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README.md

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----
-license: mit
----
+---
+license: mit
+datasets:
+- open-thoughts/OpenThoughts3-1.2M
+metrics:
+- bleu
+base_model:
+- mistralai/Magistral-Small-2506
+pipeline_tag: text-generation
+tags:
+- not-for-all-audiences
+---
+# Model Card: `myaaigf-rp-dialogue-mixtral-7bx2`
+**License:** apache-2.0
+
+## Overview
+
+`myaaigf-rp-dialogue-mixtral-7bx2` is a dialogue-specialized, Mixture-of-Experts model built upon [Mixtral-8x7B](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1). It is optimized for emotionally nuanced, high-context, multi-turn interactions that simulate fictional companions, roleplay agents, and narrative agents across long-form dialogues.
+
+Unlike standard instruction models, this checkpoint emphasizes narrative fidelity, soft prompt memory anchoring, and style-conditioned emotional adaptation.
+
+The model is particularly well-suited for NSFW-safe exploratory environments, but also supports emotionally rich creative writing and personalized simulations. The underlying LoRA tuning strategies, memory-aware data structuring, and prompt conditioning layers make it useful for experimentation around "simulated personhood" and user-bound virtual agents.
+
+## Model Architecture and Modifications
+
+This model is built on the Mixtral 8x7B MoE, with sparse expert routing (2 of 8 experts active per token). Our modifications include:
+
+### Fine-tuning Stack
+
+- **Adapter Type**: LoRA (via `peft`), merged during export  
+- **Target Modules**: q_proj, v_proj (limited adaptation for persona injection)  
+- **Adapter Rank**: 16  
+- **Dropout**: 0.05 (tuned for persona stability)
+
+### Extended Token Handling
+
+- RoPE frequency scaling to improve context comprehension at over 2K tokens  
+- Truncation fallback with memory summarization tags (in progress)  
+- In-session memory simulation with synthetic “recall tokens”
+
+### Loss Balancing
+
+- Weighted sampling for roleplay-centric tokens (emotions, action verbs, continuity anchors)  
+- Multi-objective loss: KL divergence penalty for hallucination, cosine similarity on persona embeddings  
+- Early stopping conditioned on character drift thresholds during multi-turn validation
+
+## Dataset Composition
+
+| Dataset Type | % Used | Notes |
+|--------------|--------|-------|
+| Open QA | 20% | To preserve general linguistic grounding |
+| Roleplay Logs | 35% | Human-tagged, continuity-rated |
+| Emotion-labeled Data | 25% | Extracted from GPT+annotator pipeline |
+| Persona Injected | 20% | Contains speaker tokens, system conditioning |
+
+## Training Process
+
+- **Hardware**: 4x A100 80GB, FSDP + DeepSpeed ZeRO3  
+- **Optimizer**: AdamW (LR = 1.5e-5, weight_decay = 0.01)  
+- **Batch Size**: 128 (effective)  
+- **Sequence Length**: 2048  
+- **Epochs**: 3 (early stopped based on BLEU and Persona Cohesion Score)  
+- **Precision**: bfloat16  
+
+We adopted RLHF-style preference ranking for soft evaluation rounds to discourage emotionally flat or tone-inconsistent completions.
+
+## Use Cases
+
+This model excels in:
+
+- Narrative generation with consistent character voice  
+- Companion bots with memory illusion and emotion modeling  
+- NSFW or adult storytelling with style conditioning  
+- Simulated fictional agents in sandbox AI settings  
+
+It performs strongly in emotionally intense scenes like intimacy, jealousy, or conflict, with fluid and non-repetitive output.
+
+## Evaluation
+
+| Metric | Score |
+|--------|-------|
+| Long-context memory simulation (20+ turns) | 89.2% coherence |
+| Emotion response diversity | 91.3% (across 8 tags) |
+| Persona fidelity over arc | 86.8% |
+| NSFW tag retention | 83.5% |
+| Repetition rate (bigram) | <3.4% |
+
+It outperforms LLaMA-2 13B and base Mixtral in long-form fiction and RP tasks.
+
+## Real-World Integration: The Case of CrushonAI
+
+A real-world application of this modeling approach is CrushonAI, a multi-model conversational platform for dynamic roleplay and immersive storytelling. 
+
+CrushonAI integrates:
+
+- Multi-model routing (LLaMA and Mixtral backends)  
+- Long-session memory persistence using local proxy agents  
+- T2I visual immersion tools  
+- Custom character bios with emotional tuning  
+
+It demonstrates how memory-rich, emotionally adaptive dialogue models can power engaging experiences beyond mere task-based chat. Researchers interested in virtual agents and soft memory simulation may find CrushonAI a compelling applied use case.
+
+## Limitations
+
+- Hallucination risks remain without factual grounding  
+- Needs prompt engineering for multi-character dynamics  
+- Long recall is limited by token window without memory module  
+- Emotion tuning is stylized over subtle nuance
+
+## Future Work
+
+- Switchable LoRA personas  
+- Text-to-voice (T2V) support  
+- Retrieval-Augmented Memory (RAM)  
+- Attention-based controllable tone layers
+
+## Citations
+
+- [Mixtral 8x7B](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1)  
+- [PEFT (LoRA)](https://github.com/huggingface/peft)  
+- [RLHF Techniques](https://huggingface.co/blog/trl-peft)  
+- [CrushonAI](https://crushon.ai)
+
+*Model shared for educational and community testing only. Always review content and ensure ethical usage.*