Instructions to use netease-youdao/Confucius3-Math with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use netease-youdao/Confucius3-Math with Transformers:

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

pipe = pipeline("text-generation", model="netease-youdao/Confucius3-Math")
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("netease-youdao/Confucius3-Math")
model = AutoModelForCausalLM.from_pretrained("netease-youdao/Confucius3-Math", 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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use netease-youdao/Confucius3-Math with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "netease-youdao/Confucius3-Math"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "netease-youdao/Confucius3-Math",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/netease-youdao/Confucius3-Math

SGLang

How to use netease-youdao/Confucius3-Math 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 "netease-youdao/Confucius3-Math" \
    --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": "netease-youdao/Confucius3-Math",
		"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 "netease-youdao/Confucius3-Math" \
        --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": "netease-youdao/Confucius3-Math",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use netease-youdao/Confucius3-Math with Docker Model Runner:
```
docker model run hf.co/netease-youdao/Confucius3-Math
```

nomadlx commited on Jun 20, 2025

Commit

700052f

1 Parent(s): a0b147c

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@@ -48,8 +48,8 @@ For each model, we used the official system prompt provided by the corresponding
 | Benchmark | DeepSeek-R1 | Qwen3-14B | QwQ-32B | DeepSeek-R1-Distill-Qwen-14B | Confucius3-Math |
 |-------------------|----------------------|------------|--------------|----------------|------------|
 | CK12-MATH | 92.74 | 94.04 | 93.60 | 82.86 | **96.24** |
-| GAOKAO-Bench(math) | 93.27 | 94.44 | 94.93 | 86.75 | **98.46** |
-| MathBench(K12) | 89.99 | 96.51 | **96.57** | 88.40 | 95.10 |
 | CMATH | 95.81 | 95.90 | 95.95 | 77.41 | **96.13** |
 | MATH-500 | 97.30 | 96.80 | 98.00 | 93.90 | **98.80** |
 | AIME 2024 | 79.80 | 79.30 | 79.50 | 69.70 | **81.15** |

 | Benchmark | DeepSeek-R1 | Qwen3-14B | QwQ-32B | DeepSeek-R1-Distill-Qwen-14B | Confucius3-Math |
 |-------------------|----------------------|------------|--------------|----------------|------------|
 | CK12-MATH | 92.74 | 94.04 | 93.60 | 82.86 | **96.24** |
+| GAOKAO-Bench (math) | 93.27 | 94.44 | 94.93 | 86.75 | **98.46** |
+| MathBench (K12) | 89.99 | 96.51 | **96.57** | 88.40 | 95.10 |
 | CMATH | 95.81 | 95.90 | 95.95 | 77.41 | **96.13** |
 | MATH-500 | 97.30 | 96.80 | 98.00 | 93.90 | **98.80** |
 | AIME 2024 | 79.80 | 79.30 | 79.50 | 69.70 | **81.15** |