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
```

Crossie commited on 11 days ago

Commit

28d2a10

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1 Parent(s): ce178ec

Update README.md

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README.md +25 -2

README.md CHANGED Viewed

@@ -1,4 +1,3 @@
----
 license: mit
 base_model: huihui-ai/Qwen2.5-1.5B-Instruct-abliterated
 tags:
@@ -62,4 +61,28 @@ messages = [
 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))

 license: mit
 base_model: huihui-ai/Qwen2.5-1.5B-Instruct-abliterated
 tags:
 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>
+---