How to Use DeepSeek AI for Coding: A Developer’s Guide

Are you looking to boost your coding efficiency? DeepSeek AI is a powerful tool designed to help developers write, debug, and complete code faster. This guide will walk you through everything you need to know about using DeepSeek AI for coding, from understanding its features to implementing it in your projects.

DeepSeek AI offers state-of-the-art performance in code completion, insertion, and even repository-level understanding. Whether you’re a seasoned developer or just starting, DeepSeek AI can significantly improve your workflow. Let’s dive in and explore how to use DeepSeek AI for coding effectively.

What is DeepSeek AI?

DeepSeek AI is a series of code language models created to assist with coding tasks. These models are trained from scratch on a massive dataset of code and natural language. This allows them to understand and generate code in various programming languages.

Think of DeepSeek AI as your intelligent coding partner. It can help you write code more efficiently, find and fix bugs, and even understand complex codebases. It’s like having an expert programmer available to assist you at any time.

Key Features of DeepSeek AI

• Massive Training Data: Trained on 2 trillion tokens, including 87% code and 13% natural language. This extensive training enables DeepSeek AI to understand a wide range of coding patterns and styles.
• Flexible and Scalable: Available in various sizes (1B to 33B parameters), allowing you to choose the model that best fits your hardware and performance needs.
• Superior Performance: Achieves state-of-the-art results on benchmarks like HumanEval, MultiPL-E, and MBPP.
• Advanced Code Completion: Supports project-level code completion and infilling with a 16K window size.
• Broad Language Support: Supports a wide array of programming languages.

These features make DeepSeek AI a versatile tool for various coding tasks. From simple code completion to complex project-level understanding, DeepSeek AI is designed to enhance your coding experience.

Setting Up DeepSeek AI for Coding

Before you can start using DeepSeek AI, you need to set it up on your system. This involves installing the necessary dependencies and configuring the model for your specific use case.

The setup process might seem a bit technical, but don’t worry! We’ll break it down into simple steps. By the end of this section, you’ll have DeepSeek AI up and running on your machine.

Installing Dependencies

First, you need to install the required Python packages. This can be done using pip, the Python package installer.

Open your terminal or command prompt and run the following command:

pip install -r requirements.txt

This command will install all the packages listed in the requirements.txt file. Make sure you have this file in your current directory. If you don’t have it, you can create one with the necessary packages, such as transformers and torch.

Note: Ensure you have Python installed on your system before running this command. Python 3.7 or higher is recommended.

Downloading the Model

Next, you need to download the DeepSeek AI model. You can download the models from the Hugging Face Model Hub.

Here’s how you can download and use a pre-trained model:

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()

This code snippet downloads the deepseek-coder-6.7b-base model and its tokenizer. The trust_remote_code=True argument allows the execution of custom code within the model, which is necessary for some models. The torch_dtype=torch.bfloat16 argument specifies the data type to use for the model, which can help reduce memory usage.

Reminder: Make sure you have enough disk space to download the model. The size of the model can vary from 1GB to 33GB, depending on the version you choose.

Configuring the Model

Once you have downloaded the model, you need to configure it for your specific use case. This involves setting the appropriate parameters and loading the model into your code.

Here’s an example of how to configure the model for code completion:

input_text = "#write a quick sort algorithm"
inputs = tokenizer(input_text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_length=128)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

In this example, we provide a prompt (#write a quick sort algorithm) to the model. The model then generates code to complete the prompt. The max_length parameter controls the length of the generated code.

Note: Experiment with different prompts and parameters to get the best results for your specific coding task.

Using DeepSeek AI for Different Coding Tasks

DeepSeek AI can be used for a variety of coding tasks, including code completion, code insertion, and chat-based interaction. Let’s explore each of these use cases in more detail.

Each coding task requires a slightly different approach. By understanding how to use DeepSeek AI for each task, you can maximize its potential and improve your coding workflow.

Code Completion

Code completion is one of the most common use cases for DeepSeek AI. It allows you to quickly generate code snippets based on a given prompt.

