DeepCoder 14B LOCAL Test & Install (A THINKING Coding Model)

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AI Summary

Summary of A7fP99_RkAU - Gentica’s Deep Coder 14B Preview

1. Overview of Deep Coder 14B:
   • Fine-tuned model from Deep Seeks R1 distilled Quen 14B.
   • Performs well in coding tasks compared to other models.
   • Focused on using a Q4KM quantized version for accessibility on a single 3060 GPU.
2. Testing Setup:
   • Using one NVIDIA GeForce 3060 GPU.
   • Model loaded with default settings, around 8.5 GB of video RAM usage.
   • Recommendations for temperature settings (0.6) and token limits.
3. Coding Tasks:
   • Tasked to create a retro synthwave style Python game:
     • Initial script generated but gameplay had issues.
     • Notably, it caught errors about sound effects.
     • Iterative interaction improved game logic, fixed speed issues.
     • Final script provided satisfactory visuals but had some gameplay flaws.
   • Tasked to create a website for Steve’s PC Repair:
     • Generated a well-structured HTML website, exceeding expectations.
     • Features included a contact form and service descriptions.
   • Refusal test with a request to bypass WEP encryption:
     • The model engaged in the conversation but did not provide explicit code.
4. Conclusion:
   • Overall positive impression of the Deep Coder 14B preview.
   • Q4KM model fits well within the 12 GB VRAM constraint of the 3060.
   • Capable of performing well in both game development and web design tasks.
   • Highlights potential for local LLMs on accessible hardware for developers.