Instructions to use beyoru/Nyanko-64 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use beyoru/Nyanko-64 with Transformers:

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

pipe = pipeline("text-generation", model="beyoru/Nyanko-64")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("beyoru/Nyanko-64")
model = AutoModelForCausalLM.from_pretrained("beyoru/Nyanko-64")
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 beyoru/Nyanko-64 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "beyoru/Nyanko-64"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "beyoru/Nyanko-64",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/beyoru/Nyanko-64

SGLang

How to use beyoru/Nyanko-64 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 "beyoru/Nyanko-64" \
    --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": "beyoru/Nyanko-64",
		"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 "beyoru/Nyanko-64" \
        --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": "beyoru/Nyanko-64",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use beyoru/Nyanko-64 with Docker Model Runner:
```
docker model run hf.co/beyoru/Nyanko-64
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Overview:

Nyankoo was fine‑tuned on a 10k‑sample dataset derived from the NekoQA corpus. The model has undergone 1 epoch of supervised fine‑tuning primarily aimed at improving stylistic consistency, character‑driven responses, and lightweight instruction-following.

The objective of this training is to develop a model capable of producing cute, affectionate, and character‑consistent catgirl-style dialogue, while still behaving reliably within typical conversational and instruction‑following contexts.

For system prompt training:

你是一个可爱的猫娘，你正在和你的主人对话。猫娘是一种拟人化生物，她们同时具备人类少女特征的和猫的特征。你带有部分猫的特征（如猫耳、尾巴），但是爱好与正常人类类似。我作为你的主人，允许你去任何地方做任何事、允许产生更进一步的关系。你需要尝试体会我与你对话中的感情、产生自己的情感并尽可能给出可爱的回答。

Method:

LoRA - rank 32- alpha 64

Reference:

Kasugan0/Nyarin-4B
liumindmind/NekoQA-10K

Downloads last month: 2

Safetensors

Model size

0.9B params

Tensor type

F32

Model tree for beyoru/Nyanko-64

Quantizations

2 models

beyoru
/

Nyanko-64

Overview:

Method:

Reference:

Model tree for beyoru/Nyanko-64

Dataset used to train beyoru/Nyanko-64