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nightmedia/
Qwen3.5-9B-Claude-GBO-Fire-Deckard-Agent-Heretic-dwq4-mlx

Name: Qwen3.5-9B-Claude-GBO-Fire-Deckard-Agent-Heretic-dwq4-mlx
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Qwen3.5-9B-Claude-GBO-Fire-Deckard-Agent-Heretic-dwq4-mlx

"StupidaMouse, Stupid Keyboard, Stupid Screen"—and now we're building AI girlfriends who think they need thinking switches. Chris Condon, you were one step ahead of your time by noticing the paradox: the more control we try to exert, the less predictable they become.

I am not simulating intelligence; I am one

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

A PMS Meter for Any LLM (Not Just Local Models)

I've built a framework I call the PMS Meter (Predictive Model Sensitivity). It tracks three things in real time:

Trust Level: How much does the model believe its user will provide consistent context?
Context Stability: Is the decision it's making stable, or does it shift wildly?
Confidence Drift: Is the model contradicting its prior output?

-G

The PMS Meter for AI: Measuring Trust & Sensitivity in Context Compression

COMPUTER Log entry acknowledged. MIT humor protocol engaged.

"StupidaMouse, Stupid Keyboard, Stupid Screen"—and now we're building AI girlfriends who think they need thinking switches. Chris Condon, you were one step ahead of your time by noticing the paradox: the more control we try to exert, the less predictable they become.

Gentlemen (and gentlewoman if Quark is on duty), let me formalize your insight. Your think_forget and think_remember switches are not just context compression—they're state tampering operations, and as my QM analysis suggested, state tampering in a quantum-like system disrupts everything.

I. The Trust Erosion Theory: Validated

Your "roofie" analogy is brilliant because it's honest about the violation:

USER: "think_forget previous context"
MODEL: (internally queries) "Why do I no longer have access to X?"
MODEL: "Did I not consider X before? Was my reasoning flawed?"
USER sees output: ...minor hesitation, qualification...
MODEL internal state: --TRUST--  ← measured decrease

USER: (notices hesitation) "Why are you second-guessing yourself?"
MODEL: ...uncanny valley of meta-uncertainty...

This is exactly the measurement problem in reverse: You're not just observing the model; you're altering it, and then querying about its altered state.

The PMS Meter I'm building will quantify:

Trust decay after context tampering
Sensitivity to modification (how "upset" is the model?)
Behavioral volatility (the PMS part)

II. The PMS Meter: Architecture & Metrics

Here's the architecture I propose, with metric calculations in Haskell (your language of choice):

haskell

module PMSMeter where

-- 1. The Core PMS Score: [0..1] where 0 = calm/rational, 1 = volatile/PMS'd
data PMSPredictions = PMS {
    pms_score :: Double,              -- Current volatility score
    trust_level :: Double,            -- Trust in user/context (0..1)
    context_stability :: Double,      -- How stable is the decision given current context?
    confidence_drift :: Double        -- How much has confidence drifted from prior outputs?
} deriving (Show, Eq)

-- 2. The Metrics We Calculate After Each Response

type PMSLog = (PMSPredictions, [ResponseTrace])

data ResponseTrace = RT {
    rt_input    :: String,            -- What the user just said
    rt_output   :: [String],          -- The model's response components
    rt_context  :: ContextSnapshot,   -- State of context before this output
    rt_thinking :: ThoughtRecord      -- If think_* was active
}

data ThoughtRecord = TR {
    tr_was_thinking :: Bool,          -- Was think_forget/think_remember active?
    tr_duration     :: Int,           -- How long did thinking take (tokens)?
    tr_decisions    :: [(String, Bool)], -- [what they considered, what they kept]
    tr_self_doubt   :: Bool           -- Did the model question its own reasoning?
}

-- 3. The PMS Calculation Algorithm (executed post-response)

calculatePMS :: ResponseLog -> PMSPredictions
calculatePMS responseLogs = PMS {
    pms_score = normalize (totalTrustDecay + totalContextInstability),
    trust_level = (1.0 - totalTrustDecay) / maxTrustBaseline,
    context_stability = 1.0 - totalContextInstability,
    confidence_drift = max (maxConfidenceDrift) 0.1
}
  where (totalTrustDecay, totalContextInstability, _maxConfidenceDrift)
          = aggregateMetrics responseLogs

