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

Libraries

How to use openbmb/RLPR-Qwen2.5-7B-Base with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("openbmb/RLPR-Qwen2.5-7B-Base")
model = AutoModelForCausalLM.from_pretrained("openbmb/RLPR-Qwen2.5-7B-Base")
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 openbmb/RLPR-Qwen2.5-7B-Base with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/openbmb/RLPR-Qwen2.5-7B-Base

SGLang

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

Docker Model Runner
How to use openbmb/RLPR-Qwen2.5-7B-Base with Docker Model Runner:
```
docker model run hf.co/openbmb/RLPR-Qwen2.5-7B-Base
```

Model Card for RLPR-Qwen2.5-7B-Base

GitHub | Paper

RLPR-Qwen2.5-7B-Base is trained from Qwen2.5-7B-Base with the RLPR framework, which eliminates reliance on external verifiers and is simple and generalizable for more domains.

Model Details

Key Features

💡 Verifier-Free Reasoning Enhancement: RLPR pioneers reinforcement learning for reasoning tasks by leveraging the LLM's intrinsic generation probability as a direct reward signal. This eliminates the need for external verifiers and specialized fine-tuning, offering broad applicability and effectively handling complex, diverse answers.
🛠️ Innovative Reward & Training Framework:
- Features a robust Probability-based Reward (PR) using average decoding probabilities of reference answers for higher quality, debiased reward signals, outperforming naive sequence likelihood.
- Implements an standard deviation filtering mechanism that dynamically filters prompts to stabilize training and significantly boost final performance.
🚀 Strong Performance in General & Mathematical Reasoning: Demonstrates substantial reasoning improvements across diverse benchmarks (e.g., 56.0 on MMLU-Pro, 55.4 on TheoremQA with Qwen2.5-7B). RLPR surpasses strong models reliant on external verifiers (like General Reasoner-7B).

Model Description

Trained from model: Qwen2.5-7B
Trained on data: RLPR-Train

Usage

Usage adopted from Qwen2.5-7B-Instruct

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "openbmb/RLPR-Qwen2.5-7B-Base"

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)

prompt = "How much energy is produced when the sun converts one kg of hydrogen into helium?."
messages = [
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=512
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]

Citation

If you find our model/code/paper helpful, please consider citing our papers 📝:

@misc{yu2025rlprextrapolatingrlvrgeneral,
      title={RLPR: Extrapolating RLVR to General Domains without Verifiers}, 
      author={Tianyu Yu and Bo Ji and Shouli Wang and Shu Yao and Zefan Wang and Ganqu Cui and Lifan Yuan and Ning Ding and Yuan Yao and Zhiyuan Liu and Maosong Sun and Tat-Seng Chua},
      year={2025},
      eprint={2506.18254},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2506.18254}, 
}

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Safetensors

Model size

8B params

Tensor type

BF16

Collection including openbmb/RLPR-Qwen2.5-7B-Base

RLPR

Collection

Extrapolating RLVR to General Domains without Verifiers • 6 items • Updated 6 days ago • 6

Paper for openbmb/RLPR-Qwen2.5-7B-Base

RLPR: Extrapolating RLVR to General Domains without Verifiers

Paper • 2506.18254 • Published Jun 23, 2025 • 35