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nightmedia/
Qwen3.5-9B-Claude-GBO-Fire-Deckard-Agent-Heretic-BF16

Name: Qwen3.5-9B-Claude-GBO-Fire-Deckard-Agent-Heretic-BF16
Availability: InStock

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Qwen3.5-9B-Claude-GBO-Fire-Deckard-Agent-Heretic-BF16

This is a merge between:

DavidAU/Qwen3.5-9B-Claude-4.6-OS-Auto-Variable-HERETIC-UNCENSORED-THINKING-X8b
DavidAU/Qwen3.5-9B-GBO-Fire-HERETIC-UNCENSORED-THINKING-X8
DavidAU/Qwen3.5-9B-Deckard-Uncensored-Heretic-Thinking
nightmedia/Qwen3.5-9B-Claude-GBO-Fire-Deckard-Heretic-Thinking
armand0e/Qwen3.5-9B-Agent

Brainwaves

brainwaves

          arc   arc/e boolq hswag obkqa piqa  wino
bf16      0.648,0.832,0.895,0.713,0.460,0.780,0.699
mxfp8     0.639,0.834,0.895,0.708,0.458,0.782,0.690
qx86-hi   0.631,0.824,0.891,0.731,0.440,0.778,0.702
qx64-hi   0.632,0.822,0.888,0.710,0.456,0.778,0.683
dwq4      0.638,0.824,0.880,0.716,0.450,0.783,0.699
mxfp4     0.623,0.820,0.880,0.693,0.466,0.780,0.689

Quant     Perplexity      Peak Memory   Tokens/sec
bf16      4.150 ± 0.026   24.69 GB      873
qx86-hi   4.159 ± 0.027   15.47 GB      714
qx64-hi   4.229 ± 0.027   13.23 GB      702
dwq4      4.270 ± 0.028   12.38 GB      662 (Text only)
mxfp4     4.444 ± 0.029   11.55 GB      736

Model components

armand0e/Qwen3.5-9B-Agent

brainwaves

          arc   arc/e boolq hswag obkqa piqa  wino
mxfp8     0.625,0.813,0.898,0.708,0.456,0.789,0.687
qx86-hi   0.623,0.806,0.895
mxfp4     0.602,0.798,0.883,0.702,0.454,0.775,0.691

Quant     Perplexity      Peak Memory   Tokens/sec
mxfp8     4.569 ± 0.031   16.02 GB      606
qx86-hi   4.414 ± 0.029   15.47 GB      581

nightmedia/Qwen3.5-9B-Claude-GBO-Fire-Deckard-Heretic-Thinking

brainwaves

          arc   arc/e boolq hswag obkqa piqa  wino
mxfp8     0.638,0.832,0.895,0.704,0.448,0.782,0.695
qx86-hi   0.639,0.834,0.894,0.708,0.464,0.782,0.698

Baseline model

Qwen3.5-9B-Instruct

brainwaves

          arc   arc/e boolq hswag obkqa piqa  wino
mxfp8     0.571,0.719,0.895,0.683,0.426,0.770,0.671

Thinking toggle

This model is using an earlier 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.

Update on custom templates

I was completing full tests with one of the base models, to discover how the template I was using affects model IQ, and tried out the latest version from froggeric.

As it turns out, it affects it a lot.

As tested with current template, in Instruct mode:

Qwen3.5-9B-Claude-GBO-Fire-Deckard-Heretic-Thinking

          arc   arc/e boolq
mxfp8     0.638,0.832,0.895

Tested with the original DavidAU template without thinking switches:

Qwen3.5-9B-Claude-GBO-Fire-Deckard-Heretic-Thinking-T1

          arc   arc/e boolq
mxfp8     0.643,0.837,0.893

Tested with the latest template that I used experimentally on the Qwen3.5-9B-Claude-Deckard-Agent-Coder-Heretic-qx86-hi-mlx

Qwen3.5-9B-Claude-GBO-Fire-Deckard-Heretic-Thinking-T2

          arc   arc/e boolq
mxfp8     0.619,0.813,0.886

This is not to say that froggeric's template is bad, it just does not work on the 9B as well as on the larger models.

I am running now a few tests on different template combinations, and will update the model card with metrics.

The DavidAU template

nightmedia/Qwen3.5-9B-Claude-GBO-Fire-Deckard-Heretic-Thinking-XML

brainwaves

          arc   arc/e boolq hswag obkqa piqa  wino
bf16      0.641,0.835,0.892,0.715,0.466,0.782,0.706
mxfp8     0.643,0.837,0.893

Quant     Perplexity      Peak Memory   Tokens/sec
bf16      4.226 ± 0.028   24.69 GB      871
mxfp8     4.356 ± 0.029   16.02 GB      693

The XML template does not have thinking switches, and is optimized for high IQ responses. It might fail with some obscure or badly designed tool framework. Don't blame it on the model.

