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Goedel Prover
Overview :
Goedel-Prover is an open-source large language model specializing in automated theorem proving. It significantly enhances the efficiency of automated mathematical problem solving by translating natural language mathematical questions into formal languages (such as Lean 4) and generating formal proofs. The model achieved a success rate of 57.6% on the miniF2F benchmark, surpassing other open-source models. Its key advantages include high performance, open-source extensibility, and a deep understanding of mathematical problems. Goedel-Prover aims to advance automated theorem proving technologies and provide powerful tool support for mathematical research and education.
Target Users :
Goedel-Prover is ideal for mathematicians, computer scientists, researchers, and developers interested in automated theorem proving. It provides robust technical support for automated proofs in mathematical research, education, and related fields.
Use Cases
Goedel-Prover achieved a success rate of 57.6% on the miniF2F benchmark, significantly higher than other open-source models.
Goedel-Prover successfully solved 7 problems on the PutnamBench leaderboard, ranking first.
Generated 29.7K formal proofs for the Lean Workbook, nearly doubling previous efforts.
Features
Translates natural language mathematical problems into a formal language (Lean 4).
Generates high-quality formal proofs.
Supports performance evaluation on multiple datasets.
Provides open-source models and datasets for research and extension.
Demonstrates excellent performance on various benchmarks, such as miniF2F and PutnamBench.
Supports multi-GPU parallel inference to improve computational efficiency.
How to Use
1. Clone the repository: `git clone --recurse-submodules https://github.com/Goedel-LM/Goedel-Prover.git`
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2. Install dependencies: `pip install -r requirements.txt`
3. Build Lean 4 and mathlib4: `cd mathlib4 && lake build`
4. Test the installation: Run `python prover/lean/verifier.py` to ensure the environment is set up correctly.
5. Run inference: Use the `sh eval/eval.sh` script, specifying parameters such as the dataset, model path, and output directory.
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