Instructions to use FrenzyMath/REAL-Prover with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FrenzyMath/REAL-Prover with Transformers:

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

pipe = pipeline("text-generation", model="FrenzyMath/REAL-Prover")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("FrenzyMath/REAL-Prover")
model = AutoModelForCausalLM.from_pretrained("FrenzyMath/REAL-Prover")
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 FrenzyMath/REAL-Prover with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/FrenzyMath/REAL-Prover

SGLang

How to use FrenzyMath/REAL-Prover 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 "FrenzyMath/REAL-Prover" \
    --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": "FrenzyMath/REAL-Prover",
		"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 "FrenzyMath/REAL-Prover" \
        --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": "FrenzyMath/REAL-Prover",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use FrenzyMath/REAL-Prover with Docker Model Runner:
```
docker model run hf.co/FrenzyMath/REAL-Prover
```

REAL-Prover: Retrieval Augmented Lean Prover for Mathematical Reasoning

1. Overview

REAL-Prover is a retrieval-augmented, stepwise large language model-based theorem prover for Lean 4. It is built on top of Qwen2.5-Math-7B, and fine-tuned using our custom pipeline and dataset to support formal reasoning in Lean. The model is integrated with a retrieval module (Leansearch-PS) that fetches relevant theorems from the Lean math library to enhance generation quality.

Our approach introduces several innovations:

A retrieval-enhanced generation pipeline
A custom interactive environment (Jixia-interactive) for data synthesis
Evaluations on both ProofNet and our proposed benchmark FATE-M

2. Associated Paper

REAL-Prover: Retrieval Augmented Lean Prover for Mathematical Reasoning
arXiv:2505.20613

3. Running Experiments

Repository: https://github.com/frenzymath/REAL-Prover

Input Data Format

Each input data should be a JSONL line with two keys: id and formal_statement. See data/minif2f_test.jsonl for examples.

Running

Create a runtime configuration file (in TOML format) in ./experiment/examples/, then run:

cd Realprover
python -m experiment.run /path/to/config.toml

4. Citation

@misc{realprover2025,
      title={REAL-Prover: Retrieval Augmented Lean Prover for Mathematical Reasoning}, 
      author={Ziju Shen and Naohao Huang and Fanyi Yang and Yutong Wang and Guoxiong Gao and Tianyi Xu and Jiedong Jiang and Wanyi He and Pu Yang and Mengzhou Sun and Haocheng Ju and Peihao Wu and Bryan Dai and Bin Dong},
      year={2025},
      eprint={2505.20613},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2505.20613}, 
}