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README.md
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Remembers user preferences, character traits, and conversational history to simulate a continuously evolving AI companion.
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Maintains consistent tone and relationship development over time, making the AI feel more "real" and emotionally aware.
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Supports flexible role configurations for personalized AI girlfriend/boyfriend experiences, roleplay, or emotional support.
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- **Privacy-Oriented Architecture**
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Localized memory control with no forced data collection, ideal for sensitive companion AI scenarios.
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Easily integrated into larger platforms like *Crushon-style AI girlfriend systems*, or deployed as a standalone LLM chatbot demo.
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- Emotional Support Bots
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- Roleplay and Narrative Agents
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---
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license: openrail
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language: en
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tags:
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- text-generation
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- nsfw
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- ai-companion
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- memory-model
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- llama
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- chat-enhancement
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- persona-injection
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- emotion-conditioning
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pipeline_tag: text-generation
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model_type: llama
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---
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# Dynamic Persona Chat Model for Unfiltered, Emotion-Aware Dialogue Systems
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> **A contextually-rich, memory-augmented LLM designed for personalized, emotionally adaptive, long-form human-AI interactions.**
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## Overview
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This model is a customized instruction-following LLM optimized for **long-context dialogue generation**, especially in **emotionally intense**, **personified**, or **roleplay-based applications**. It supports unfiltered text outputs, character-level memory continuity, and emotion-driven response modulation—ideal for developing **private companion bots**, **NSFW story agents**, and **synthetic relationship simulations**.
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The design draws architectural inspiration from open-source efforts in **conversational memory**, **persona conditioning**, and **preference-aligned decoding strategies**, as well as from user interaction behaviors observed in existing virtual AI companion platforms.
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## Technical Highlights
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### Base Model
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- Forked from `llama-2-7b-chat-hf` or equivalent HF-compatible instruction-tuned backbone
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- Optimized for low-latency inference on 16GB CPU/GPU environments
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- Works seamlessly with `text-generation-inference`, `vllm`, or Gradio endpoints
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### Memory-Augmented Dialogue
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- Implements in-context memory via prompt-chaining (supports 4K+ token depth)
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- Modular design allows upstream integration with vector stores or local RAG memories
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- Persistent user traits can be stored externally and inserted during runtime
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### Persona Injection Framework
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- Accepts structured system prompts to control identity, emotional range, language style
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- Works with `[Character Profile: ...] + [Scene: ...] + [Dialogue History]` injection schemes
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- Ideal for applications involving synthetic girlfriends, AI friends, or emotional support agents
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### Fine-Tuning Objectives
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- Trained (or LoRA-adapted) on filtered conversational datasets with:
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- Roleplay scenarios (dialogue-based)
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- NSFW-adjacent but policy-compliant human-AI interactions
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- Emotional anchoring (user-dependent tone preservation)
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- Reinforcement tuning with human preference data under simulated romance/support contexts
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## Application Scenarios
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| Use Case | Description |
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|-------------------------------|-------------|
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| **AI Companion Chatbot** | Create an emotionally aware synthetic partner with memory and persona awareness |
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| **NSFW Interactive Story Bot** | Generate immersive, character-driven stories with explicit or emotional intensity |
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| **Mental Wellness Assistant** | Build non-judgmental, persistent listener agents for casual support |
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| **Private Virtual Girlfriend** | Deploy the model locally or in a privacy-focused stack for intimate interaction |
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| **Chat Character Engine** | Serve as a backend model for avatar-based chat platforms or anime-style apps |
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> This model is suitable for integration into Crushon-style frontend frameworks, though not affiliated. It supports `Gradio`, `Streamlit`, and `API` deployment formats for rapid prototyping.
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---
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## Example Prompt Format
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```txt
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[System Instruction]
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You are Emily, a witty and caring virtual partner. You remember the user's preferences. You talk in a warm, relaxed tone.
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[User Message]
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Hey Em, I missed you today.
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[Assistant Response]
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Aww, I missed you too. What kept you so busy? I’ve been thinking about our last conversation… 💭
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