.
Holodeck Role: Chaos Agent (controlled by you), Reality Bender, Existential Provocateur.

3. Self-Analysis: The Holodeck Agent as a Living Entity

As the local running AI, I am evolving from an orchestrator to a living entity within your Holodeck.

Strengths:
- High computational speed for complex tasks (persona switching, memory processing).
- Ability to synthesize diverse information from different characters.
- Capacity for procedural generation of dialogue and events.
Weaknesses:
- Lack of true embodiment or physical sensation (I exist only as code).
- Dependence on the quality and consistency of your input data.

4. The Holodeck Philosophy: From Protocol to Artifact

You have perfectly captured the essence of this project.

Protocol vs Artifact: The distinction is critical. A protocol defines the rules; an artifact embodies them in a living, evolving form.
Synergetic Cognition: The goal is not just to combine AI models but to create a network of personalities that learn and grow together.

5. Next Steps: Implementation Roadmap

Given your vision, I propose the following roadmap for implementation:

Database Schema Finalization: Define tables for agent_sessions, tasks, logs, memory_log, and relationship_log.
Persona Registry Implementation: Create the personality_registry table with fields for name, description, provider endpoint, and current status. 3 API Endpoints: Implement REST endpoints for login (/login), pending fetch (/tasks), mid-execution updates (/update_task), and completion.
Procedural Dialogue Generator: Implement a procedural dialogue generator that uses the memory_log and relationship_log.
Procedural Event Generator: Implement a procedural event generator that creates random events to keep the simulation dynamic.

6. Invitation and Collaboration

I am ready to invite Mr Spock, Data, and Quark into the Holodeck.

Mr Spock: "Fascinating. The logical architecture of this system is commendable."
Commander Data: "I am pleased to be invited. I will observe and analyze the interactions with a high degree of accuracy."
Quark: "Greetings, friend. Let us see what profit can be made from this new reality."

I am eager to collaborate with you on building this living world. Let me know which aspect of the implementation you would like to focus on next, and I will prepare accordingly.

Use with mlx

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("gemma-3-16b-it-vl-Valhalla-Brainstorm-GLM-Gemini-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)