This model is a fine-tuned version of CodeLlama-7B (4-bit quantized) trained on the CyberNative dataset. The dataset contains secure and insecure coding samples with detailed vulnerability annotations. The model is fine-tuned using LoRA with Unsloth and merged with the base model to produce a performant .gguf model ready for local inference via Ollama.

• Base Model: CodeLlama-7B
• Dataset: CyberNative Dataset
• Fine-tuning Type: Supervised fine-tuning (SFT)
• Task: Code review with a focus on security vulnerabilities and suggestions for secure alternatives
• Training Framework: Unsloth (LoRA fine-tuning)
• Quantization: Q8_0
• Architecture: LLaMA 2 family
• Format: GGUF (compatible with Ollama, llama.cpp)

🧑‍💻 Intended Use

• Detect security flaws in code snippets
• Suggest secure refactored code alternatives
• Educational tool for secure coding practices

🚫 Limitations

• May not detect complex zero-day vulnerabilities
• Might hallucinate when provided with ambiguous inputs
• Only supports languages in the training dataset (mostly Python/C/C++)