How to Locally Deploy DeepSeek

Introduction

DeepSeek, a powerful artificial intelligence model, offers remarkable capabilities in various fields such as natural language processing and coding assistance. Locally deploying DeepSeek allows users to have more control over data privacy and potentially achieve faster response times. This article will guide you through the steps of locally deploying DeepSeek, specifically taking DeepSeek Coder as an example.

Prerequisites

Hardware Requirements

Since large - language models demand significant computational resources, it is recommended to have an NVIDIA GPU with at least 8GB of video memory (for running smaller - scale models), such as an NVIDIA RTX 3080. A CPU with multiple cores and good performance, along with at least 16GB of RAM, is also required.

Software Requirements

Install a Linux system (e.g., Ubuntu 20.04 or later), Python 3.8 or higher, CUDA (if using an NVIDIA GPU), and cuDNN.

Download the Model

Obtain the DeepSeek model files from the official channels or legally authorized sources. For example, DeepSeek Coder comes in different parameter - scale versions.

Environment Setup

Install Dependencies

Open the terminal and use the following commands to create and activate a virtual environment, and then install the required Python libraries:

bash

# Create a virtual environment
python -m venv deepseek_env
# Activate the virtual environment
source deepseek_env/bin/activate
# Install PyTorch (choose the appropriate PyTorch version according to your CUDA version)
pip install torch torchvision torchaudio
# Install other dependencies, such as transformers
pip install transformers accelerate sentencepiece

Model Deployment

Write Python Code to Load the Model

Create a Python file, for example, load_deepseek.py, and write the following code:

python

from transformers import AutoTokenizer, AutoModelForCausalLM

# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained("deepseek - ai/deepseek - coder - 1.3b", trust_remote_code=True)
# Load the model
model = AutoModelForCausalLM.from_pretrained("deepseek - ai/deepseek - coder - 1.3b", trust_remote_code=True).cuda()

# Example input
input_text = "print('Hello, World!')"
# Tokenize the input
inputs = tokenizer(input_text, return_tensors="pt").to(model.device)
# Generate output
outputs = model.generate(**inputs, max_new_tokens=100, do_sample=True, top_p=0.95, temperature=0.8)
# Decode the output
output_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(output_text)

In this code, we use the transformers library to load the DeepSeek Coder model and its corresponding tokenizer, and then perform a simple text - generation example. Note that "deepseek - ai/deepseek - coder - 1.3b" is the model name. If you download a different version of the model, you need to modify this name accordingly.

Run the Code

Execute the following command in the terminal to run the Python code:

bash

python load_deepseek.py

Notes

• Model Scale: If your hardware resources are limited, it is advisable to choose a model with a smaller parameter scale, such as the 1.3B version in the example. If you have sufficient hardware resources, you can try larger - scale models, but they may require more video memory and computational resources.
• Data Security: When deploying the model locally, pay attention to protecting the security of the model files and user data to avoid data leakage and malicious attacks.
• License: Ensure that you comply with the usage license requirements of the DeepSeek model and use the model legally.