DeepSeek-Prover-V2
https://github.com/deepseek-ai/deepseek-prover-v2- Category
- AI Tools
- Rank
- No. 1093Tools index
- Pricing
- Open Source
- Type
- TOOL
- Builder
- deepseek-ai
- GitHub
- 1.3k stars
- Date
About
DeepSeek's open-source LLM specialized in formal theorem proving in Lean 4.
What it does
Give it a Lean theorem statement and it generates two linked outputs: a readable proof plan and formal Lean code. Its training process learned this pattern by breaking difficult theorems into smaller subgoals, solving those pieces, recombining their proofs, then applying reinforcement learning with correct-or-incorrect feedback.
Why it's ranked here
The case rests on unusually concrete assets. DeepSeek publishes two model sizes, benchmark results across competition and textbook mathematics, downloadable example proofs, and a separate evaluation dataset. The smaller model also offers a practical entry point, while the much larger model targets stronger proof performance at substantially greater computational scale.
What's good
Subgoal decomposition gives the model a useful bridge between mathematical explanation and machine-checkable proof construction. The training pipeline specifically keeps hard problems whose pieces can be solved even when direct proof search fails. The released ProverBench spans algebra, calculus, analysis, probability, and other areas, making evaluation broader than one competition set.
Tradeoffs
The larger model has 671 billion parameters, so local inference demands are inherently substantial. Even the quick start uses automatic device placement and bfloat16 tensors. It also enables remote model code. Most importantly, the example generates and prints an answer but does not demonstrate compiling the result with Lean, repairing errors, or closing the verification loop.
How to use it well
Use it when you already work with Lean and want a draft proof plan plus candidate formal code for review. Start with the 7 billion parameter model, test outputs in your own Lean environment, and treat acceptance by the proof checker as authoritative. It does not replace theorem formulation, environment setup, proof validation, or an interactive repair workflow.
Technical notes+
README.md documents inference through Hugging Face Transformers using AutoTokenizer.from_pretrained(), AutoModelForCausalLM.from_pretrained(), apply_chat_template(), and generate(). The example requests bfloat16, device_map="auto", trust_remote_code=True, and up to 8192 generated tokens. It loads either the 7B or 671B checkpoint and prompts for a proof plan before Lean 4 code. LICENSE-MODEL contains the custom model agreement, including downstream use restrictions and redistribution duties. DeepSeek_Prover_V2.pdf is the linked paper, figures/performance.png supplies the performance graphic, and minif2f-solutions.zip contains generated benchmark proofs.
Observed
- Model license
- Custom DeepSeek License Agreement with use-based restrictions and redistribution conditions.
- Model distribution
- The 7B and 671B checkpoints are distributed through Hugging Face.
- Inference interface
- Python inference uses Hugging Face Transformers and PyTorch.
- Dataset distribution
- DeepSeek-ProverBench is available as a separate Hugging Face dataset.
- Model variants
- Two parameter scales are offered: 7B and 671B.
- Context support
- The 7B model supports context lengths up to 32K tokens.
Read from README.md, LICENSE-MODEL.
What it can do
Prove mathematical theorems formally
Mathematical theorem statement in Lean 4 syntax → Formal proof in Lean 4
Verify proof correctness
Lean 4 proof code → Verification result and error messages if invalid
Generate proof steps
Theorem statement and partial proof context → Next logical proof steps or tactics
Convert informal mathematical statements to formal logic
Natural language mathematical statement → Lean 4 formalized statement
Complete partial proofs
Incomplete Lean 4 proof with gaps → Complete formal proof
Suggest proof tactics
Current proof state and goal → Recommended Lean 4 tactics to apply
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