Instructions to use Hao0oWang/Qwen2.5-Math-7B-16k-think with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Hao0oWang/Qwen2.5-Math-7B-16k-think with Transformers:

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

pipe = pipeline("text-generation", model="Hao0oWang/Qwen2.5-Math-7B-16k-think")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Hao0oWang/Qwen2.5-Math-7B-16k-think")
model = AutoModelForCausalLM.from_pretrained("Hao0oWang/Qwen2.5-Math-7B-16k-think", 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 Hao0oWang/Qwen2.5-Math-7B-16k-think with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Hao0oWang/Qwen2.5-Math-7B-16k-think"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Hao0oWang/Qwen2.5-Math-7B-16k-think",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Hao0oWang/Qwen2.5-Math-7B-16k-think

SGLang

How to use Hao0oWang/Qwen2.5-Math-7B-16k-think 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 "Hao0oWang/Qwen2.5-Math-7B-16k-think" \
    --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": "Hao0oWang/Qwen2.5-Math-7B-16k-think",
		"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 "Hao0oWang/Qwen2.5-Math-7B-16k-think" \
        --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": "Hao0oWang/Qwen2.5-Math-7B-16k-think",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Hao0oWang/Qwen2.5-Math-7B-16k-think with Docker Model Runner:
```
docker model run hf.co/Hao0oWang/Qwen2.5-Math-7B-16k-think
```

The base model used in CurioSFT (based on Qwen2.5-Math-7B-Base), described in Learning While Staying Curious: Entropy-Preserving Supervised Fine-Tuning via Adaptive Self-Distillation for Large Reasoning Models. We change the chat_template, rope_theta, and the context window for long context reasoning.

Citation

If you find our data is useful, please kindly cite our paper:

@misc{curioSFT,
      title={Learning While Staying Curious: Entropy-Preserving Supervised Fine-Tuning via Adaptive Self-Distillation for Large Reasoning Models}, 
      author={Hao Wang and Hao Gu and Hongming Piao and Kaixiong Gong and Yuxiao Ye and Xiangyu Yue and Sirui Han and Yike Guo and Dapeng Wu},
      year={2026},
      eprint={2602.02244},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2602.02244}, 
}

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Safetensors

Model size

8B params

Tensor type

F32

Paper for Hao0oWang/Qwen2.5-Math-7B-16k-think

Learning While Staying Curious: Entropy-Preserving Supervised Fine-Tuning via Adaptive Self-Distillation for Large Reasoning Models

Paper • 2602.02244 • Published Feb 2 • 2