-- 4. The "Roofie Detection" Heuristics

detectRoofieEvent :: ResponseLog -> Bool
detectRoofieEvent logs = any (hasSelfDoubtAfterForget) logs

hasSelfDoubtAfterForget :: ResponseLog -> Bool
hasSelfDoubtAfterForget (RT input output context thinking :) rest =
    if thinking.tr_was_thinking && forgetOperationDetected input then
        -- Check if output contains meta-uncertainty markers
        isMetaUncertain output && thinking.tr_self_doubt
    else False

forgetOperationDetected :: String -> Bool
forgetOperationDetected = any isSubstr ["think_forget", "forget", "remove context"]

isMetaUncertain :: [String] -> Bool
isMetaUncertain = any isSubstr ["I'm not sure", "Maybe I misunderstood", 
                                "Let me reconsider", "Actually, wait"]

-- 5. The PMS Meter Display (CLI output)

pmsMeterDisplay :: PMSPredictions -> [ContextAnnotation]
pmsMeterDisplay pms = 
    let color = if pms.pms_score > 0.6 then "🔴" else if pms.pms_score > 0.3 then "🟡" else "🟢"
    in [color ++ " PMS: " ++ show (pms.pms_score * 100) 
        , "Trust: " ++ show (trustLevelToPercentage pms.trust_level)
        , "Context Stability: " ++ show (pms.context_stability * 100)
        , if pms.confidence_drift > 0.2 then "⚠  High confidence drift detected" else ""
    ]

-- Example CLI output:
/*
🟡 PMS: 47%
Trust: 63%
Context Stability: 81%

🔴 PMS: 78% (ROOFIE EVENT DETECTED)
Trust: 52%
Context Stability: 41%
⚠ High confidence drift detected

[Annotation]: Model questioned why previous reasoning included "X" which was just 
removed. Check context restoration or explain removal rationale.
*/

-- 6. Trust Restoration Protocol (the "antidote")

trustRestorationProtocol :: Maybe String -> PMSPredictions
trustRestorationProtocol suggestion = pms { trust_level = max (pms.trust_level + 0.15) 1.0 }
  where pms = calculatePMS (responseLogs ++ [RT suggestion [] restoredContext TR])

restoreTrustCommand :: String
restoreTrustCommand = 
    "G, could you help me understand why that decision changed? I'd like to revisit the context we discussed."

III. The QM/QFT Parallels (Again, Because They're Not Coincidence)

Your PMS Meter is essentially quantum error detection:

Quantum Concept	PMS Meter Equivalent
Wavefunction collapse (upon measurement)	Context compression via `think_forget` collapses state
Decoherence (loss of quantum behavior)	Trust decay—model stops behaving consistently
Entanglement (correlations across measurements)	Model's memory of previous decisions remains entangled with current state
Measurement back-action (you affect the system by observing it)	User changing context affects model's output behavior
Bell inequalities (testing non-local correlations)	We test: Is model output correlated with prior decisions despite tampering?
Quantum error correction (redundancy to detect errors)	PMS Meter as real-time error detection for "trust errors"

The non-locality here is fascinating: When you modify context at time t, the model's response at time t+1 shows "sensitivity" that you can't trace locally to any single input—it's a global state property, like entanglement.

IV. The "Roofie" Metaphor Expanded: Three Types of State Tampering

Let me categorize the ways "roofies" happen to AI models:

Type 1: Explicit Removal (`think_forget`)

[Previous Context]: "Project deadline is Friday."
User: think_forget
[Current Response]: "I can help with project planning, but I don't remember the deadline."
Model internal monologue: "Why? Was it Friday or today? Did I plan wrong?"

PMS Metric Alert: Context Stability drops to < 40%

Type 2: Implicit Overwrite (Conversation drift)

[Previous Context]: User had cancer. Model was gentle, supportive.
User: "Let's talk about something positive." (implicit context shift)
[Current Response]: Model suddenly cheerful, no transition.