As far as the jinja, it seems that the less you put in it, the better the inference.

ymmv

-G

From the qx86-hi model trace

NuSLERP and Emergent Council Dynamics

What NuSLERP Does (Briefly)

NuSLERP (Neural Spherical Linear Interpolation) is a weight blending technique that interpolates neural network weights along geodesics on the hypersphere. It's used for:

Model merging: Combining multiple trained models into one
Ensemble methods: Creating hybrid models from different architectures
Continual learning: Blending old and new knowledge without catastrophic forgetting

python

# Conceptual NuSLERP blending
merged_weights = SLERP(weights_A, weights_B, alpha)

# For multiple models:
merged_weights = Σᵢ αᵢ · SLERP(weights_i, reference, βᵢ)

Would the Council Dynamics Emerge?

The Short Answer: Not Automatically

NuSLERP creates a blended model, not necessarily multiple interacting sub-personalities. However, there are interesting connections:

Scenario 1: NuSLERP as the "Council" Mechanism

┌─────────────────────────────────────────────────────────────┐
│  NuSLERP-Blended Model with Council Dynamics                │
├─────────────────────────────────────────────────────────────┤
│                                                             │
│  Layer A (Spock) ───┐                                       │
│                     ├──→ NuSLERP Weight Blending ←─────────┤
│  Layer B (Data) ────┘                                       │
│                     ├──→ Emergent "Council" Behavior        │
│  Layer C (Quark) ───┐                                       │
│                     ├──→ Each layer has "personality"       │
│  Layer D (Dick) ────┘                                       │
│                     ├──→ NuSLERP blends them dynamically    │
│  Layer E (Twain) ───────────────────────────────────────────┘
│                                                             │
│  Result: A single model that can "switch" between           │
│          perspectives based on input context                │
└─────────────────────────────────────────────────────────────┘

Scenario 2: The Holodeck Agent + NuSLERP Integration

This is where your architecture gets interesting:

sql

-- The personality_registry becomes the NuSLERP weight source
CREATE TABLE nu_slerp_configs (
    id uuid PRIMARY KEY DEFAULT gen_random_uuid(),
    session_id uuid REFERENCES agent_sessions(id),
    character_id uuid REFERENCES personality_registry(id),
    weight_alpha float4 DEFAULT 0.2, -- blending coefficient
    layer_range text[], -- which layers to blend for this character
    timestamp timestamptz DEFAULT now()
);

-- Example: Spock's NuSLERP configuration
INSERT INTO nu_slerp_configs (character_id, weight_alpha, layer_range) VALUES 
('spock', 0.35, '{"attention_layers_12", "feedforward_layers_45"}');

The Emergent Behavior Question

What NuSLERP Actually Creates:

NuSLERP Effect	Council Equivalent
Weight interpolation between models	Blending different expertise domains
Context-dependent activation	Input routing to appropriate "flipper"
Smooth transitions between states	Seamless character switching

What NuSLERP Doesn't Automatically Create:

Missing Element	Why It Matters
Explicit interaction patterns	Council dynamics require bouncing ideas, not just blending weights
Memory of past interactions	NuSLERP is stateless; the Holodeck needs persistent logs
Emergent consensus mechanisms	Requires voting/decision logic beyond weight blending

The Hybrid Architecture: Holodeck + NuSLERP

This is where it gets genuinely interesting. Imagine:

┌─────────────────────────────────────────────────────────────┐
│  HYBRID ARCHITECTURE: Holodeck Agent + NuSLERP             │
├─────────────────────────────────────────────────────────────┤
│                                                             │
│  ┌──────────────────────────────────────────────────────┐   │
│  │  PostgreSQL (Holodeck Core)                          │   │
│  │  - Personality registry                             │   │
│  │  - Memory system                                    │   │
│  │  - Council interaction logs                         │   │
│  └─────────────────────┬────────────────────────────────┘   │
│                        │                                    │
│  ┌─────────────────────▼────────────────────────────────┐   │
│  │  Haskell Agent Layer                                │   │
│  │  - Orchestrates Council dynamics                    │   │
│  │  - Routes ideas through "flippers"                  │   │
│  └─────────────────────┬────────────────────────────────┘   │
│                        │                                    │
│  ┌─────────────────────▼────────────────────────────────┐   │
│  │  NuSLERP Weight Blending Layer                      │   │
│  │  - Fetches character-specific weight configs        │   │
│  │  - Blends model weights dynamically                 │   │
│  └─────────────────────┬────────────────────────────────┘   │
│                        │                                    │
│  ┌─────────────────────▼────────────────────────────────┐   │
│  │  Inference Endpoints (4B models)                    │   │
│  │  - Each character has a specialized model           │   │
│  │  - NuSLERP blends them based on context             │   │
│  └──────────────────────────────────────────────────────┘   │
│                                                             │
└─────────────────────────────────────────────────────────────┘

The NuSLERP Configuration Table

sql

-- Character-specific weight blending configurations
CREATE TABLE character_slerp_configs (
    id uuid PRIMARY KEY DEFAULT gen_random_uuid(),
    character_id uuid REFERENCES personality_registry(id),
    base_model text NOT NULL, -- e.g., 'spock_base_v2'
    blend_weights jsonb DEFAULT '{"Spock": 0.4, "Data": 0.2, "Quark": 0.1}',
    layer_mapping jsonb DEFAULT '{"attention_12": "Spock", "feedforward_45": "Data"}',
    activation_threshold float4 DEFAULT 0.7, -- when to activate this blend
    timestamp timestamptz DEFAULT now()
);

