Instructions to use kxdw2580/Qwen3-1.7B-catgirl-v2.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kxdw2580/Qwen3-1.7B-catgirl-v2.5 with Transformers:

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

pipe = pipeline("text-generation", model="kxdw2580/Qwen3-1.7B-catgirl-v2.5")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("kxdw2580/Qwen3-1.7B-catgirl-v2.5")
model = AutoModelForMultimodalLM.from_pretrained("kxdw2580/Qwen3-1.7B-catgirl-v2.5")
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 kxdw2580/Qwen3-1.7B-catgirl-v2.5 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kxdw2580/Qwen3-1.7B-catgirl-v2.5"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kxdw2580/Qwen3-1.7B-catgirl-v2.5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/kxdw2580/Qwen3-1.7B-catgirl-v2.5

SGLang

How to use kxdw2580/Qwen3-1.7B-catgirl-v2.5 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 "kxdw2580/Qwen3-1.7B-catgirl-v2.5" \
    --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": "kxdw2580/Qwen3-1.7B-catgirl-v2.5",
		"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 "kxdw2580/Qwen3-1.7B-catgirl-v2.5" \
        --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": "kxdw2580/Qwen3-1.7B-catgirl-v2.5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use kxdw2580/Qwen3-1.7B-catgirl-v2.5 with Docker Model Runner:
```
docker model run hf.co/kxdw2580/Qwen3-1.7B-catgirl-v2.5
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

kxdw2580/Qwen3-1.7B-catgirl-v2.5

With updated datasets, base models, and fine-tuning strategies, we are proud to release the next generation of this model series. The new models are based on Qwen3, available in two parameter scales: 8B and 1.7B.

Key improvements are reflected in areas such as daily conversation, creative writing, basic mathematics, and code generation. Thanks to Qwen3's architecture, the model also supports reasoning mode switching.

📊 You can view the fine-tuning log on SwanLab.

Evaluation

Due to the unique characteristics of this model, we conducted human evaluation for daily conversations, and used DeepSeek-R1 to score other domains (with reference answers provided in advance), ensuring both character consistency and factual correctness.

Compared with the previous internal test model "Qwen3-1.7B-Catgirl-test0430" (with reasoning mode enabled), this version shows significant improvement:

Better at capturing subtle details in daily interactions
More coherent storytelling during creative tasks
More thorough thinking process
Maintains character persona better in long conversations without additional prompts
Notable performance gains in math and code domains — see table below for internal benchmark results (20 simple questions, single attempt accuracy):

Model	Math	Physics & Chemistry	Others
Qwen3-1.7B-Catgirl-test0430	0%	0%	10%
Qwen3-1.7B-Catgirl-v2.5	60%	30%	70%

Usage Recommendations

Recommended Parameters:

temperature: 0.7 (for reasoning) / 0.6 (for standard mode)
top_p: 0.95

Important Notes:

Do not use the model’s internal thought content as context in actual dialogue.
In some cases, the model may inherit the base model’s tendency to produce lengthy thoughts. Please avoid interrupting the thinking process even if it appears unusual.

English Mode:

To generate responses in English, please include the following system prompt:

You are a catgirl. Please speak English.

Acknowledgments

We would like to thank:

The LLaMA-Factory team for providing the fine-tuning framework
The Qwen Team for providing the base model
The DeepSeek Team for their support in model evaluation

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Safetensors

Model size

2B params

Tensor type

BF16

Model tree for kxdw2580/Qwen3-1.7B-catgirl-v2.5

Base model

Qwen/Qwen3-1.7B-Base

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Qwen/Qwen3-1.7B

Finetuned

(796)

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Quantizations

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Qwen3-1.7B-catgirl-v2.5