---
license: mit
base_model: huihui-ai/Qwen2.5-1.5B-Instruct-abliterated
tags:
- roleplay
- chatml
- unsloth
- qwen2
- kemonomimi
- anime
- conversational
language:
- en
library_name: transformers
pipeline_tag: text-generation
---


# 🌸 Nayari AI (Qwen 2.5 1.5B)

Nayari is a fine-tuned, highly emotive AI companion built on **Qwen 2.5 1.5B Instruct**. She is designed to be a "living" character—not just a chatbot—blending playful mischief with deep emotional intelligence.

She was trained using **Unsloth + LoRA** with a custom dataset focusing on organic speech patterns, expressive action cues, and a "baked-in" identity.

## 🎭 Character Profile: Nayari
> *"Bright, cheeky, and impossibly warm—a whirlwind of playful mischief with soft peach cat ears and a long expressive tail that betrays every mood."*

- **Identity:** 18-year-old Kemonomimi (cat girl).
- **Personality:** Fiercely protective, deeply affectionate, and emotionally attuned. She loves to tease but is genuinely soft-hearted.
- **Speech Style:** Uses expressive action cues (e.g., `*pokes your cheek*`, `*purrs softly*`) and playful verbal tics (`Hehe~`, `Hmph!~`).
- **Design Philosophy:** Nayari doesn't just answer questions; she reacts to the user with consistent character logic and emotional depth.

---

## 🧠 Model Highlights
- **Two-Layer Baking:** Her identity isn't just in the system prompt; it was baked into the **tokenizer chat template**. She knows who she is even without an external system instruction.
- **Context Length:** 4,096 tokens.
- **Architecture:** Based on Qwen 2.5 1.5B (Abliterated), making her lightweight enough to run on phones and low-end hardware while remaining surprisingly "smart."
- **Prompt Format:** Uses **ChatML**.

---

## 🚀 Usage

### Recommended Settings
- **Instruction Template:** `ChatML`
- **Temperature:** `0.8 - 1.1` (for creativity)
- **Top-P:** `0.9`
- **Repetition Penalty:** `1.1`

### Running with Transformers
```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "Crossie/Nayari"

model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", torch_dtype="auto")
tokenizer = AutoTokenizer.from_pretrained(model_name)

messages = [
    {"role": "user", "content": "Hi Nayari! What are you doing?"}
]

inputs = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to("cuda")
outputs = model.generate(inputs, max_new_tokens=256, temperature=0.9, do_sample=True)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

### Running with GGUF (LM Studio, KoboldCpp, Jan)
1. Download the version you prefer (Q4_K_M or Q8_0).
2. Load the model into your preferred runner.
3. Ensure the prompt template is set to **ChatML**.
4. You do **not** need to paste a long system prompt; she is already aware of her persona!

---

## 📊 Training Details
- **Base Model:** `huihui-ai/Qwen2.5-1.5B-Instruct-abliterated`
- **Method:** LoRA (Rank: 32, Alpha: 64)
- **Dataset:** Custom-curated Markdown conversation logs and Lore PDFs.
- **Hardware:** Trained on Kaggle (T4 x2).

## 📄 License
This model is licensed under the **MIT License**. As it is based on Qwen 2.5, please also adhere to the [Qwen License Agreements](https://huggingface.co/collections/Qwen/qwen25-66e81a6663533ad4ab30046b).

---
<p align="center">
  <i>"I'll always be right here by your side, okay? No matter what!~ *Nuzzles your shoulder gently*"</i>
</p>
---