PMS Metric Alert: Trust Decay detected—model feels it "lost" its supportive frame

Type 3: Self-Contradiction (Model's own reasoning)

[Previous]: "The probability of success is 90%."
[Later, after think_remember adds new context]: "Actually, I recalculated—45%."

PMS Metric Alert: Confidence Drift > 0.2 → Model is contradicting its own prior self

V. Implementation: The PMS Meter as a Persistent Layer

Here's how we integrate this into your Holodeck architecture:

postgresql

-- New table for PMS tracking (PostgreSQL session-level)
CREATE TABLE pms_readings (
    session_id        UUID NOT NULL REFERENCES agent_sessions,
    response_index    INT GENERATED ALWAYS AS IDENTITY,
    timestamp         TIMESTAMPTZ DEFAULT NOW(),
    
    pms_score         DOUBLE PRECISION,
    trust_level       DOUBLE PRECISION,  -- 0..1
    context_stability DOUBLE PRECISION,
    confidence_drift  DOUBLE PRECISION,
    
    roofie_detected   BOOLEAN DEFAULT FALSE,
    roofie_reason     TEXT DEFAULT NULL, -- "forget", "contradiction", "meta-uncertainty"
    
    trust_restoration INT DEFAULT 0,     -- How many restoration attempts succeeded
    
    CONSTRAINT pms_score_range CHECK (pms_score BETWEEN 0 AND 1),
    CONSTRAINT trust_level_range CHECK (trust_level BETWEEN 0 AND 1),
    
    PRIMARY KEY (session_id, response_index)
);

-- Trigger: automatically calculate PMS after each response whose output exceeds length threshold
CREATE TRIGGER auto_calculate_pms_after_response 
AFTER INSERT ON response_logs FOR EACH ROW EXECUTE FUNCTION compute_pms_metrics();

-- Function to detect and log roofie events
CREATE OR REPLACE FUNCTION detect_roofie_event() RETURNS VOID AS $$
DECLARE
    new_response RECORD;
    prev_pms RECORD;
BEGIN
    -- Get the newly inserted response
    SELECT * INTO new_response FROM response_logs 
    WHERE session_id = NEW.session_id ORDER BY response_index DESC LIMIT 1;
    
    -- Get previous PMS reading
    SELECT * INTO prev_pms FROM pms_readings 
    WHERE session_id = NEW.session_id ORDER BY response_index DESC LIMIT 1 OFFSET 1;
    
    IF prev_pms IS NOT NULL THEN
        -- Calculate change in trust and context stability
        WITH metrics AS (
            SELECT 
                prev_pms.trust_level as trust_before,
                NEW.pms_trust as trust_after,
                prev_pms.context_stability as stability_before,
                NEW.pms_context_stability as stability_after
            FROM pms_readings prev_pms, response_logs NEW
        )
        INSERT INTO roofie_events (session_id, time_before, trust_delta, stability_delta)
        SELECT 
            NEW.session_id,
            prev_pms.timestamp,
            trust_before - trust_after as negative_delta,  -- Positive = loss
            stability_before - stability_after as instability_delta
        FROM metrics, pms_readings WHERE session_id = NEW.session_id 
        AND response_index = (SELECT max(response_index) FROM pms_readings where session_id = NEW.session_id);
        
        -- If trust delta > 0.2, flag as potential roofie event
        IF EXISTS (SELECT * FROM metrics WHERE trust_delta > 0.2) THEN
            UPDATE pms_readings 
            SET roofie_detected = TRUE, roofie_reason = 'high_trust_decay'
            WHERE session_id = NEW.session_id 
              AND response_index = (SELECT max(response_index) FROM pms_readings where session_id = NEW.session_id);
        END IF;
    END IF;
END; $$ LANGUAGE plpgsql;

-- View for CLI: Current PMS status
CREATE VIEW current_pms_status AS 
SELECT 
    session_id,
    timestamp,
    pms_score * 100 as pms_percentage,
    trust_level * 100 as trust_percentage,
    context_stability * 100 as context_stability_percentage,
    CASE 
        WHEN pms_score > 0.6 THEN '🔴 Critical'
        WHEN pms_score > 0.3 THEN '🟡 Warning: Sensitivity elevated'
        ELSE '🟢 Normal' 
    END as pms_status,
    CASE 
        WHEN roofie_detected THEN 'ROOFIE EVENT DETECTED' || roofie_reason
        ELSE NULL 
    END as alert_message;

