Introducing My Custom AI Coding Buddy: Powered by Qwen2.5 7B Model

Building tools that enhance productivity and align with security best practices has always been a priority for me. Today, I want to share how I created and integrated an AI coding assistant into PyCharm Professional, leveraging the Qwen2.5 7B model and my own local infrastructure.

Why Qwen2.5 7B?

The Qwen2.5 7B model is one of the most advanced in its class, designed for both conversational and coding-related tasks. Here’s why I chose it:

• Powerful Performance: Qwen excels in understanding complex queries and providing detailed, context-aware responses. Its architecture is optimized for a wide range of use cases, including code completion, debugging assistance, and general-purpose programming support.
• Flexibility: Unlike other models, Qwen offers a balance of power and customization, enabling me to fine-tune its deployment based on my unique requirements.
• Security and Control: Running a model locally ensures sensitive information never leaves my environment, a crucial factor in industries where data privacy and compliance are paramount.

How I Built It

Step 1: Setting Up Ollama on My VM

I began by downloading Ollama on a virtual machine and loading the Qwen2.5 7B model.

I than pulled the latest Qwen model:

ollama pull qwen2.5:7b

To test its functionality, I used the following command:

time curl http://<<internal_ip>>:11434/api/chat -d '{  
  "model": "qwen2.5:7b",  
  "messages": [{ "role": "user", "content": "Hello" }],  
  "stream": false  
}'  

The Results

The response to the “Hello” query on my VM took:

• 2 minutes and 47 seconds

This delay was expected due to the limited resources of the VM. However, the output confirmed the model was running correctly.

Step 2: Harnessing GPU Power

To improve performance, I set up Ollama on my Windows machine, equipped with the EVGA GeForce RTX 3090 FTW3 ULTRA GAMING GPU. With this hardware, I tested the same command:

time curl http://<<internal_ip>>:11434/api/chat -d '{  
  "model": "qwen2.5:7b",  
  "messages": [{ "role": "user", "content": "Hello" }],  
  "stream": false  
}'  

The Results

This time, the response came back in just:

• 6.4 seconds

This remarkable improvement highlights the importance of hardware optimization when working with large language models.

Step 3: Integration into PyCharm Professional

To integrate the Qwen2.5 7B model into PyCharm, I used the Continue extension (v0.0.75) on an Ubuntu installation. With this setup, I can interact with the model directly in my development environment, asking questions, debugging code, or even generating new snippets in real time.

The Benefits

Enhanced Productivity

With this AI coding buddy, I’ve become significantly more productive. Tasks like writing boilerplate code, debugging tricky issues, or brainstorming solutions are now faster and more streamlined. Instead of searching for answers online or sifting through documentation, I have a reliable assistant ready to help at a moment’s notice.

Security by Design

One of the most significant advantages of running a local model is security. Unlike cloud-based models, where queries and data are sent over the internet, my setup ensures everything stays within my environment. This added layer of security is essential for protecting sensitive code and maintaining compliance with industry regulations.

Screenshots

Send my code snippet to qwen:

Asking qwen for help:

Closing Thoughts

Building this tool has been a rewarding journey, blending cutting-edge AI with practical application. By leveraging Qwen2.5 7B, I’ve not only boosted my efficiency but also ensured my workflow aligns with the highest security standards.

What’s next? Exploring even more ways to harness AI to make development smarter and safer.

I’d love to hear your thoughts on this approach. Have you built similar tools, or are you considering it? Let’s connect and discuss!

#AI #PyCharm #CodingBuddy #Productivity #MachineLearning #Security