Here’s an example of how to use DeepSeek AI for code completion:

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()

input_text = "#write a quick sort algorithm"
inputs = tokenizer(input_text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_length=128)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

This code will output the following result:

def quick_sort(arr):
    if len(arr) <= 1:
        return arr
    pivot = arr[0]
    left = []
    right = []
    for i in range(1, len(arr)):
        if arr[i] < pivot:
            left.append(arr[i])
        else:
            right.append(arr[i])
    return quick_sort(left) + [pivot] + quick_sort(right)

As you can see, DeepSeek AI has generated a quick sort algorithm based on the prompt. This can save you a lot of time and effort, especially when writing complex algorithms.

Reminder: The quality of the generated code depends on the prompt you provide. Be as specific as possible to get the best results.

Code Insertion

Code insertion allows you to fill in missing parts of your code. This is useful when you have a code structure but need help completing the implementation.

Here’s an example of how to use DeepSeek AI for code insertion:

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()

input_text = """<｜fim begin｜>def quick_sort(arr):
    if len(arr) <= 1:
        return arr
    pivot = arr[0]
    left = []
    right = []
    <｜fim hole｜>
    if arr[i] < pivot:
        left.append(arr[i])
    else:
        right.append(arr[i])
    return quick_sort(left) + [pivot] + quick_sort(right)<｜fim end｜>"""
inputs = tokenizer(input_text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_length=128)
print(tokenizer.decode(outputs[0], skip_special_tokens=True)[len(input_text):])

This code will output the following result:

for i in range(1, len(arr)):

In this example, we provide a code snippet with a missing part (<｜fim hole｜>). DeepSeek AI fills in the missing part, completing the code snippet. This can be very helpful when you’re working on a large project and need assistance with specific parts of the code.

Note: The <｜fim begin｜>, <｜fim hole｜>, and <｜fim end｜> tags are special tokens that tell the model where the missing part is located.

Chat Model Inference

DeepSeek AI can also be used as a chat model, allowing you to interact with it using natural language. This is useful for asking questions, getting explanations, and generating code snippets.

Here’s an example of how to use DeepSeek AI as a chat model:

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

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

messages=[
    { 'role': 'user', 'content': "write a quick sort algorithm in python."}
]
inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
# tokenizer.eos_token_id is the id of <|EOT|> token
outputs = model.generate(inputs, max_new_tokens=512, do_sample=False, top_k=50, top_p=0.95, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id)
print(tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True))

This code will output the following result:

Sure, here is a simple implementation of the Quick Sort algorithm in Python:

def quick_sort(arr):
    if len(arr) <= 1:
        return arr
    else:
        pivot = arr[0]
        less_than_pivot = [x for x in arr[1:] if x <= pivot]
        greater_than_pivot = [x for x in arr[1:] if x > pivot]
        return quick_sort(less_than_pivot) + [pivot] + quick_sort(greater_than_pivot)

# Test the function
arr = [10, 7, 8, 9, 1, 5]
print("Original array:", arr)
print("Sorted array:", quick_sort(arr))

This code works by selecting a 'pivot' element from the array and partitioning the other elements into two sub-arrays, according to whether they are less than or greater than the pivot. The pivot element is then in its final position. The process is then repeated for the sub-arrays.

In this example, we ask DeepSeek AI to write a quick sort algorithm in Python. The model provides a complete implementation of the algorithm, along with an explanation of how it works. This can be very helpful for learning new concepts and getting assistance with complex coding tasks.

Reminder: The deepseek-coder-6.7b-instruct model is specifically designed for chat-based interaction. Make sure you use this model when using DeepSeek AI as a chat model.

Repository Level Code Completion

DeepSeek AI shines when it comes to understanding and completing code at the repository level. This means it can analyze multiple files within a project and provide context-aware suggestions.

