Instructions to use Crossie/Nayari with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Crossie/Nayari with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Crossie/Nayari")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Crossie/Nayari")
model = AutoModelForCausalLM.from_pretrained("Crossie/Nayari")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Crossie/Nayari with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Crossie/Nayari"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Crossie/Nayari",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Crossie/Nayari

SGLang

How to use Crossie/Nayari with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Crossie/Nayari" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Crossie/Nayari",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Crossie/Nayari" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Crossie/Nayari",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use Crossie/Nayari with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Crossie/Nayari to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Crossie/Nayari to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Crossie/Nayari to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="Crossie/Nayari",
    max_seq_length=2048,
)

Docker Model Runner
How to use Crossie/Nayari with Docker Model Runner:
```
docker model run hf.co/Crossie/Nayari
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

🌸 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

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!