deepseek

Running DeepSeek Prover V2 7B on Linux: A Complete Guide

Anas Mohammad

May 5, 2025 • 5 min read

DeepSeek Prover V2 7B is an open-source large language model designed specifically for formal theorem proving, particularly in the Lean 4 proof assistant language.

It excels at formal mathematical reasoning by generating precise proofs, making it a powerful tool for researchers, educators, and enthusiasts in mathematics and computer science.

This guide will walk you through the entire process of running DeepSeek Prover V2 7B on a Linux environment.

What is DeepSeek Prover V2 7B?

DeepSeek Prover V2 is a state-of-the-art AI model developed by DeepSeek AI, designed to generate formal proofs in Lean 4.

It uses a unique training approach that breaks down complex problems into smaller reasoning steps, combining informal explanations with formal proof construction.

The 7B parameter version is a smaller, more accessible variant compared to the larger 67B model, making it suitable for local deployment on capable hardware.

Key features include:

Formal theorem proving in Lean 4 syntax
High accuracy on mathematical benchmarks (e.g., 88.9% pass rate on MiniF2F)
Open-source availability on Hugging Face and other platforms
Compatibility with popular ML frameworks like PyTorch and transformers.

System Requirements and Prerequisites

Before installing and running DeepSeek Prover V2 7B on Linux, ensure your system meets the following minimum requirements:

Operating System: Ubuntu 22.04 or a similar Debian-based Linux distribution is recommended for ease of setup.
GPU: NVIDIA GPU with at least 32GB VRAM (e.g., RTX A6000 or better) to handle the model efficiently.
CPU: Modern multi-core processor.
RAM: Minimum 32GB system RAM.
Storage: At least 100GB free disk space to store model checkpoints and dependencies.
Python: Python 3.11 or later.
Other software: Git, Jupyter Notebook (optional but recommended for interactive use).

Note: While the 7B model is more accessible than the 67B version, it still requires substantial hardware resources, especially GPU VRAM, for smooth inference.

Setting Up Your Linux Environment

Step 1: Update Your System

Open a terminal and update your package lists:

bashsudo apt update && sudo apt upgrade -y

Step 2: Install Essential Tools

Install Python, Git, and other utilities:

bashsudo apt install -y python3 python3-pip python3-venv git build-essential

If your default Python version is older than 3.11, add the deadsnakes PPA to install a newer version:

bashsudo apt install -y software-properties-common
sudo add-apt-repository -y ppa:deadsnakes/ppa
sudo apt update
sudo apt install -y python3.11 python3.11-venv python3.11-dev

Set Python 3.11 as default if needed:

bashsudo update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.11 1

Step 3: Install NVIDIA Drivers and CUDA Toolkit

Ensure your NVIDIA GPU drivers and CUDA are installed correctly to enable GPU acceleration:

bashsudo apt install -y nvidia-driver-525
sudo reboot

After reboot, verify GPU status:

bashnvidia-smi

Install CUDA toolkit as per NVIDIA instructions for your GPU and Linux distribution.

Installing Dependencies and Setting Up the Project

Step 4: Create a Python Virtual Environment

It is best practice to use a virtual environment to manage dependencies:

bashpython3.11 -m venv deepseek-env
source deepseek-env/bin/activate

Step 5: Install Python Packages

Install PyTorch with CUDA support, transformers, and other required libraries:

bashpip install --upgrade pip
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
pip install transformers>=4.38.0 accelerate>=0.25.0 bitsandbytes>=0.41.0 einops

Step 6: (Optional) Install Jupyter Notebook

For interactive experimentation, install Jupyter Notebook:

bashpip install notebook ipywidgets
jupyter notebook --allow-root

If running on a remote server, set up SSH port forwarding to access the notebook in your local browser.

