DeepSeek-Prover-V2 - AI Language Models Tool

Overview

DeepSeek-Prover-V2 is an open-source neural theorem prover targeted at formal mathematical reasoning in Lean 4. The project uses a two-stage training pipeline: (1) a cold-start data synthesis phase that prompts DeepSeek-V3 to decompose hard theorems into subgoals and pair formal Lean 4 proofs with chain-of-thought sketches, and (2) a reinforcement-learning fine-tuning stage using binary correctness rewards to improve end-to-end proof synthesis. ([github.com](https://github.com/deepseek-ai/DeepSeek-Prover-V2)) The release provides two model sizes for different use cases: a 7B-parameter variant (built on Prover‑V1.5) with an extended 32K-token context window for lightweight or recursive-subgoal search, and a MoE-style 671B variant trained on top of DeepSeek‑V3 for high-performance formal proving. DeepSeek reports strong benchmark results (88.9% pass ratio on the MiniF2F-test and 49/658 solved on PutnamBench) and ships a new ProverBench dataset of 325 formalized problems (including 15 AIME problems) to evaluate generalization. The models and dataset are available on Hugging Face for direct use with Huggingface Transformers. ([github.com](https://github.com/deepseek-ai/DeepSeek-Prover-V2))

GitHub Statistics

• Stars: 1,225
• Forks: 93
• License: NOASSERTION
• Last Updated: 2025-07-18T08:11:28Z

Repository activity and community signals show strong interest but limited direct repository development. The GitHub repo lists about 1.2k stars and 93 forks, with 11 open issues and 2 open pull requests visible on the main page. The project tree is small (the README, a PDF, a model license file, and a ZIP of miniF2F solutions), and the GitHub history shows only a couple of commits, indicating the release materials were posted but the codebase has limited ongoing commit activity. Users should treat the repo as a research/model release rather than a rapidly iterating engineering project; reproduce evaluations and read the Model License before commercial usage. ([github.com](https://github.com/deepseek-ai/DeepSeek-Prover-V2))

Installation

Install via pip:

pip install torch transformers accelerate safetensors huggingface_hub

huggingface-cli login  # authenticate if model access requires it

python -c "from transformers import AutoModelForCausalLM, AutoTokenizer; AutoTokenizer.from_pretrained('deepseek-ai/DeepSeek-Prover-V2-7B'); AutoModelForCausalLM.from_pretrained('deepseek-ai/DeepSeek-Prover-V2-7B', trust_remote_code=True)"

Key Features

• Cold-start data synthesis: DeepSeek‑V3 decomposes theorems into subgoals to create paired informal/formal training data.
• Reinforcement learning stage: binary correct/incorrect reward fine-tuning to improve formal proof success rates.
• Two model sizes: 7B (32K token context) for searches; 671B MoE for high-performance theorem proving.
• Strong benchmark performance: reported 88.9% MiniF2F pass ratio and 49/658 PutnamBench solves.
• ProverBench dataset: 325 formalized problems (15 from AIME 24&25) for evaluation and reproducibility.

Community

Interest is high (≈1.2k stars, 93 forks) but developer-side activity is modest (few commits, issues and PRs exist on the repo). The models are mirrored on Hugging Face and by community converters; several media and blog posts cover the release. Independent reviewers and some Lean community members have raised concerns about formalization details and whether some proofs rely on implicit placeholders (e.g., reported discussions on Lean Zulip / community commentary), so practitioners are advised to verify proofs and reproduce benchmarks locally. ([github.com](https://github.com/deepseek-ai/DeepSeek-Prover-V2))

Key Information

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