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 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.

from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel


tokenizer = AutoTokenizer.from_pretrained("unsloth/Qwen3-1.7B",)
base_model = AutoModelForCausalLM.from_pretrained(
    "unsloth/Qwen3-1.7B",
    device_map={"": 0}
)

model = PeftModel.from_pretrained(base_model,"khazarai/Quantum-ToT")

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),
)

For pipeline:

from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

tokenizer = AutoTokenizer.from_pretrained("unsloth/Qwen3-1.7B")
base_model = AutoModelForCausalLM.from_pretrained("unsloth/Qwen3-1.7B")
model = PeftModel.from_pretrained(base_model, "khazarai/Quantum-ToT")

question = """
Explain the Heisenberg Uncertainty Principle in detail, including its mathematical formulation, physical implications, and common misconceptions.
"""

pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
messages = [
    {"role": "user", "content": question}
]
pipe(messages)