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DeepSeek-Prover-V2 - AI Language Models Tool

Overview

DeepSeek-Prover-V2 is an open-weight AI theorem prover built to bridge informal chain-of-thought reasoning and machine-checkable proofs in Lean 4. The project uses a two-stage training pipeline: a “cold‑start” dataset synthesized by a larger DeepSeek-V3 model that decomposes problems into subgoals, then reinforcement learning on the prover model using binary correct/incorrect verification feedback. The authors release two sizes (7B and 671B parameters) and provide tooling and example prompts to generate Lean 4 proofs and proof plans programmatically via Hugging Face Transformers. According to the project materials, the 671B variant attains state-of-the-art neural theorem proving results (e.g., an 88.9% pass ratio on the MiniF2F-test) and substantially improves Putnam-style performance versus earlier open provers. ([github.com](https://github.com/deepseek-ai/DeepSeek-Prover-V2)) The repository also publishes a new benchmark called ProverBench (325 formalized problems, including 15 adapted from AIME 2024–25) and example solution archives (miniF2F solutions). The code examples show a straightforward Hugging Face inference path (AutoTokenizer/AutoModelForCausalLM) for both interactive proof plans (Chain-of-Thought mode) and fast formal proof generation (non‑CoT mode). Model downloads and dataset links are referenced from the repo and Hugging Face model pages. ([github.com](https://github.com/deepseek-ai/DeepSeek-Prover-V2))

GitHub Statistics

  • Stars: 1,237
  • Forks: 94
  • License: NOASSERTION
  • Last Updated: 2025-07-18T08:11:28Z

The GitHub repository for DeepSeek-Prover-V2 shows strong community interest (about 1.2k stars and ~94 forks) and contains the core README, a PDF paper, and packaged MiniF2F solutions. Activity is light in terms of code churn (the repo contains a small number of commits and a modest issue count), and the project distributes models and datasets via Hugging Face links rather than packaged binaries. The project uses a separate model license (referenced in the repo) rather than a standard permissive OSS license; users should review the repository’s model license before commercial use. ([github.com](https://github.com/deepseek-ai/DeepSeek-Prover-V2))

Installation

Install via pip:

git clone https://github.com/deepseek-ai/DeepSeek-Prover-V2.git
pip install transformers accelerate torch --upgrade
python -c "from transformers import AutoModelForCausalLM, AutoTokenizer; AutoTokenizer.from_pretrained('DeepSeek-Prover-V2-7B')"  # use model id shown in repo / HF

Key Features

  • Cold‑start synthetic dataset created by DeepSeek‑V3 subgoal decomposition and formalization in Lean 4.
  • Reinforcement learning with binary correct/incorrect verification to refine prover behavior.
  • Two model sizes: 7B (32K context) and 671B (high-capacity variant trained on DeepSeek‑V3 base).
  • Chain‑of‑Thought (CoT) mode produces detailed proof plans before Lean code generation.
  • ProverBench: 325 formalized problems (includes 15 AIME 2024–25 problems) and downloadable miniF2F solutions.

Community

DeepSeek‑Prover‑V2 has attracted notable attention from the formal‑math and ML communities (≈1.2k GitHub stars, ~94 forks) and broad coverage in AI outlets praising its MiniF2F and PutnamBench gains. The GitHub repo shows a small number of commits and issues—suggesting the project is research‑focused with limited external contribution so far—and the model is distributed primarily via Hugging Face and project links. Observers have both celebrated the benchmark advances and flagged wider DeepSeek tooling and hardware/deployment issues reported in the press; organizations should review the model license and operational requirements before production use. ([github.com](https://github.com/deepseek-ai/DeepSeek-Prover-V2))

Last Refreshed: 2026-03-03

Key Information

  • Category: Language Models
  • Type: AI Language Models Tool
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