Best model to fine-tune for code explanation and debugging assistant (zero-cost deployment goal)

Models
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Hi everyone,

I’m working on building a rubber duck–style code assistant that takes user code and returns explanations, debugging suggestions, and thoughtful guiding questions. I want to fine-tune a model to handle this well.

My key goals are:

  • The model should perform well for code analysis, especially for C++ and Python.
  • I want to fine-tune using LoRA or any lightweight method, ideally on Google Colab (free tier) or a modest local setup (Ryzen 5, 8GB RAM).
  • I eventually want to deploy the model for backend use in my app — so I need something that can be deployed at zero cost or with open-source tools (like ollama, text-generation-webui, etc.).

Currently considering models like DeepSeek-Coder, CodeLlama, or StarCoder.

Can anyone suggest:

  1. Which open-source model would be best for my needs?
  2. Are there quantized versions that run well on basic systems?
  3. Any fine-tuning tools or tricks for keeping it lightweight?

Thanks in advance!

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The appropriate model varies depending on the person, so it is better to try it out first and select the one that works best before fine-tuning. Benchmarks and leaderboards are useful for narrowing down the candidates.

2

Many models on Hugging Face have quantized models created by volunteers, so it’s easy and reliable to download and try them out. I think Ollama is the easiest way to test GGUF.

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There are countless techniques, and no one knows them all yet, but if you want to try it within the scope of Colab Free, QLoRA is definitely the way to go.

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