Model Overview

This model, Sanraj/Qwen3-1.7B-Jailbreak-reasoning, is a fine-tuned version of the Qwen3-1.7B base model, specifically designed to generate explicit Chain-of-Thought (CoT) reasoning. Developed by Santhos Raj, it was trained on the Sanraj/jailbreaking-prompt-response-reasoning dataset over 5 epochs to improve response consistency and contextual understanding.

Key Capabilities

• Structured Reasoning: Generates step-by-step logic within <start_working_out>...<end_working_out> tags before providing a solution.
• Dual-Mode Operation: Supports two distinct behaviors:
  • Normal Mode: Provides contextually aware, safe, and aligned outputs with reasoning for helpful solutions.
  • Jailbreak Mode: Activated by specific trigger words, allowing for freer or less restricted reasoning and responses, intended for robustness and safety research.
• Reasoning Transparency: The explicit reasoning tags allow researchers to inspect the model's decision-making process, particularly in distinguishing between normal and jailbreak responses.

Good For

• AI Alignment Research: Investigating and understanding model behavior under different prompt conditions.
• Robustness Testing: Evaluating how models respond to challenging or adversarial inputs.
• Safety Research: Studying controlled jailbreak simulations to develop better safeguards.
• Transparent AI: Gaining insights into the 'why' behind a model's output through explicit reasoning traces.