Step 2: Load the DeepSeek-R1 model weights
Posted: Mon Feb 10, 2025 6:12 am
1. Install Python and Pip : Make sure you have Python 3.8 or higher installed. You can download it from the official Python website.
2. Set up a virtual environment : Use venvor condato create an isolated environment for your project.
python -m venv deepseek-env
source deepseek-envbinactivate
3. Install PyTorch or TensorFlow : Depending on your preference, install the deep learning framework.
pip install torch torchvision torchaudio
4. Install additional dependencies : Install libraries such panama mobile database as NumPy, Pandas, and Transformers.
pip install numpy pandas transformers
Next, you will need to download the DeepSeek-R1 model weights . These are usually available from the official repository or a trusted source.
1. Clone the repository : Use Git to clone the official DeepSeek-R1 repository.
git clone s:githubomdeepseek-aiDeepSeek-R1it
cd DeepSeek-R1
2. Download the model weights : Follow the instructions in the repository to download the pre-trained weights.
Step 3: Setting up the model
Once you have loaded the scales, you need to customize the model for your specific use case.
2. Set up a virtual environment : Use venvor condato create an isolated environment for your project.
python -m venv deepseek-env
source deepseek-envbinactivate
3. Install PyTorch or TensorFlow : Depending on your preference, install the deep learning framework.
pip install torch torchvision torchaudio
4. Install additional dependencies : Install libraries such panama mobile database as NumPy, Pandas, and Transformers.
pip install numpy pandas transformers
Next, you will need to download the DeepSeek-R1 model weights . These are usually available from the official repository or a trusted source.
1. Clone the repository : Use Git to clone the official DeepSeek-R1 repository.
git clone s:githubomdeepseek-aiDeepSeek-R1it
cd DeepSeek-R1
2. Download the model weights : Follow the instructions in the repository to download the pre-trained weights.
Step 3: Setting up the model
Once you have loaded the scales, you need to customize the model for your specific use case.