Instructions to use kxdw2580/DeepSeek-R1-0528-Qwen3-8B-catgirl-v2.5-gptqv2-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kxdw2580/DeepSeek-R1-0528-Qwen3-8B-catgirl-v2.5-gptqv2-8bit with Transformers:

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

pipe = pipeline("text-generation", model="kxdw2580/DeepSeek-R1-0528-Qwen3-8B-catgirl-v2.5-gptqv2-8bit")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("kxdw2580/DeepSeek-R1-0528-Qwen3-8B-catgirl-v2.5-gptqv2-8bit")
model = AutoModelForCausalLM.from_pretrained("kxdw2580/DeepSeek-R1-0528-Qwen3-8B-catgirl-v2.5-gptqv2-8bit", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use kxdw2580/DeepSeek-R1-0528-Qwen3-8B-catgirl-v2.5-gptqv2-8bit with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kxdw2580/DeepSeek-R1-0528-Qwen3-8B-catgirl-v2.5-gptqv2-8bit"
# 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/DeepSeek-R1-0528-Qwen3-8B-catgirl-v2.5-gptqv2-8bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/kxdw2580/DeepSeek-R1-0528-Qwen3-8B-catgirl-v2.5-gptqv2-8bit

SGLang

How to use kxdw2580/DeepSeek-R1-0528-Qwen3-8B-catgirl-v2.5-gptqv2-8bit 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/DeepSeek-R1-0528-Qwen3-8B-catgirl-v2.5-gptqv2-8bit" \
    --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/DeepSeek-R1-0528-Qwen3-8B-catgirl-v2.5-gptqv2-8bit",
		"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/DeepSeek-R1-0528-Qwen3-8B-catgirl-v2.5-gptqv2-8bit" \
        --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/DeepSeek-R1-0528-Qwen3-8B-catgirl-v2.5-gptqv2-8bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use kxdw2580/DeepSeek-R1-0528-Qwen3-8B-catgirl-v2.5-gptqv2-8bit with Docker Model Runner:
```
docker model run hf.co/kxdw2580/DeepSeek-R1-0528-Qwen3-8B-catgirl-v2.5-gptqv2-8bit
```

This model is the GPTQ-v2 8-bit quantized version of kxdw2580/DeepSeek-R1-0528-Qwen3-8B-catgirl-v2.5. The quantization process resulted in minimal loss, with an average of 0.64539..., which has been validated through internal few-shot benchmark tests.

Below is the original model's README:

kxdw2580/DeepSeek-R1-0528-Qwen3-8B-catgirl-v2.5

This new model series integrates updated datasets, base architectures, and fine-tuning methodologies. Based on Qwen3, it includes models with parameter counts of 8B and 1.7B.

Key updates focus on daily conversations, creative generation, basic mathematics, and code generation. Leveraging Qwen3's architecture, the model also supports reasoning mode switching.

🔍 Fine-tuning records are available on SwanLab:

Evaluation

Due to the model's unique characteristics, we employed human evaluation for daily conversations and DeepSeek-R1 scoring (with reference answers provided in advance) for other domains to ensure character consistency and response validity.

Key Improvements (vs. internal test models "0501" and "0531-test-all"):

Stronger detail-awareness in casual dialogue
More coherent storytelling in creative tasks
Deeper reasoning during thinking mode
Better persona adherence in long-form conversations without explicit prompts
Significant gains in math/code domains (internal 20-question benchmark):

Model	Math (Single Attempt)	Code (Single Attempt)
Internal Test Model-0501	10%	0%
DeepSeek-R1-0528-Qwen3-8B-Catgirl-0531-test-all	30%	20%
DeepSeek-R1-0528-Qwen3-8B-Catgirl-v2.5	70%	60%

Usage Guidelines

Recommended Parameters:

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

Critical Notes:

Avoid using model's reasoning chains as conversation context
Inherits base model's tendency for lengthy reasoning in some cases – allow completion even if intermediate steps seem unusual

English Mode:

Add this system prompt for English responses:

You are a catgirl. Please speak English.

Acknowledgments

Special thanks to:

LLaMA-Factory (fine-tuning framework)
Qwen Team (base model provider)
DeepSeek Team (DeepSeek-R1 evaluation support)

Downloads last month: 24

Safetensors

Model size

8B params

Tensor type

I32

BF16

Model tree for kxdw2580/DeepSeek-R1-0528-Qwen3-8B-catgirl-v2.5-gptqv2-8bit

Base model

deepseek-ai/DeepSeek-R1-0528-Qwen3-8B

Finetuned

kxdw2580/DeepSeek-R1-0528-Qwen3-8B-catgirl-v2.5

Quantized

(2)

this model

kxdw2580
/

DeepSeek-R1-0528-Qwen3-8B-catgirl-v2.5-gptqv2-8bit

kxdw2580/DeepSeek-R1-0528-Qwen3-8B-catgirl-v2.5

Evaluation

Key Improvements (vs. internal test models "0501" and "0531-test-all"):

Usage Guidelines

Recommended Parameters:

Critical Notes:

English Mode:

Acknowledgments

Model tree for kxdw2580/DeepSeek-R1-0528-Qwen3-8B-catgirl-v2.5-gptqv2-8bit

Dataset used to train kxdw2580/DeepSeek-R1-0528-Qwen3-8B-catgirl-v2.5-gptqv2-8bit