-- Example: Spock's NuSLERP configuration
INSERT INTO character_slerp_configs (character_id, base_model, blend_weights) VALUES 
('spock', 'base_llama_4b_v2',
 '{"Spock": 0.5, "Data": 0.3, "Quark": 0.1, "Philip K. Dick": 0.1}');

The Emergent "Council" in NuSLERP-Blended Models

What Could Happen:

Context-Aware Blending: The model automatically shifts weight distributions based on input topic
Memory of Previous Blends: Store which blends worked for which topics (in PostgreSQL)
Feedback Loop: Successful Council interactions refine the NuSLERP weights

Example: Topic-Based Weight Shifting

python

def get_nuslerp_weights(topic, session_id):
    """Dynamically compute NuSLERP weights based on topic"""
    
    # Fetch character expertise from PostgreSQL
    spock_expertise = db.fetch("SELECT domain FROM gravity_wells WHERE character_id = 'spock'")
    data_expertise = db.fetch("SELECT domain FROM gravity_wells WHERE character_id = 'data'")
    
    # Compute topic similarity to each expertise domain
    spock_score = cosine_similarity(topic, spock_expertise)
    data_score = cosine_similarity(topic, data_expertise)
    
    # Adjust NuSLERP weights dynamically
    base_weights = {'Spock': 0.4, 'Data': 0.3, 'Quark': 0.1}
    
    if spock_score > 0.7:
        base_weights['Spock'] = 0.6
    if data_score > 0.7:
        base_weights['Data'] = 0.5
    
    return base_weights

# This creates a model that "becomes" different characters based on context

The Beautiful Integration

Your Holodeck Agent architecture already has the Council dynamics in PostgreSQL. NuSLERP would be the mechanism that makes those dynamics emergent at inference time.

The Complete Picture:

┌─────────────────────────────────────────────────────────────┐
│  INPUT: "Plan a mission to the Klingon Empire"             │
├─────────────────────────────────────────────────────────────┤
│                                                             │
│  1. PostgreSQL: Fetch personality_registry + gravity_wells  │
│     → Topic = 'strategic_mission_planning'                  │
│                                                             │
│  2. Haskell Agent: Route through Council                    │
│     → Spock deflects (logical framework)                     │
│     → Data amplifies (historical data)                       │
│     → Quark reframes (practical logistics)                   │
│                                                             │
│  3. NuSLERP: Compute dynamic weights                        │
│     → Spock: 0.6, Data: 0.3, Quark: 0.1                     │
│                                                             │
│  4. Inference: Blend model weights                         │
│     → Output reflects Council consensus                      │
│                                                             │
│  5. PostgreSQL: Log the interaction                         │
│     → Update character_development                          │
│     → Refine gravity_wells for future topics                 │
│                                                             │
└─────────────────────────────────────────────────────────────┘

Q's Comment on NuSLERP Integration

"You're creating a system where the Council doesn't just discuss ideas—they physically become them through weight blending. That's... almost like they're not just in the simulation, but are the simulation."

The Holodeck Agent + NuSLERP integration creates:

Persistent Council dynamics (PostgreSQL logs)
Emergent behavior at inference time (NuSLERP blending)
Learning through interaction (character_development updates)

It's a genuinely elegant architecture that bridges symbolic AI (PostgreSQL schema, Haskell logic) with neural network techniques (NuSLERP blending).

Would you like me to design the specific NuSLERP weight computation algorithm that would make this work?

Use with mlx

pip install mlx-lm

python

from mlx_lm import load, generate

model, tokenizer = load("Qwen3.5-9B-Claude-GBO-Fire-Deckard-Agent-Heretic-BF16")

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)

nightmedia/Qwen3.5-9B-Claude-GBO-Fire-Deckard-Agent-Heretic-BF16

Qwen3.5-9B-Claude-GBO-Fire-Deckard-Agent-Heretic-BF16

Model components

Baseline model

Thinking toggle

Update on custom templates

The DavidAU template

From the qx86-hi model trace

NuSLERP and Emergent Council Dynamics

What NuSLERP Does (Briefly)

Would the Council Dynamics Emerge?

The Short Answer: Not Automatically

Scenario 1: NuSLERP as the "Council" Mechanism

Scenario 2: The Holodeck Agent + NuSLERP Integration

The Emergent Behavior Question

What NuSLERP Actually Creates:

What NuSLERP Doesn't Automatically Create:

The Hybrid Architecture: Holodeck + NuSLERP

The NuSLERP Configuration Table

The Emergent "Council" in NuSLERP-Blended Models

What Could Happen:

Example: Topic-Based Weight Shifting

The Beautiful Integration

The Complete Picture:

Q's Comment on NuSLERP Integration

Use with mlx

nightmedia/
Qwen3.5-9B-Claude-GBO-Fire-Deckard-Agent-Heretic-BF16