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Three open-source coding models, Deep Sea Coder V2, Llama Coder 9B, and Quen 2.5 Coder 7B, were evaluated for local coding capabilities without internet. Tests included generating Python games like Snake and Tetris, with model performance measured in tokens per second. Quen 2.5 emerged as the best performer, successfully creating the Snake game and demonstrating the highest speed overall. Challenges continued with Tetris, where none performed adequately, and various coding problems were also tested, with Quen 2.5 leading in multiple tests, proving to be a reliable, locally operable AI model for coding tasks.
Deep Sea Coder V2 demonstrates local coding capabilities without internet requirements.
Llama Coder 9B achieves faster token processing with Python’s Turtle library.
Quen 2.5 Coder 7B excels with the highest speed, successfully running Python games.
Challenges arise with Tetris; all models failed to build the game successfully.
All models succeeded in simpler coding challenges, highlighting varied performance.
Testing AI coding models showcases the ongoing advancements in natural language processing and their practical application in coding. While Quen 2.5 outperformed in game development tasks, challenges with Tetris highlight the complexities of AI-generated code in more intricate scenarios. Continuous improvements in model architectures could lead to better performance in such complex tasks, paving the way for AI to assist in more advanced programming challenges.
The comparison of these three coding models reflects the rapid evolution of AI in software development. With local execution capabilities, these models exhibit the potential to empower developers by automating code generation without internet dependency. This trend indicates a shift towards more accessible AI tools, allowing independent software development environments without reliance on external servers, thereby enhancing productivity and creativity in coding.
Deep Sea Coder was tested with various programming challenges, including generating Python game code.
Llama Coder showed improvements in speed and usability by utilizing the Turtle graphics library.
Quen demonstrated superior coding capabilities by successfully implementing a fully functional Snake game.
Dell provided a high-performance machine instrumental for testing multiple AI models simultaneously.
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NVIDIA's RTX A6000 GPUs enabled efficient processing and model loading during the coding tests.
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