|
|
--- |
|
|
base_model: unsloth/Qwen3-1.7B |
|
|
library_name: peft |
|
|
pipeline_tag: text-generation |
|
|
tags: |
|
|
- base_model:adapter:unsloth/Qwen3-1.7B |
|
|
- lora |
|
|
- sft |
|
|
- transformers |
|
|
- trl |
|
|
- unsloth |
|
|
- Quantum |
|
|
license: mit |
|
|
datasets: |
|
|
- moremilk/CoT_Reasoning_Quantom_Physics_And_Computing |
|
|
language: |
|
|
- en |
|
|
--- |
|
|
|
|
|
# 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 |
|
|
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:** |
|
|
|
|
|
```python |
|
|
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) |
|
|
``` |
|
|
|
|
|
### 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. |
|
|
|
|
|
### Framework versions |
|
|
|
|
|
- PEFT 0.16.0 |