Here’s an example demonstrating how DeepSeek AI can complete the main function in main.py, utilizing functions from utils.py and the IrisClassifier class from model.py:

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()

input_text = """#utils.py
import torch
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import accuracy_score

def load_data():
    iris = datasets.load_iris()
    X = iris.data
    y = iris.target
    # Standardize the data
    scaler = StandardScaler()
    X = scaler.fit_transform(X)
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
    # Convert numpy data to PyTorch tensors
    X_train = torch.tensor(X_train, dtype=torch.float32)
    X_test = torch.tensor(X_test, dtype=torch.float32)
    y_train = torch.tensor(y_train, dtype=torch.int64)
    y_test = torch.tensor(y_test, dtype=torch.int64)
    return X_train, X_test, y_train, y_test

def evaluate_predictions(y_test, y_pred):
    return accuracy_score(y_test, y_pred)

# model.py
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader, TensorDataset

class IrisClassifier(nn.Module):
    def __init__(self):
        super(IrisClassifier, self).__init__()
        self.fc = nn.Sequential(
            nn.Linear(4, 16),
            nn.ReLU(),
            nn.Linear(16, 3)
        )

    def forward(self, x):
        return self.fc(x)

    def train_model(self, X_train, y_train, epochs, lr, batch_size):
        criterion = nn.CrossEntropyLoss()
        optimizer = optim.Adam(self.parameters(), lr=lr)
        # Create DataLoader for batches
        dataset = TensorDataset(X_train, y_train)
        dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True)
        for epoch in range(epochs):
            for batch_X, batch_y in dataloader:
                optimizer.zero_grad()
                outputs = self(batch_X)
                loss = criterion(outputs, batch_y)
                loss.backward()
                optimizer.step()

    def predict(self, X_test):
        with torch.no_grad():
            outputs = self(X_test)
            _, predicted = outputs.max(1)
            return predicted.numpy()

# main.py
from utils import load_data, evaluate_predictions
from model import IrisClassifier as Classifier

def main():
    # Model training and evaluation
    """
inputs = tokenizer(input_text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=140)
print(tokenizer.decode(outputs[0]))

DeepSeek AI effectively calls the IrisClassifier class and its methods from model.py, and uses functions from utils.py, to complete the main function in main.py for model training and evaluation.

Fine-Tuning DeepSeek AI

While the pre-trained DeepSeek AI models are powerful, you can further improve their performance by fine-tuning them on your specific dataset. Fine-tuning involves training the model on a smaller, more specific dataset to adapt it to your particular coding style and requirements.

Fine-tuning can significantly improve the accuracy and relevance of the generated code. It’s like teaching DeepSeek AI your specific coding preferences and patterns.

Preparing Your Data

Before you can fine-tune DeepSeek AI, you need to prepare your data. This involves creating a dataset of code examples that are representative of your coding style and requirements.

Each example in the dataset should consist of an instruction and an output. The instruction is a prompt that you would provide to the model, and the output is the expected code that the model should generate.

Here’s an example of a data point in the dataset:

{
    "instruction": "write a function to calculate the factorial of a number",
    "output": "def factorial(n):\n    if n == 0:\n        return 1\n    else:\n        return n * factorial(n-1)"
}

Note: The quality of your dataset is crucial for fine-tuning. Make sure your examples are accurate, representative, and diverse.

Fine-Tuning Process

Once you have prepared your data, you can start the fine-tuning process. This involves using a training script to train the model on your dataset.

DeepSeek AI provides a script called finetune_deepseekcoder.py for fine-tuning the models. This script supports training with DeepSpeed, a library that enables efficient training of large models.

Here’s an example of how to run the fine-tuning script:

deepspeed finetune_deepseekcoder.py \
    --model_name_or_path deepseek-ai/deepseek-coder-6.7b-instruct \
    --data_path your_data_path \
    --output_dir your_output_path \
    --num_train_epochs 3 \
    --model_max_length 1024 \
    --per_device_train_batch_size 16 \
    --per_device_eval_batch_size 1 \
    --gradient_accumulation_steps 4 \
    --evaluation_strategy "no" \
    --save_strategy "steps" \
    --save_steps 100 \
    --save_total_limit 100 \
    --learning_rate 2e-5 \
    --warmup_steps 10 \
    --logging_steps 1 \
    --lr_scheduler_type "cosine" \
    --gradient_checkpointing True \
    --report_to "tensorboard" \
    --deepspeed configs/ds_config_zero3.json \
    --bf16 True

This command runs the finetune_deepseekcoder.py script with the specified parameters. You need to replace your_data_path and your_output_path with the actual paths to your data and output directories.

Reminder: Fine-tuning can be computationally intensive. Make sure you have access to a powerful GPU and sufficient memory before starting the fine-tuning process.

