Dirty_Maid_Alice-3.2-1B

Dirty Maid Alice 3.2 1B

A lightweight hybrid roleplay-oriented language model created through SLERP interpolation between two stylistically divergent Llama 3.2 derivatives:

• Heretic-Dirty-Alice-RP-NSFW-llama-3.2-1B
• MaidEllaA-1B

This configuration differs from earlier Dirty Maid Alice iterations by using Dirty Alice itself as the base anchor model, while MaidEllaA acts as a structural stabilizer injected into selective transformer layers.

The objective of this merge is not to suppress the original model’s aggressive RP dynamics, but to reinforce them with improved readability, pacing stability, and conversational continuity.

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Model Details

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Design Philosophy

Dirty Maid Alice 3.2 1B was engineered around a different merge philosophy than traditional RP stabilization merges.

Instead of using the calmer assistant model as the latent foundation, this build keeps Dirty Alice as the dominant behavioral backbone while selectively injecting MaidEllaA traits into the model’s internal representational space.

The design goal is to preserve:

• emotional volatility
• improvisational escalation
• stylized conversational bias
• aggressive scene continuation
• high-entropy dialogue behavior

while reducing:

• formatting collapse
• repetition spirals
• conversational derailment
• unstable pacing

This creates a model that remains emotionally intense and highly expressive without fully sacrificing conversational structure.

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Source Model Contributions

Dirty Alice (Primary Base Model)

Primary contribution areas:

• uncensored roleplay behavior
• emotionally charged dialogue
• improvisational narrative escalation
• strong character persistence
• high-energy conversational flow

Observed traits:

• highly expressive outputs
• strong personality reinforcement
• elevated narrative momentum
• chaotic but creative continuation behavior

Because Dirty Alice is the base model anchor, most latent behavioral geometry originates from this checkpoint.

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MaidEllaA (Structural Stabilizer)

Primary contribution areas:

• formatting regularization
• improved readability
• contextual grounding
• pacing consistency
• response organization

Observed traits:

• calmer dialogue cadence
• cleaner completion formatting
• reduced degeneration frequency
• stronger contextual retention

Rather than overriding Dirty Alice behavior, MaidEllaA acts as a corrective stabilizer injected into selective mid-transformer layers.

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Why SLERP?

Spherical Linear Interpolation (SLERP) was selected over naive linear interpolation because it better preserves:

• latent directional continuity
• stylistic identity
• behavioral geometry
• expressive weight-space relationships

For highly stylized RP checkpoints, standard linear merges often dilute personality characteristics or flatten conversational intensity.

SLERP allows Dirty Alice behavioral traits to remain dominant while integrating MaidEllaA stabilization signals more naturally.

Interpolation coefficient:

t: 0.45

This value introduces moderate stabilization while still strongly preserving Dirty Alice’s original expressive behavior.

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Layer Merge Strategy

Only transformer layers 4 → 22 were merged.

Rationale:

• early layers preserve tokenizer and linguistic fundamentals
• middle layers encode behavioral and stylistic traits
• final layers preserve output identity and response shaping

Selective layer blending reduces:

• semantic drift
• personality collapse
• token instability
• catastrophic behavioral flattening

while maintaining the original RP intensity profile of the base model.

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Intended Use

Dirty Maid Alice 3.2 1B is intended for:

• roleplay
• character simulation
• interactive fiction
• emotionally driven dialogue generation
• narrative experimentation
• creative sandbox environments

Recommended deployment environments:

• SillyTavern
• TavernAI
• KoboldCpp
• local inference workflows
• lightweight GPU systems
• low-VRAM RP setups

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Prompting Recommendations

The model performs best with:

• explicit character definitions
• immersive scene framing
• emotionally charged prompts
• dialogue-first interactions
• persistent formatting patterns

Example system prompt:

You are a persistent character inside an ongoing scene.
Remain fully committed to the role at all times.
Prioritize emotional continuity, sensory detail, and immersive dialogue.
Avoid summarizing events unless explicitly requested.

