Discover the Perfect ChatGPT Alternative for Coding

Discover the Perfect ChatGPT Alternative for Coding

Table of Contents

1. Introduction
2. Finding Open Source Models
3. The Deep Seek Coder Model
4. Benefits of Local Model Running
5. Downloading and Setting up the Deep Seek Coder Model
6. Testing the Model
7. Example: Creating the Game of Snake in Python
8. Example: Automating Batch Scripts
9. Example: Debugging Code Errors
10. Conclusion

Introduction

In light of recent events surrounding OpenAI, there has been a renewed search for open source models that can be run locally, eliminating the need to rely on external APIs. One such model that has gained Attention is the Deep Seek Coder model, known for its exceptional coding capabilities. With a parameter count comparable to GPT 3.5 Turbo, this model holds great promise for those looking to code with high performance and accuracy. This article will explore the Deep Seek Coder model, its benefits, and provide examples of its usage.

Finding Open Source Models

With the growing demand for local model running, the search for open source models has intensified. Developers seek models that can be downloaded and run on their own hardware, without the need for external APIs. These models provide a Sense of security and independence, allowing developers to have full control over their coding environment. The Deep Seek Coder model is one such example, offering the ability to code locally and harness its impressive capabilities.

The Deep Seek Coder Model

The Deep Seek Coder model has caught the attention of developers due to its impressive coding prowess. With a parameter count of 33 billion, it is capable of performing on par, if not better, than GPT 3.5 Turbo. What sets this model apart is its ability to fit within the constraints of most GPUs, making it accessible to a wider audience. Developers with GPUs equipped with 24 GB of VRAM can download the quantized version of the model and enjoy its coding capabilities.

Benefits of Local Model Running

Running models locally offers a multitude of benefits to developers. Firstly, it removes the reliance on external APIs, eliminating the need to switch between different platforms. This grants developers the freedom to code without constraints and the assurance that their work remains within their control. Local model running also allows for faster generation times, as communication with an external server is no longer necessary. Additionally, data privacy and security are enhanced as sensitive code and data are kept within the confines of the developer's hardware.

Downloading and Setting up the Deep Seek Coder Model

To harness the power of the Deep Seek Coder model, developers need to undertake a simple process of downloading and setting up the model on their local machines. The model can be obtained in a BLS AWQ quantized version, designed to fit comfortably within GPUs with 24 GB of VRAM. Once downloaded, loading the model is a straightforward process that can be completed within seconds.

Testing the Model

Before integrating the Deep Seek Coder model into a coding workflow, it is essential to conduct thorough testing. To evaluate the model's performance, a quick batch of text generation can be initiated using a suitable interface. By providing Prompts and observing the generated output, developers can assess the model's capabilities in real-world coding scenarios. This verification step ensures that the model meets their expectations and performs as intended.

Example: Creating the Game of Snake in Python

To showcase the coding abilities of the Deep Seek Coder model, let's consider an example where the task is to Create the classic game of Snake in Python. By soliciting the implementation from the model, developers can gauge its proficiency in generating functional code. The output can then be analyzed for accuracy, efficiency, and adherence to coding best practices. This example serves as a useful test case to evaluate the model's coding capabilities.

Example: Automating Batch Scripts

The Deep Seek Coder model also proves valuable in automating batch scripts. It excels at assisting developers in creating command-line options and generating scripts that streamline processes. By defining variables and stacking commands, the model can assist in creating more efficient and user-friendly batch scripts. This example highlights the model's versatility beyond coding specific tasks and expands its application to script automation.

Example: Debugging Code Errors

In the realm of debugging, the Deep Seek Coder model can offer crucial assistance. By presenting an error code to the model, developers can leverage its contextual understanding to identify potential solutions and workarounds. This interaction allows for a collaborative debugging process, helping developers resolve issues more efficiently. The model's ability to grasp the nuances of code errors and provide helpful insights is an asset to any developer's toolkit.

Conclusion

The Deep Seek Coder model presents a compelling solution for developers seeking to run open source models locally. Its impressive coding capabilities, compatibility with a range of GPUs, and the ability to perform at par with or surpass larger models make it a valuable addition to any coding workflow. By utilizing the Deep Seek Coder model, developers can unlock new levels of productivity and accuracy, ultimately enhancing their coding experience.