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

Libraries

How to use sail/Qwen2.5-Math-7B-Oat-Zero with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("sail/Qwen2.5-Math-7B-Oat-Zero")
model = AutoModelForCausalLM.from_pretrained("sail/Qwen2.5-Math-7B-Oat-Zero")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use sail/Qwen2.5-Math-7B-Oat-Zero with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/sail/Qwen2.5-Math-7B-Oat-Zero

SGLang

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

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

Qwen2.5-Math-7B-Oat-Zero

Introduction

This model is trained by the minimalist R1-Zero recipe introduced in our paper:

Algorithm: Dr. DRPO
Data: level 3-5 questions from MATH dataset
Base model: Qwen/Qwen2.5-Math-7B
Template: Qwen-Math

Evaluation results on widely used math benchmarks are shown below:

Usage

import vllm


def apply_qwen_math_template(question: str):
    return (
        "<|im_start|>system\nPlease reason step by step, and put your final answer within \\boxed{}.<|im_end|>\n<|im_start|>user\n"
        + question
        + "<|im_end|>\n<|im_start|>assistant\n"
    )

def apply_r1_template(question: str):
    return (
        "A conversation between User and Assistant. The User asks a question, and the Assistant solves it. The Assistant first thinks about the reasoning process in the mind and then provides the User with the answer. "
        "The reasoning process is enclosed within <think> </think> and answer is enclosed within <answer> </answer> tags, respectively, i.e., <think> reasoning process here </think> <answer> answer here </answer>.\nUser: "
        + question
        + "\nAssistant: <think>"
    )

model_name = "sail/Qwen2.5-Math-7B-Oat-Zero"

sampling_params = vllm.SamplingParams(
    n=1,
    temperature=0,
    top_p=1,
    max_tokens=3000,
)

model = vllm.LLM(
    model_name,
    max_model_len=4096,
    dtype="bfloat16",
    enable_prefix_caching=True,
)

if "Llama-3.2-3B-Oat-Zero" in model_name:
    apply_template = apply_r1_template
else:
    apply_template = apply_qwen_math_template

prompts = [
    "How many positive whole-number divisors does 196 have?"
]
prompts = list(map(apply_template, prompts))
outputs = model.generate(prompts, sampling_params)

print(outputs)

Citation

@article{liu2025understanding,
  title={Understanding r1-zero-like training: A critical perspective},
  author={Liu, Zichen and Chen, Changyu and Li, Wenjun and Qi, Penghui and Pang, Tianyu and Du, Chao and Lee, Wee Sun and Lin, Min},
  journal={arXiv preprint arXiv:2503.20783},
  year={2025}
}