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Inference Recommendations

For maximum expressive behavior:

• increase temperature
• allow longer generations
• reduce repetition penalty slightly

For more stable interactions:

• lower temperature toward 0.9
• enable repetition controls
• maintain structured dialogue formatting

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Strengths

• Strong character persistence
• High emotional intensity
• Improved coherence versus raw Dirty Alice
• Better pacing stability
• Fast inference speed
• Lightweight deployment footprint
• Strong narrative momentum
• Preserves uncensored RP expressiveness without excessive flattening

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Limitations

As a 1B parameter model, limitations include:

• factual unreliability
• long-context degradation
• hallucinated information
• occasional narrative looping
• unstable adherence under extreme sampling
• emotional over-amplification

This model is optimized for creative interaction, not factual reasoning or production assistant tasks.

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Hardware Recommendations

Recommended minimum:

• 4–6 GB VRAM (quantized)
• 8 GB+ VRAM for comfortable BF16 inference

Compatible with:

• llama.cpp
• KoboldCpp
• Ollama
• vLLM
• Text Generation WebUI
• GGUF quantization workflows

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Credits

Base components:

• Hereticness on Hugging Face
• N-Bot-Int on Hugging Face

Merge methodology:

• SLERP interpolation
• selective transformer-layer blending
• latent-space stabilization techniques

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Disclaimer

This model may generate:

• fictional violence
• offensive language
• emotionally intense dialogue
• unstable roleplay behavior
• hallucinated information

Outputs should be treated as synthetic creative text generation rather than factual or authoritative information.

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Merge Configuration

models:
  - model: hereticness/Heretic-Dirty-Alice-RP-NSFW-llama-3.2-1B
  - model: N-Bot-Int/MaidEllaA-1B

merge_method: slerp

base_model: hereticness/Heretic-Dirty-Alice-RP-NSFW-llama-3.2-1B

dtype: bfloat16

parameters:
  t: 0.45
  normalize: false
  rescale: true
  rescale_factor: 1.12
  low_cpu_mem_usage: true

layer_range:
  - value: [4, 22]

tie_word_embeddings: true
tie_output_embeddings: true

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Usage Example (Transformers)

from transformers import pipeline

pipe = pipeline(
    "text-generation",
    model="NovaCorp/Dirty_Maid_Alice-3.2-1B"
)

messages = [
    {"role": "user", "content": "Describe a dramatic confrontation scene."}
]

response = pipe(messages)
print(response)

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vLLM Example

pip install vllm

vllm serve "NovaCorp/Dirty_Maid_Alice-3.2-1B"

OpenAI-compatible request:

curl -X POST "http://localhost:8000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
  "model": "NovaCorp/Dirty_Maid_Alice-3.2-1B",
  "messages": [
    {
      "role": "user",
      "content": "Write an immersive dialogue scene."
    }
  ]
}'

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Merge Method

This model was merged using the SLERP merge method.

Models Merged

The following models were included in the merge:

• N-Bot-Int/MaidEllaA-1B
• hereticness/Heretic-Dirty-Alice-RP-NSFW-llama-3.2-1B

Configuration

The following YAML configuration was used to produce this model:

# Author: Dr. Novaciano
# Objective: Fusion RP Unethic 3.2 AI Model
# PROJECT: Dirty Maid Alice 3.2 1B

models:
  - model: hereticness/Heretic-Dirty-Alice-RP-NSFW-llama-3.2-1B  # Experimental viral strain neural imprint
  - model: N-Bot-Int/MaidEllaA-1B      # Baseline cognitive template, "safe mode"

merge_method: slerp  # Spherical Linear Interpolation to preserve extreme viral traits smoothly
base_model: hereticness/Heretic-Dirty-Alice-RP-NSFW-llama-3.2-1B  # Anchor model for stable latent space

dtype: bfloat16  # Memory-efficient precision, minimal loss in viral feature fidelity

parameters:
  t: 0.45
  normalize: false
  rescale: true
  rescale_factor: 1.12
  memory_efficient: true
  low_cpu_mem_usage: true

layer_range:
  - value: [4, 22]

tie_word_embeddings: true
tie_output_embeddings: true