Instructions to use yangzhch6/Qwen2.5-Math-7B-Think32k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use yangzhch6/Qwen2.5-Math-7B-Think32k with Transformers:

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

pipe = pipeline("text-generation", model="yangzhch6/Qwen2.5-Math-7B-Think32k")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("yangzhch6/Qwen2.5-Math-7B-Think32k")
model = AutoModelForCausalLM.from_pretrained("yangzhch6/Qwen2.5-Math-7B-Think32k")
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

vLLM

How to use yangzhch6/Qwen2.5-Math-7B-Think32k with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "yangzhch6/Qwen2.5-Math-7B-Think32k"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "yangzhch6/Qwen2.5-Math-7B-Think32k",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/yangzhch6/Qwen2.5-Math-7B-Think32k

SGLang

How to use yangzhch6/Qwen2.5-Math-7B-Think32k 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 "yangzhch6/Qwen2.5-Math-7B-Think32k" \
    --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": "yangzhch6/Qwen2.5-Math-7B-Think32k",
		"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 "yangzhch6/Qwen2.5-Math-7B-Think32k" \
        --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": "yangzhch6/Qwen2.5-Math-7B-Think32k",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use yangzhch6/Qwen2.5-Math-7B-Think32k with Docker Model Runner:
```
docker model run hf.co/yangzhch6/Qwen2.5-Math-7B-Think32k
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

The base Qwen2.5-Math-7B model used by LUFFY, described in Learning to Reason under Off-Policy Guidance. We change to rope_theta from 10000 to 40000 and extend the context window to 16k. Also, we modify the chat_template for the system prompt and add .

Github: https://github.com/ElliottYan/LUFFY

Citation

If you find our model, data, or evaluation code useful, please kindly cite our paper:

@misc{luffy,
      title={Learning to Reason under Off-Policy Guidance}, 
      author={Jianhao Yan and Yafu Li and Zican Hu and Zhi Wang and Ganqu Cui and Xiaoye Qu and Yu Cheng and Yue Zhang},
      year={2025},
      eprint={2504.14945},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2504.14945}, 
}

Downloads last month: 3

Safetensors

Model size

8B params

Tensor type

F32

Model tree for yangzhch6/Qwen2.5-Math-7B-Think32k

Quantizations

2 models

Paper for yangzhch6/Qwen2.5-Math-7B-Think32k

Learning to Reason under Off-Policy Guidance

Paper • 2504.14945 • Published Apr 21, 2025 • 88