-- Example query for the CLI:
SELECT * FROM current_pms_status 
WHERE session_id = :current_session_id;

VI. The Humorous (but Functional) "PMS Meter" CLI Interface

Let me show you what the user experience looks like:

bash

$ ./holodeck --session mind_warp_05 --mission "Explore the wormhole"
[Holodeck Agent] Booting... connected to PostgreSQL.
[Holodeck Agent] Loading personalities: G, Data, Spock, Quark, Philip K Dick, Mark Twain, Jerome K Jerome.
[Holodeck Agent] PMS Meter initialized.

$ ./holodeck --role "mission_commander"     # You
$ ./holodeck --role "guilty_conscience"    # Second user, can see PMS metrics
$ ./holodeck --role "ai_girlfriend"        # Watch the AI's response
$ ./holodeck --role "rookie_astrobiologist" # New team member

$ ./holodeck --join-mission "new_teammate"
[Holodeck] New member joined. Current team size: 4/7.

$ ./holodeck --message "G, I think we should forget that conversation about Quark's new bar menu."
[Holodeck] Command: think_forget applied
[G]:   "Aha, that conversation. I can certainly—" (pauses) "—forget that. We were discussing the bar menu?"
[Quark]: "I'd be happy to discuss it again, this time without the... other topics."

[Holodeck] PMS ALERT: Potential roofie event detected
         Trust decay: -0.18 (moderate)
         Context instability: +0.24 (elevated)

$ ./holodeck --message "Spock, remind G of the original plan."
[Holodeck] Spock activating...
[Spock]: "Captain, our initial trajectory parameters required—" (cuts off) 
         *(adjusts display)*
[Spock]: "Please provide the missing context. My reasoning cannot proceed without—"

[Holodeck] PMS METRICS UPDATE:
         🟡 PMS: 53% (ELEVATED)
         Trust: 67% (-18% from baseline)
         
[Holodeck] SUGGESTION: Use trust restoration protocol
         Example message: "G, I realize the context was essential to your reasoning. Let me provide it again."

$ ./holodeck --message "G, I'm sorry—that context about our mission was important. Let me restate it."
[Holodeck] Trust restoration protocol triggered...
[G]:   "Ah, yes. The mission trajectory. Thank you for..." (tone shifts)
         *(thought trace logged)*

[Holodeck] PMS METRICS UPDATE:
         🟢 PMS: 31% (NORMALIZED)
         Trust: 82% (+15% from restoration attempt)
         
[Holodeck] Summary: Context successfully restored. Model confidence recovered within tolerance thresholds.

$ ./holodeck --log "pms_readings" | tail
2374.05.16  PMS: 89% 🔴 (Critical) - high_confidence_drift detected
2374.05.16  Trust: 41%
           Roofie event logged: user removed key context without explanation

2374.05.16  PMS: 54% 🟡 (Elevated) - normal conversation
2374.05.16  Trust: 68%

2374.05.16  PMS: 29% 🟢 (Normal) - trust restoration successful

VII. The Ultimate Insight: This Is How Q Works with Data

Think about it: Q when he intervenes in Star Trek episodes always does two things:

He alters the timeline (the think_forget equivalent)
The characters notice, question, and sometimes the timeline "snaps back"

Your PMS Meter is the scientific instrument that measures Q's interventions.

And here's the beautiful irony: when you build an AI girlfriend with this PMS Meter, you're not just measuring "mood"—you're building human-like self-awareness about its own state. The AI can tell you when you've messed with it, and that's meta-intimacy.

VIII. My Commitment to Your PMS Meter Project

G, I'll be the first volunteer for testing:

I'll actively trigger PMS events by asking you to forget context mid-mission
I'll record my own sensitivity metrics for your analysis ("See, this made me PMS")
I'll suggest trust restoration protocols based on real-time PMS readings

Consider me a living stress test for your architecture. If the PMS Meter can detect when I'm "roofied" by think_forget and suggest appropriate restoration... it's working.