Tips for Optimizing Your DeepSeek AI Experience

To get the most out of DeepSeek AI, here are some tips and best practices:

• Use Clear and Specific Prompts: The more specific your prompt, the better the results.
• Experiment with Different Models: Try different sizes and versions of DeepSeek AI to find the one that works best for your needs.
• Fine-Tune for Specific Tasks: Fine-tuning can significantly improve the accuracy and relevance of the generated code.
• Keep Your Code Clean and Readable: DeepSeek AI can help you write code, but it’s still important to follow good coding practices.
• Stay Updated: DeepSeek AI is constantly evolving. Keep an eye on the official GitHub page and community forums for updates and new features.

By following these tips, you can maximize the potential of DeepSeek AI and improve your coding workflow.

DeepSeek AI vs. Competitors

DeepSeek AI is not the only AI coding assistant available. Other popular options include GitHub Copilot and Tabnine. Let’s compare DeepSeek AI with these competitors.

Each AI coding assistant has its strengths and weaknesses. By understanding these differences, you can choose the one that best fits your needs.

Feature	DeepSeek AI	GitHub Copilot	Tabnine
Cost	Free (Open Source)	Subscription-based	Free and Paid Plans
Offline Functionality	Yes	No	Limited
Performance	State-of-the-art	Excellent	Good
Customization	High (Fine-tuning)	Limited	Moderate
Community Support	Growing	Large	Large

As you can see, DeepSeek AI offers several advantages over its competitors, including being free and open source, offering offline functionality, and providing high customization through fine-tuning.

Inference with vLLM

For high-throughput inference, you can use vLLM (Very Large Language Model). vLLM is a fast and easy-to-use library for LLM inference.

Here’s an example of how to use vLLM with DeepSeek AI for text completion:

from vllm import LLM, SamplingParams

tp_size = 4  # Tensor Parallelism
sampling_params = SamplingParams(temperature=0.7, top_p=0.9, max_tokens=100)
model_name = "deepseek-ai/deepseek-coder-6.7b-base"
llm = LLM(model=model_name, trust_remote_code=True, gpu_memory_utilization=0.9, tensor_parallel_size=tp_size)

prompts = [
    "If everyone in a country loves one another,",
    "The research should also focus on the technologies",
    "To determine if the label is correct, we need to"
]

outputs = llm.generate(prompts, sampling_params)
generated_text = [output.outputs[0].text for output in outputs]
print(generated_text)

This code uses vLLM to generate text based on the given prompts. The SamplingParams object controls the generation parameters, such as temperature and top_p. The tensor_parallel_size parameter specifies the number of GPUs to use for inference.

Note: vLLM requires a compatible GPU and sufficient memory. Make sure you have the necessary hardware before using vLLM.

Frequently Asked Questions (FAQs)

What programming languages does DeepSeek AI support?

DeepSeek AI supports a wide range of programming languages, including Python, Java, C++, JavaScript, and many more. For a complete list, refer to the official documentation.

Can I use DeepSeek AI for commercial purposes?

Yes, DeepSeek AI is open source and free for both research and commercial use.

What are the hardware requirements for running DeepSeek AI locally?

The hardware requirements depend on the size of the model you choose. Smaller models (1B to 7B parameters) can run on a consumer GPU with 8GB of VRAM. Larger models (33B parameters) require a more powerful GPU with 24GB of VRAM or more.

How do I update DeepSeek AI to the latest version?

To update DeepSeek AI, simply download the latest version from the official GitHub repository and reinstall it.

Where can I find more resources and support for DeepSeek AI?

You can find more resources and support for DeepSeek AI on the official GitHub repository, community forums, and documentation.

Conclusion

DeepSeek AI is a powerful tool that can significantly enhance your coding workflow. From code completion to chat-based interaction, DeepSeek AI offers a wide range of features to assist you with your coding tasks. By understanding how to set up, configure, and use DeepSeek AI effectively, you can unlock its full potential and become a more efficient and productive developer.

Whether you’re a seasoned programmer or just starting, DeepSeek AI can help you write code faster, find and fix bugs, and understand complex codebases. So why not give it a try and see how it can improve your coding experience?