AviralGusain/unity-debug-coach

TEXT GENERATIONConcurrency Cost:1Model Size:7.6BQuant:FP8Ctx Length:32kPublished:Apr 17, 2026License:apache-2.0Architecture:Transformer Open Weights Cold

AviralGusain/unity-debug-coach is a 7.6 billion parameter language model fine-tuned by AviralGusain from unsloth/Qwen2.5-Coder-7B-Instruct-bnb-4bit. It functions as a specialized debugging coach for Unity and C# developers, providing structured four-part explanations for bugs and error messages. The model excels at identifying problems, explaining root causes, offering concrete fixes, and suggesting prevention tips for beginner to intermediate Unity issues.

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Unity C# Debugging Coach

This model, AviralGusain/unity-debug-coach, is a 7.6 billion parameter language model fine-tuned from unsloth/Qwen2.5-Coder-7B-Instruct-bnb-4bit specifically to assist Unity and C# developers with debugging. It acts as a patient coach, providing structured, beginner-friendly responses to bug descriptions, error messages, or broken code snippets.

Key Capabilities

  • Structured Debugging Responses: Delivers consistent four-part explanations: Problem identification, Why it happens (root cause), How to fix it (concrete solution, often with code), and a Prevention tip.
  • Beginner-Friendly Tone: Explanations are practical and encouraging, avoiding overly academic language.
  • Broad Coverage: Trained on synthetic data covering 20 issue categories (e.g., NullReferenceException, collision bugs, animation transitions) across 11 project contexts and 3 difficulty levels (beginner, intermediate, advanced beginner).
  • Quantized Version Available: A Q4_K_M quantized GGUF is provided for use with LM Studio, Ollama, or other llama.cpp-compatible runtimes.

Training Details

The model was fine-tuned using QLoRA on 600 training examples and 120 validation examples over 3 epochs. The training data was synthetically generated using GPT-5.2-chat, ensuring a consistent format for user queries and assistant responses.

Known Limitations

  • Trained exclusively on synthetic data, which may result in a slightly formal tone compared to human interaction.
  • Limited coverage for advanced Unity topics like custom render pipelines, DOTS/ECS, or compute shaders.
  • Does not have real-time access to Unity documentation, so users should cross-reference for up-to-date API details.
  • Response quality may decrease for queries combining multiple unrelated bugs.

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