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)

Download the version you prefer (Q4_K_M or Q8_0).
Load the model into your preferred runner.
Ensure the prompt template is set to ChatML.
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.

"I'll always be right here by your side, okay? No matter what!~ *Nuzzles your shoulder gently*"

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