DeepSeek Coder: Get Started. Everything to know about DeepSeek Coder and how to get started, today!

What is DeepSeek Coder?

DeepSeek Coder is an open-source code language model developed by DeepSeek AI, designed to assist developers by generating code snippets, offering code completions, and providing solutions across various programming languages.

Trained on a vast dataset comprising 87% code and 13% natural language in both English and Chinese, it aims to enhance coding efficiency and support multilingual development.

Key Features of DeepSeek Coder

• Diverse Model Sizes: DeepSeek Coder is available in multiple configurations, including models with 1.3 billion, 5.7 billion, 6.7 billion, and 33 billion parameters. This range allows developers to select a model that best fits their computational resources and project requirements.
• Extended Context Window: With a context window of up to 16,000 tokens, DeepSeek Coder supports project-level code completion and infilling, enabling it to handle larger codebases and provide more comprehensive assistance.
• Multilingual Support: The model is trained on code and natural language data in both English and Chinese, making it versatile for developers working in these languages.

Who Can Benefit from DeepSeek Coder?

DeepSeek Coder is designed for a wide range of users:

• Professional Developers: It assists in generating code snippets, completing code, and providing solutions across various programming languages, thereby enhancing productivity.
• Educators and Students: The model serves as a valuable learning tool, offering explanations and examples that aid in understanding complex coding concepts.
• Researchers: With its open-source nature, DeepSeek Coder provides a foundation for further exploration in code generation and language modeling.

Use Cases for DeepSeek Coder

• Code Generation: Developers can input prompts to generate code snippets in languages such as Python, JavaScript, and more.
• Code Completion: The model can predict and complete code segments, reducing the time spent on writing repetitive code.
• Code Explanation: It offers explanations for existing code, aiding in code reviews and learning.

Getting Started with DeepSeek Coder: A Sample Project

Imagine you’re developing a Python application and need to implement a quicksort algorithm. DeepSeek Coder can assist you in generating this function efficiently.

Access the Model:

You can access DeepSeek Coder through platforms like Hugging Face.

Set Up the Environment:

Install the necessary dependencies:

bash pip install transformers torch

Load the Model:

Use the following Python code to load the model and tokenizer:

from transformers import AutoTokenizer, AutoModelForCausalLM import torch tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/deepseek-coder-6.7b-base", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("deepseek-ai/deepseek-coder-6.7b-base", trust_remote_code=True, torch_dtype=torch.bfloat16).cuda()

Generate the Quicksort Function:

Define your input prompt and generate the code:

input_text = "# Write a quicksort algorithm in Python" inputs = tokenizer(input_text, return_tensors="pt").to(model.device) outputs = model.generate(**inputs, max_length=128) generated_code = tokenizer.decode(outputs[0], skip_special_tokens=True) print(generated_code)

The model will output a Python implementation of the quicksort algorithm based on your prompt.

DeepSeek Coder stands as a powerful tool for developers, educators, and researchers, offering advanced code generation capabilities across multiple programming languages. Its open-source nature and diverse model configurations make it a versatile asset in various coding and educational scenarios.