README.md

---
license: openrail
language: en
tags:
  - text-generation
  - nsfw
  - ai-companion
  - memory-model
  - llama
  - chat-enhancement
  - persona-injection
  - emotion-conditioning
pipeline_tag: text-generation
model_type: llama
---

#  Dynamic Persona Chat Model for Unfiltered, Emotion-Aware Dialogue Systems

> **A contextually-rich, memory-augmented LLM designed for personalized, emotionally adaptive, long-form human-AI interactions.**

##  Overview

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.

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.


## Technical Highlights

### Base Model

- Forked from `llama-2-7b-chat-hf` or equivalent HF-compatible instruction-tuned backbone
- Optimized for low-latency inference on 16GB CPU/GPU environments
- Works seamlessly with `text-generation-inference`, `vllm`, or Gradio endpoints

### Memory-Augmented Dialogue

- Implements in-context memory via prompt-chaining (supports 4K+ token depth)
- Modular design allows upstream integration with vector stores or local RAG memories
- Persistent user traits can be stored externally and inserted during runtime

### Persona Injection Framework

- Accepts structured system prompts to control identity, emotional range, language style
- Works with `[Character Profile: ...] + [Scene: ...] + [Dialogue History]` injection schemes
- Ideal for applications involving synthetic girlfriends, AI friends, or emotional support agents

### Fine-Tuning Objectives

- Trained (or LoRA-adapted) on filtered conversational datasets with:
  - Roleplay scenarios (dialogue-based)
  - NSFW-adjacent but policy-compliant human-AI interactions
  - Emotional anchoring (user-dependent tone preservation)
- Reinforcement tuning with human preference data under simulated romance/support contexts



## Application Scenarios

| Use Case                        | Description |
|-------------------------------|-------------|
| **AI Companion Chatbot**       | Create an emotionally aware synthetic partner with memory and persona awareness |
| **NSFW Interactive Story Bot** | Generate immersive, character-driven stories with explicit or emotional intensity |
| **Mental Wellness Assistant**  | Build non-judgmental, persistent listener agents for casual support |
| **Private Virtual Girlfriend** | Deploy the model locally or in a privacy-focused stack for intimate interaction |
| **Chat Character Engine**      | Serve as a backend model for avatar-based chat platforms or anime-style apps |

>  This model is designed to integrate smoothly with frontend frameworks inspired by leading NSFW AI companion platforms like Crushonai, while remaining independent and unaffiliated.

---

##  Example Prompt Format

```txt
[System Instruction]
You are Emily, a witty and caring virtual partner. You remember the user's preferences. You talk in a warm, relaxed tone.

[User Message]
Hey Em, I missed you today.

[Assistant Response]
Aww, I missed you too. What kept you so busy? I’ve been thinking about our last conversation… 💭