khazarai/Quantum-ToT

Hugging Face
TEXT GENERATIONConcurrency Cost:1Model Size:2BQuant:BF16Ctx Length:32kPublished:Mar 25, 2026License:apache-2.0Architecture:Transformer0.0K Open Weights Warm

khazarai/Quantum-ToT is a fine-tuned variant of Qwen3-1.7B, specifically optimized for Chain-of-Thought (CoT) reasoning within quantum mechanics and quantum computing contexts. Trained on the moremilk/CoT_Reasoning_Quantum_Physics_And_Computing dataset, it excels at explaining quantum principles with structured, step-by-step logic and reasoning through conceptual problems. This model is primarily designed for educational assistance, AI tutors, and research applications requiring interpretable reasoning chains in quantum phenomena.

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Quantum-ToT: Chain-of-Thought Reasoning for Quantum Concepts

Quantum-ToT is a specialized fine-tuned version of the Qwen3-1.7B model, developed by khazarai. Its core innovation lies in its optimization for Chain-of-Thought (CoT) reasoning specifically within the domains of quantum mechanics and quantum computing. Unlike general-purpose LLMs, Quantum-ToT is trained to provide structured, step-by-step logical explanations for complex quantum principles.

Key Capabilities

  • Structured Quantum Explanations: Explains quantum principles (superposition, entanglement, quantum gates) with clear, step-by-step logic.
  • Conceptual Problem Solving: Reasons through theoretical problems in quantum physics and computing, focusing on conceptual understanding.
  • Interpretable Reasoning: Designed to show the logical process behind its answers, making it valuable for educational and research contexts.
  • Specialized Knowledge: Fine-tuned on the moremilk/CoT_Reasoning_Quantum_Physics_And_Computing dataset, which emphasizes reasoning-based question-answer pairs.

Good for

  • Educational Assistance: Ideal for AI tutors or reasoning assistants in STEM learning, particularly for quantum physics and computing.
  • Research in CoT Interpretability: Useful for studying how models generate and present reasoning chains in specialized scientific domains.
  • Conceptual Understanding: Supports applications requiring deep, conceptual explanations of quantum phenomena rather than mathematical derivations or simulations.

Limitations

  • Not intended for predicting new physical phenomena or running quantum simulations.
  • May hallucinate if prompted outside its specialized quantum domain.

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