Downloading and Running DeepSeek Prover V2 7B

Step 7: Download the Model

DeepSeek Prover V2 7B is available on Hugging Face under the repository deepseek-ai/DeepSeek-Prover-V2-7B. You can download and load the model using the transformers library:

pythonfrom transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "deepseek-ai/DeepSeek-Prover-V2-7B" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained( model_id, device_map="auto", torch_dtype=torch.bfloat16, trust_remote_code=True )

This code will automatically download the model weights and tokenizer and load them onto your GPU if available.

Step 8: Running Inference

You can now run the model with your input prompts. For example, to generate a formal proof in Lean 4:

pythonprompt = "prove that for any two sets A and B, their intersection is a subset of A; that is, A ∩ B ⊆ A. Provide the complete Lean 4 proof." inputs = tokenizer(prompt, return_tensors="pt").to(model.device) outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.2) print(tokenizer.decode(outputs[0], skip_special_tokens=True))

This will output a formal proof in Lean 4 syntax, demonstrating the model's reasoning capability.

Using DeepSeek Prover V2 with llama.cpp on Linux

An alternative lightweight method to run the model on Linux is via llama.cpp, a C++ implementation optimized for running LLaMA-based models efficiently on CPU or GPU.

Step 9: Install llama.cpp

Clone the repo and build with CUDA support:

bashgit clone https://github.com/ggerganov/llama.cpp
cd llama.cpp
LLAMA_CUDA=1 make

Step 10: Download GGUF Quantized Model

A quantized version of DeepSeek Prover V2 7B is available as NikolayKozloff/DeepSeek-Prover-V2-7B-Q8_0-GGUF on Hugging Face.

Step 11: Run Inference Using llama.cpp CLI

Run the model with a prompt:

bash./llama-cli --hf-repo NikolayKozloff/DeepSeek-Prover-V2-7B-Q8_0-GGUF --hf-file deepseek-prover-v2-7b-q8_0.gguf -p "The meaning to life and the universe is"

Or start a server for interactive sessions:

bash./llama-server --hf-repo NikolayKozloff/DeepSeek-Prover-V2-7B-Q8_0-GGUF --hf-file deepseek-prover-v2-7b-q8_0.gguf -c 2048

This method is more resource-friendly and works well on Linux systems without heavy GPU requirements.

Tips for Effective Use

Detailed Prompts: DeepSeek Prover V2 performs best with precise, detailed prompts specifying the theorem and desired output format.
Error Handling: Implement retry logic in your scripts, as the model may occasionally return incomplete or empty outputs.
Resource Monitoring: Keep an eye on GPU memory usage with nvidia-smi to avoid out-of-memory errors.
Security: If using API keys or cloud services, never expose credentials publicly.
Experiment with Parameters: Adjust generation parameters like temperature and max tokens to balance creativity and accuracy.

Troubleshooting Common Issues

Model Fails to Load: Ensure you have sufficient VRAM and the correct PyTorch version with CUDA support.
Slow Performance: Check GPU utilization and consider using a more powerful GPU or cloud GPU instances.
Dependency Conflicts: Use a clean virtual environment and install dependencies fresh.
SSH Connection Issues: Verify your SSH keys and port forwarding settings when accessing remote servers.
Jupyter Notebook Access: Use SSH tunneling to forward the notebook port from the remote server to your local machine.

Running DeepSeek Prover V2 7B on Cloud (Optional)

If local hardware is insufficient, consider using cloud GPU providers like NodeShift Cloud, which offers affordable GPU nodes with easy setup for DeepSeek Prover V2. The process involves:

Creating an account on NodeShift
Deploying a GPU node with suitable specs (e.g., NVIDIA A100 or H100)
Connecting via SSH
Setting up the environment and running the model as described above

NodeShift also supports Jupyter notebooks and provides a user-friendly interface for managing GPU resources.

Conclusion

Running DeepSeek Prover V2 7B on Linux enables powerful formal theorem proving capabilities locally or on cloud infrastructure. By following this detailed guide, you can begin generating formal proofs in Lean 4.