azherali/qwen3_ai_code_detector
The azherali/qwen3_ai_code_detector is a 32 billion parameter Qwen3-based causal language model developed by azherali, fine-tuned from unsloth/Qwen3-32B. This model was trained using Unsloth and Huggingface's TRL library, enabling faster training. With a 32768 token context length, it is specifically designed for tasks involving AI code detection.
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Model Overview
The azherali/qwen3_ai_code_detector is a 32 billion parameter Qwen3-based language model developed by azherali. It was fine-tuned from the unsloth/Qwen3-32B model, leveraging the Unsloth library and Huggingface's TRL for accelerated training.
Key Capabilities
- Architecture: Based on the Qwen3 family of models.
- Parameter Count: Features 32 billion parameters, offering substantial capacity for complex tasks.
- Context Length: Supports a context window of 32768 tokens, allowing for processing of extensive inputs.
- Training Efficiency: Utilized Unsloth for 2x faster fine-tuning, indicating an optimized training process.
Use Cases
This model is specifically designed and fine-tuned for AI code detection. Its architecture and training focus suggest strong performance in identifying and analyzing code generated by AI, making it suitable for applications requiring automated code review, plagiarism detection in AI-generated code, or ensuring compliance in AI development workflows.