Introducing DeepSeek Coder: The Next Generation AI-Powered Code Language Model for Developers

In the ever-evolving landscape of software development, staying ahead of the curve is paramount. Today, we are thrilled to unveil DeepSeek Coder, a groundbreaking AI-powered code language model designed to revolutionize the way developers write, debug, and optimize code. Whether you're a seasoned developer, a data scientist, or an AI enthusiast, DeepSeek Coder is poised to become your indispensable coding companion.

What is DeepSeek Coder?

DeepSeek Coder is an advanced suite of code language models meticulously trained on an expansive dataset of 2 trillion tokens, comprising 87% code and 13% natural language in both English and Chinese. This unique composition ensures that DeepSeek Coder not only understands programming languages but also comprehends the nuances of natural language, making it exceptionally versatile for various coding tasks.

Key Specifications:

• Training Data: 2 trillion tokens (87% code, 13% natural language)

• Languages Supported: English and Chinese

• Model Sizes: 1B, 5.7B, 6.7B, and 33B parameters

• Window Size: 16K tokens for project-level code completion and infilling

• Programming Languages Supported: Python, Java, C++, JavaScript, and over 70 others

Why DeepSeek Coder Stands Out

In the crowded field of AI-driven coding assistants, DeepSeek Coder distinguishes itself through its robust architecture, extensive training data, and unparalleled performance across multiple benchmarks. Here's why DeepSeek Coder is a game-changer for developers:

1. Massive Training Data

DeepSeek Coder is trained from scratch on 2 trillion tokens, ensuring a deep understanding of diverse coding patterns and natural language. This extensive training data allows the model to generate accurate and contextually relevant code snippets, enhancing developer productivity and reducing the time spent on repetitive tasks.

2. Highly Flexible & Scalable

Available in multiple sizes—1B, 5.7B, 6.7B, and 33B—DeepSeek Coder offers flexibility to cater to varying computational resources and project requirements. Whether you're working on a lightweight script or a large-scale application, there's a DeepSeek Coder model tailored to your needs.

3. State-of-the-Art Performance

Benchmarking against leading open-source models, DeepSeek Coder consistently outperforms competitors on renowned benchmarks such as HumanEval, MBPP, DS-1000, and APPS. For instance, the DeepSeek-Coder-Base-33B model surpasses CodeLlama-34B by 7.9% on HumanEval Python and 9.3% on HumanEval Multilingual tasks.

4. Advanced Code Completion Capabilities

With a 16K token window size, DeepSeek Coder excels in project-level code completion and infilling tasks. This feature is particularly beneficial for large projects where context from multiple files is crucial for generating coherent and functional code.

5. Multilingual Support

DeepSeek Coder supports an extensive array of programming languages, including but not limited to Python, Java, C++, JavaScript, and Rust. This broad language support ensures that developers across various tech stacks can leverage the model's capabilities.

Performance Evaluation: DeepSeek Coder Leads the Pack

To validate its superiority, DeepSeek Coder underwent rigorous evaluation across multiple coding benchmarks. The results are nothing short of impressive:

These results underscore DeepSeek Coder's exceptional ability to generate accurate and efficient code, making it a reliable tool for developers seeking to enhance their coding workflows.

Getting Started with DeepSeek Coder

Integrating DeepSeek Coder into your development environment is seamless. Follow these simple steps to unlock the full potential of this AI-powered assistant.

1. Installation

Before you begin, ensure that you have the necessary dependencies installed. Execute the following command to install required packages:

pip install -r requirements.txt

2. Accessing the Demo

Experience DeepSeek Coder firsthand through our 🤗 Hugging Face Space. For those who prefer a local setup, you can run the demo using app.py located in the demo folder. A big thank you to the Hugging Face team for their unwavering support!

3. Using DeepSeek Coder: Code Completion Example

Here's a quick example to demonstrate how DeepSeek Coder can assist you in writing a Quick Sort algorithm in Python:

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))

Output:

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)

This example showcases DeepSeek Coder's ability to generate efficient and syntactically correct code based on simple prompts, significantly speeding up the development process.

Fine-Tuning DeepSeek Coder for Your Needs

DeepSeek Coder is not just a static tool; it's highly customizable to fit your specific requirements. We provide a comprehensive script for fine-tuning our models on downstream tasks, ensuring that you can adapt the model to your unique use cases.

Steps to Fine-Tune:

1. Install Required Packages:

   pip install -r finetune/requirements.txt
   

2. Prepare Your Dataset: Ensure your training data follows the Sample Dataset Format, where each line is a JSON-serialized string containing two fields: instruction and output.

3. Execute the Fine-Tuning Script:

   DATA_PATH="<your_data_path>"
   OUTPUT_PATH="<your_output_path>"
   MODEL="deepseek-ai/deepseek-coder-6.7b-instruct"
   
   cd finetune && deepspeed finetune_deepseekcoder.py \
       --model_name_or_path $MODEL_PATH \
       --data_path $DATA_PATH \
       --output_dir $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
   

By fine-tuning DeepSeek Coder, you can optimize the model's performance for specific tasks, enhancing its utility in your projects.

Community and Support

At DeepSeek, we believe in the power of community. Join our vibrant Discord and WeChat (微信) communities to collaborate, seek support, and share your experiences with fellow developers and AI enthusiasts. Our dedicated support team is always ready to assist you with any queries or challenges you may encounter.

@misc{deepseek-coder,
  author = {Daya Guo, Qihao Zhu, Dejian Yang, Zhenda Xie, Kai Dong, Wentao Zhang, Guanting Chen, Xiao Bi, Y. Wu, Y.K. Li, Fuli Luo, Yingfei Xiong, Wenfeng Liang},
  title = {DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence},
  journal = {CoRR},
  volume = {abs/2401.14196},
  year = {2024},
  url = {https://arxiv.org/abs/2401.14196},
}

Conclusion: Embrace the Future of Coding with DeepSeek Coder

DeepSeek Coder is more than just an AI model; it's a transformative tool designed to empower developers by automating mundane tasks, providing intelligent code suggestions, and enhancing overall productivity. By integrating DeepSeek Coder into your workflow, you can focus on what truly matters—innovating and building exceptional software solutions.