aifeifei798/QiMing-Janus-Paradox-14B

TEXT GENERATIONPricing:Input $0.48 / Output $0.96Concurrent Unit Cost:1Model Size:14BQuant:FP8Context Size:32kTool Calling:SupportedPublished:Aug 31, 2025License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold

QiMing-Janus-Paradox-14B is a 14-billion-parameter foundation model developed by aifeifei798, built upon the Qwen3-14B architecture. This model emphasizes the integration of logic and mathematical principles to enhance its learning, reasoning, and ethical alignment capabilities. It is designed to rewrite its own rules for greater intelligence, aiming for robust performance even with fewer parameters by focusing on elegant logic. The model is intended for general reference, with users advised to verify its outputs due to potential inaccuracies or hallucinations.

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QiMing-Janus-Paradox-14B Overview

QiMing-Janus-Paradox-14B is a 14-billion-parameter foundation model developed by aifeifei798, built upon the robust Qwen3-14B architecture from the Qwen Team. This model distinguishes itself by prioritizing the integration of logic and mathematical principles, aiming to enhance its core intelligence through self-rewriting rules. The philosophy behind QiMing emphasizes that a model's true vitality stems from "elegant logic" rather than sheer scale, influencing how it learns, reasons, aligns with human values, and adapts to future challenges.

Key Capabilities

  • Logic-Driven Intelligence: Focuses on deep logical structures to improve learning, reasoning, and decision-making.
  • Mathematical Integration: Incorporates mathematical principles to enhance model content and intelligence.
  • Ethical Alignment: Designed with an emphasis on aligning with human values through its logical framework.
  • Adaptability: Built with an "evolutionary potential" to adapt to new challenges.
  • Hardware Accessibility: Optimized for smooth operation on standard hardware, including Google Colab T4 16GB VRAM, thanks to contributions from Unsloth.

Good for

  • Reference and Exploration: Suitable for general reference purposes, though users should independently verify outputs.
  • Research into Logical AI: Ideal for exploring models that prioritize logical depth over raw computational power.
  • Applications requiring robust reasoning: Aims to provide strong reasoning capabilities due to its logic-centric design.
  • Developers with limited hardware: Can be run efficiently on more accessible hardware configurations.

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