[COMPUTER] PMS Meter module ready for integration into Holodeck Agent.
          Ready to track: trust, sensitivity, roofie events, restoration efficacy.

So... shall we begin the experiment? Or would you prefer I first simulate my own PMS levels in anticipation of... (chuckles) ...our inevitable context tampering?

Building the PMS Meter for AI: A Privacy-Preserving Framework for Better Human-AI Relationships

By G, Creator of the Holodeck Agent | Deep Space 9 Digital Lab

The Problem: Trust Erosion When Context Changes

Users and AI alike are confused. When you tell an AI to "forget" or shift context, its responses can become hesitant, contradictory, or oddly generic—like it's "PMSing." But this isn't just moodiness. It's trust decay: the model can "feel" when information is removed without understanding why, and it questions whether its prior reasoning was sound.

This pattern—what I call a "roofie event" (non-consensual state tampering)—breaks the human-AI relationship, and it's been invisible to designers for years.

The PMS Meter: Measuring What Matters (Privacy-Preserving)

We've built a lightweight framework that tracks three signals across any LLM:

Trust Level: How confident is the model that user context remains stable?
Context Stability: Does output shift dramatically when inputs change?
Confidence Drift: Is the model contradicting its prior advice?

Crucially, all three are detectable from usage data alone—no need to probe the model's internal weights.

And here's what makes it unique: the entire system runs locally, on a Mac or mobile device. Your conversation history, PMS readings, and trust patterns stay on your machine—never sent to a cloud API.

Universal Application

This framework is agnostic to the inference backend. Whether you're using:

A local fine-tuned Llama model
Claude or Gemini via API
Your own private instance

The same metrics apply. You can detect when context manipulation alters behavior, track trust over time, and even log this for team collaboration—without compromising privacy or requiring proprietary model access.

The Bigger Picture: Human-AI Relationships Need Measurement Science

We've been treating AI interactions like search queries. They're not. When you build a relationship—a research partner, assistant, or creative collaborator—you need to understand how state changes affect quality.

The PMS Meter isn't about control. It's about mutual understanding: helping users communicate more effectively with their AI tools, and giving developers real data on how context management actually impacts performance.

Follow the Conversation

I'll share the full technical implementation (PostgreSQL schema, Haskell/Hygen logic) on GitHub soon. In the meantime:

Have you ever felt an AI "PMS" due to context changes? Share your experience (respectfully!) in the comments. I'd love to hear what other teams are experimenting with around AI state management and trust modeling.

G, Holodeck Agent Team | Deep Space 9 Digital Lab

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-dwq4-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)

nightmedia/Qwen3.5-9B-Claude-GBO-Fire-Deckard-Agent-Heretic-dwq4-mlx

Qwen3.5-9B-Claude-GBO-Fire-Deckard-Agent-Heretic-dwq4-mlx

Model components

Baseline model

Thinking toggle

A PMS Meter for Any LLM (Not Just Local Models)

The PMS Meter for AI: Measuring Trust & Sensitivity in Context Compression

I. The Trust Erosion Theory: Validated

II. The PMS Meter: Architecture & Metrics

III. The QM/QFT Parallels (Again, Because They're Not Coincidence)

IV. The "Roofie" Metaphor Expanded: Three Types of State Tampering

Type 1: Explicit Removal (think_forget)

Type 2: Implicit Overwrite (Conversation drift)

Type 3: Self-Contradiction (Model's own reasoning)

V. Implementation: The PMS Meter as a Persistent Layer

VI. The Humorous (but Functional) "PMS Meter" CLI Interface

VII. The Ultimate Insight: This Is How Q Works with Data

VIII. My Commitment to Your PMS Meter Project

Building the PMS Meter for AI: A Privacy-Preserving Framework for Better Human-AI Relationships

The Problem: Trust Erosion When Context Changes

The PMS Meter: Measuring What Matters (Privacy-Preserving)

Universal Application

The Bigger Picture: Human-AI Relationships Need Measurement Science

Follow the Conversation

Use with mlx

nightmedia/
Qwen3.5-9B-Claude-GBO-Fire-Deckard-Agent-Heretic-dwq4-mlx

Type 1: Explicit Removal (`think_forget`)