Instructions to use Laow0v0/neko-qwen3-4b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Laow0v0/neko-qwen3-4b with Transformers:

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

pipe = pipeline("text-generation", model="Laow0v0/neko-qwen3-4b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Laow0v0/neko-qwen3-4b")
model = AutoModelForCausalLM.from_pretrained("Laow0v0/neko-qwen3-4b", device_map="auto")
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 Settings

vLLM

How to use Laow0v0/neko-qwen3-4b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Laow0v0/neko-qwen3-4b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Laow0v0/neko-qwen3-4b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Laow0v0/neko-qwen3-4b

SGLang

How to use Laow0v0/neko-qwen3-4b 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 "Laow0v0/neko-qwen3-4b" \
    --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": "Laow0v0/neko-qwen3-4b",
		"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 "Laow0v0/neko-qwen3-4b" \
        --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": "Laow0v0/neko-qwen3-4b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use Laow0v0/neko-qwen3-4b 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 Laow0v0/neko-qwen3-4b 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 Laow0v0/neko-qwen3-4b to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Laow0v0/neko-qwen3-4b to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="Laow0v0/neko-qwen3-4b",
    max_seq_length=2048,
)

Docker Model Runner
How to use Laow0v0/neko-qwen3-4b with Docker Model Runner:
```
docker model run hf.co/Laow0v0/neko-qwen3-4b
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

neko-qwen3-4b 🐾

Developed by: Laow0v0
License: apache-2.0
Finetuned from model: unsloth/qwen3-4b-instruct-2507 (bnb-4bit), merged to 16-bit
Training data: liumindmind/NekoQA-10K
Demo: Laow0v0/neko-qwen3-4b-demo

A catgirl-persona (猫娘) finetune of Qwen3-4B-Instruct-2507.

Training data

Finetuned on NekoQA-10K by liumindmind — 10,000 single-turn QA pairs written in a consistent catgirl persona. Per the dataset card, every answer follows the same conventions:

addresses the user as 主人 ("master"),
ends sentences with characteristic verbal tics (喵~, no desu, 的说喵),
keeps a cute, affectionate, 二次元 register.

The data is primarily Chinese, with some mixed Chinese-English. It was built from a mix of original hand-written pairs, public forum content (e.g. 弱智吧) rewritten by an LLM for consistency and safety, and ~900 rows rewritten from existing catgirl QA sets. Answers were mostly LLM-generated and human-filtered. The dataset is Apache-2.0.

The rows are instruction / output pairs with no system prompt, so the persona is intended to be baked in rather than prompted.

Intended use

Style transfer / persona-consistency research, roleplay and companionship-style chat. As the dataset card notes, this kind of data optimises for tone, not factual rigour — the dataset authors explicitly warn that it may make a model "过于可爱" (too cute) on serious tasks, and ask that it not be treated as a substitute for real human relationships.

Limitations

Persona adherence is inconsistent. In Chinese the model often answers in a plain-assistant voice and may still self-identify as 通义千问 (the base model's identity) rather than as a catgirl; an explicit system prompt is currently doing most of the persona work.
Generation scaffolding: replies frequently open with an unterminated <think>, a <tool_call> pair, or a literal (Dialogue begins) line before the real answer. In this repo's tokenizer.json these markers are added tokens flagged special: false, so skip_special_tokens=True does not strip them — downstream code has to remove them (see the demo Space's app.py). Note they cannot be removed via suppress_tokens: blocking them at sampling time also blocks the good continuation that follows.
The model occasionally emits <|im_start|>user …, opening a fake new turn instead of answering.
Not suitable for tasks requiring factual reliability.

Citation

Please cite the dataset if you build on this work:

@article{nekoqa2025,
  title={NekoQA-10K: A Catgirl Dialogue Dataset and NekoBench Evaluation},
  author={MindsRiverPonder},
  journal={ZHIHU preprint ZHIHU:2508.22},
  year={2025}
}

This qwen3 model was trained 2x faster with Unsloth and Huggingface's TRL library.

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Safetensors

Model size

4B params

Tensor type

BF16

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