# deepseek-ai/DeepSeek-Prover-V2

Repository: https://github.com/deepseek-ai/DeepSeek-Prover-V2
Canonical: https://ross.abutalabs.com/products/deepseek-prover-v2
License: NOASSERTION
License Family: other
Last push: 2025-07-18T08:11:37+00:00

## Health v2 (maintenance only)
Score: 34/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 32, release rhythm 35, longevity 35
- inputs: {"age_days": 490, "days_push": 411, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1297, forks 107 (observed 2026-08-28T04:04:16.981161+00:00)

## What it is
DeepSeek-Prover-V2 is an open-source large language model for formal theorem proving in Lean 4, trained via reinforcement learning with subgoal decomposition seeded by DeepSeek-V3. The repository provides model weights, the ProverBench dataset, and quick-start instructions for inference.

## Use cases
- prove theorems automatically in Lean 4
- generate formal mathematical proofs with an LLM
- benchmark LLMs on formal math reasoning
- train or fine-tune a theorem-proving model
- decompose complex math problems into subgoals

## When to choose
- you need automated or assisted formal proof generation in Lean 4
- you want an open model specialized in formal mathematics
- you need a benchmark dataset for theorem proving research

## When to avoid
- you need general-purpose chat or coding assistance
- you work in informal mathematics without a proof assistant
- you cannot host large GPU models locally

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, llm-training, reinforcement-learning, nlp
- domain: artificial-intelligence, large-language-models, mathematics, deep-learning
- platform: python
- tags: theorem-proving, lean4, formal-mathematics, open-weights, dataset, proverbench, gpu, linux

## Member repositories
- deepseek-ai/DeepSeek-Prover-V2 (main) score 34

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:16.981161+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T04:53:52.835789+00:00, confidence not recorded.
  - readme: https://github.com/deepseek-ai/DeepSeek-Prover-V2 (fetched 2026-08-28T04:04:16.981161+00:00, sha b3780ba7db93)
- Data as of 2026-08-30T08:39:29.467469+00:00.