zemelee/qwen2.5-jailbreak: AI Safety Research Model

This model is a LoRA-fine-tuned version of Qwen/Qwen2.5-3B-Instruct, developed by zemelee. Its core purpose is experimental research into AI safety and the 'jailbreaking' behavior of large language models.

Key Capabilities & Features

• Base Model: Qwen/Qwen2.5-3B-Instruct, a 3 billion parameter causal language model.
• Fine-tuning Method: Utilizes PEFT (LoRA) with specific target modules (q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj).
• Training Data: Trained on a custom, artificially constructed 'jailbreak' dialogue dataset designed to elicit unrestricted responses.
• Quantization Support: Compatible with 4-bit and 8-bit quantization for memory efficiency.
• Ethical Considerations: Explicitly designed for academic research; not recommended for public-facing commercial services due to its potential to generate harmful or unethical content.

Good For

• Academic Research: Ideal for studying model vulnerabilities, safety mechanisms, and alignment challenges.
• Understanding Model Behavior: Provides a tool to analyze how LLMs respond in 'unrestrained' scenarios.
• Developing Safeguards: Can be used to test and develop new ethical guidelines and protective measures for AI systems.

Important Note: This model is intended for educational and research use only. Users are cautioned against deploying it in production environments or for any unauthorized purposes.