初始化项目，由ModelHub XC社区提供模型

Model: khazarai/Quantum-ToT
Source: Original Platform

README.md

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+---
+library_name: transformers
+tags:
+- Physics
+- Quantum
+- reasoning
+- unsloth
+- sft
+- lora
+license: apache-2.0
+datasets:
+- mattwesney/CoT_Reasoning_Quantom_Physics_And_Computing
+language:
+- en
+base_model:
+- unsloth/Qwen3-1.7B
+pipeline_tag: text-generation
+---
+
+# Quantum-ToT
+
+## Model Details
+
+Quantum-ToT is a fine-tuned variant of Qwen3-1.7B, optimized for Chain-of-Thought (CoT) reasoning in quantum mechanics and quantum computing contexts.
+This model was trained using the [moremilk/CoT_Reasoning_Quantum_Physics_And_Computing](https://huggingface.co/datasets/moremilk/CoT_Reasoning_Quantom_Physics_And_Computing) dataset — a curated collection of question–answer pairs that go beyond surface-level definitions to show the logical reasoning process behind quantum concepts.
+
+The goal of this fine-tuning is to enhance the model’s ability to:
+
+- Explain quantum principles with structured, step-by-step logic
+- Reason through conceptual problems in quantum physics and computing
+- Support educational and research applications that require interpretable reasoning chains
+
+
+## Uses
+
+### Direct Use
+
+- Educational assistance in quantum physics and quantum computing
+- AI tutors or reasoning assistants for STEM learning
+- Conceptual reasoning benchmarks involving quantum phenomena
+- Research in reasoning-aware model behavior and CoT interpretability
+
+### Out of Scope  
+
+- Predicting new or unverified physical phenomena
+- Running quantum simulations or algorithmic derivations
+- Hardware-level quantum design
+- Real-time physics predictions
+
+## Bias, Risks, and Limitations
+
+- May hallucinate if prompted outside the quantum domain
+- Not suitable for advanced quantum algorithm design or experimental predictions
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("khazarai/Quantum-ToT")
+model = AutoModelForCausalLM.from_pretrained(
+    "khazarai/Quantum-ToT",
+    device_map={"": 0}
+)
+
+question = """
+Explain the Heisenberg Uncertainty Principle in detail, including its mathematical formulation, physical implications, and common misconceptions.
+"""
+
+messages = [
+    {"role" : "user", "content" : question}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize = False,
+    add_generation_prompt = True,
+    enable_thinking = True,
+)
+
+from transformers import TextStreamer
+_ = model.generate(
+    **tokenizer(text, return_tensors = "pt").to("cuda"),
+    max_new_tokens = 3000, 
+    temperature = 0.6,
+    top_p = 0.95,
+    top_k = 20,
+    streamer = TextStreamer(tokenizer, skip_prompt = True),
+)
+```
+
+### Dataset
+
+Dataset: [moremilk/CoT_Reasoning_Quantum_Physics_And_Computing](https://huggingface.co/datasets/moremilk/CoT_Reasoning_Quantom_Physics_And_Computing)
+
+This dataset contains rich reasoning-based question–answer pairs covering:
+
+- Core quantum principles: superposition, entanglement, measurement
+- Effects of quantum gates (Hadamard, Pauli-X/Y/Z, etc.) on qubits
+- Multi-qubit reasoning (e.g., Bell states, entangled systems)
+- Basic quantum algorithms and logical operations
+- Probabilistic interpretation of measurement outcomes
+
+Each entry includes:
+
+- think block → model’s internal reasoning process
+- answer block → final concise explanation or solution
+
+The dataset focuses on conceptual understanding rather than heavy mathematical derivations or complex quantum hardware design.