This model is a specialized fine-tune of Qwen/Qwen2.5-7B designed to move beyond basic crash detection into senior-level architectural and logical bug hunting.

Project Overview: Volume 2 Evolution

After initial training, the 'Volume 1' model successfully identified syntax errors but struggled with deep Python internals. Volume 2 integrates algorithmic reasoning to solve these gaps.

Volume 2 Improvements:

Logic Augmentation: Integrated 300 highly complex algorithmic samples from deepmind/code_contests mapped into RAG format.
Two-Stage SFT: Stage 2 used a reduced LR (3e-5) using the Unsloth engine to absorb complex logical patterns.

Strengths

Deep Memory Insight: Diagnoses issues during Garbage Collection (e.g., recursion in destructors).
Internal Scoping Logic: Proficient at identifying variable shadowing and late-binding in closures.
Structured Reasoning: Consistent use of internal chain-of-thought to simulate code execution.

Limitations

Async Logic: May occasionally suggest thread-safety fixes for purely asynchronous event-loop blocks.
Repetition: Can exhibit repetitive confidence phrases in the final summary phase.

Usage & Recommended Parameters

For optimal coding performance, use the following settings to balance creativity and logical precision:

python

from unsloth import FastLanguageModel
model, tokenizer = FastLanguageModel.from_pretrained("yukiii02/qwen2.5-7b-flash-bug-hunting-unsloth")
FastLanguageModel.for_inference(model)

# Recommended Inference Settings for Coding:
generation_params = {
    "max_new_tokens": 512,
    "temperature": 0.1,    # Lower temperature for logical stability
    "top_p": 0.95,
    "top_k": 50,
    "repetition_penalty": 1.1
}

# Recommended System Prompt:
# 'You are an expert bug hunter. Trace the code step-by-step using RAG-style reasoning.'

Acknowledgements & Datasets

This model was trained using the following resources:

Datasets:
- yukiii02/flash-bug-hunting: The primary source for structured RAG-style bug diagnostics.
- DeepMind CodeContests: Used for advanced algorithmic reasoning and logic augmentation.
Frameworks:
- Unsloth AI: High-efficiency fine-tuning engine.
- Qwen2.5: Base model architecture provided by the Qwen Team.