Emre Yumlu 
Chinese AI firm DeepSeek has made a bold move in the open-source field by unveiling Prover V2, a large language model (LLM) designed to assist with mathematical theorem verification. With this latest release, DeepSeek aims to push the boundaries of AI-assisted scientific research and education.
Prover V2 Automates Proof Generation
On April 30, DeepSeek released its new model on Hugging Face, a popular open-source AI platform. Distributed under the MIT license, Prover V2 features a staggering 671 billion parameters, placing it well ahead of its earlier versions, Prover V1 and V1.5, both introduced in 2024.
Earlier documentation from the Prover V1 release showed that the model could translate complex math problems into formal logic using the Lean 4 programming language, a tool often used for mathematical proof systems. With Prover V2, DeepSeek expands this capability, enabling the model to compress and generate mathematical knowledge and verify it with higher accuracy.
The Debate Over Open Weights
The open release of LLM weights continues to spark debate within the AI community. By sharing these models publicly, companies make it possible for users to run advanced AI on their own systems — without relying on proprietary infrastructure. However, this openness also introduces concerns about misuse, as no safeguards prevent the model from being used irresponsibly.
Despite the risks, many see DeepSeek’s approach as a victory for AI transparency and accessibility, especially following the footsteps of Meta’s LLaMA models. The move signals growing momentum behind open AI development as a counterweight to more restrictive platforms.
Making AI Models More Accessible
Running massive models like Prover V2 once required extremely high-end hardware. That’s changing, thanks to techniques like quantization and model distillation. Distillation allows a smaller model to learn the behaviors of a larger one, preserving much of the performance while cutting hardware requirements. Quantization, on the other hand, reduces the numerical precision of weights, saving memory and boosting inference speeds.
Prover V2 uses 8-bit quantized weights, allowing the 650 GB model to become more manageable. DeepSeek has previously distilled its R1 model into lighter versions, some with as few as 1.5 billion parameters — low enough to run even on mobile devices in some cases.
This release not only advances AI in math but also continues to democratize powerful AI tools for researchers, developers, and learners around the world.
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