tbowman/quantum-readiness-gui

Quantum Readiness LLM (GUI Version)

A fine-tuned language model specialized in quantum computing readiness assessment, including post-quantum cryptography (PQC) vulnerability scanning and quantum feasibility analysis.

Model Description

This model is fine-tuned from Qwen/Qwen2.5-3B-Instruct using QLoRA (4-bit quantization) on a custom dataset of quantum security and quantum computing analysis examples.

Key Features

• Post-Quantum Cryptography (PQC) Analysis: Identifies quantum-vulnerable algorithms (RSA, ECDSA, ECDH, DSA, DH, DES, 3DES, RC4, MD5, SHA-1)
• NIST Compliance Guidance: References FIPS 203 (ML-KEM/Kyber), FIPS 204 (ML-DSA/Dilithium), FIPS 205 (SLH-DSA/SPHINCS+)
• Quantum Feasibility Analysis (QFA): Identifies opportunities for quantum algorithms (QAOA, VQE, Grover's, HHL, QML)
• Migration Guidance: Provides code examples for transitioning to post-quantum alternatives
• Standalone Operation: Provides direct responses without requiring tool orchestration

Model Details

Property	Value
Base Model	Qwen/Qwen2.5-3B-Instruct
Parameters	~3B
Fine-tuning Method	QLoRA (4-bit NF4)
LoRA Rank	32
LoRA Alpha	64
Training Epochs	3
Final Loss	0.02
Token Accuracy	99%
Context Length	4096 tokens
Format	GGUF (F16)
Size	~6.2 GB

Intended Use

Primary Use Cases

1. Security Auditing: Scan codebases for quantum-vulnerable cryptographic implementations
2. Compliance Assessment: Evaluate alignment with NIST post-quantum standards
3. Migration Planning: Get guidance on transitioning to post-quantum algorithms
4. Quantum Opportunity Analysis: Identify code patterns suitable for quantum acceleration
5. Education: Learn about quantum threats and post-quantum cryptography

Example Prompts

# PQC Security Scanning
"What cryptographic algorithms are vulnerable to quantum attacks?"
"How do I replace RSA with post-quantum alternatives?"
"Explain the quantum threat to ECDSA"

# Quantum Feasibility
"What quantum algorithms could benefit my optimization problems?"
"Explain QAOA and its use cases"
"What is the Quantum Feasibility Index?"

# NIST Standards
"What are the NIST PQC standards?"
"Explain ML-KEM (Kyber) and FIPS 203"
"What's the difference between ML-DSA and SLH-DSA?"

How to Use

With Ollama (Recommended)

1. Download the GGUF file and Modelfile
2. Create the model:

ollama create quantum-readiness-gui -f Modelfile

3. Run:

ollama run quantum-readiness-gui "What algorithms are vulnerable to quantum attacks?"

Modelfile Configuration

FROM quantum-readiness-gui-f16.gguf

PARAMETER num_ctx 4096
PARAMETER temperature 0.7
PARAMETER top_p 0.9
PARAMETER repeat_penalty 1.1

SYSTEM """You are the ArcQubit Quantum Assistant, an AI-powered expert in quantum computing readiness, post-quantum cryptography (PQC), and quantum feasibility analysis."""

TEMPLATE """{{- if .System }}<|im_start|>system
{{ .System }}<|im_end|>
{{ end }}{{- range .Messages }}<|im_start|>{{ .Role }}
{{ .Content }}<|im_end|>
{{ end }}<|im_start|>assistant
"""

With llama.cpp

./main -m quantum-readiness-gui-f16.gguf \
  -p "<|im_start|>user\nWhat algorithms are vulnerable to quantum attacks?<|im_end|>\n<|im_start|>assistant\n" \
  -n 512 --temp 0.7

With Transformers (Merged Model)

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("path/to/merged-model-gui")
tokenizer = AutoTokenizer.from_pretrained("path/to/merged-model-gui")

messages = [
    {"role": "user", "content": "What is ML-KEM (Kyber)?"}
]

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Training Data

The model was trained on ~490 examples covering:

• PQC Vulnerability Scanning: RSA, ECDSA, ECDH, DSA, DH, DES, 3DES, RC4, MD5, SHA-1 detection and remediation
• Quantum Feasibility Analysis: QAOA, VQE, Grover's, HHL, QML opportunity identification
• Migration Guides: Step-by-step code examples for transitioning to post-quantum algorithms
• NIST Standards: FIPS 203, 204, 205 compliance guidance

Training Configuration

model:
  base_model: "Qwen/Qwen2.5-3B-Instruct"
  load_in_4bit: true
  bnb_4bit_quant_type: "nf4"

lora:
  r: 32
  lora_alpha: 64
  lora_dropout: 0.05
  target_modules: ["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"]

training:
  num_train_epochs: 3
  per_device_train_batch_size: 4
  gradient_accumulation_steps: 4
  learning_rate: 2.0e-4
  lr_scheduler_type: "cosine"

NIST PQC Standards Reference

Algorithm	Purpose	Standard	Status
ML-KEM (Kyber)	Key Encapsulation	FIPS 203	Finalized
ML-DSA (Dilithium)	Digital Signatures	FIPS 204	Finalized
SLH-DSA (SPHINCS+)	Stateless Signatures	FIPS 205	Finalized

Quantum Algorithm Categories

Category	Algorithm	Use Case
Optimization	QAOA	Portfolio optimization, scheduling, logistics
Simulation	VQE	Chemistry, materials science
Search	Grover's	Database search, cryptanalysis
Linear Algebra	HHL	Machine learning, optimization
Machine Learning	QML	Classification, clustering

Limitations

• Not a Security Tool: This model provides guidance but should not replace professional security audits
• General Patterns: File paths and line numbers in responses are illustrative, not from actual scans
• Knowledge Cutoff: Based on NIST standards as of early 2025
• No Code Execution: The model provides analysis but does not execute code or access repositories

Ethical Considerations

• Use responsibly for security improvement, not for identifying vulnerabilities to exploit
• Verify all recommendations with security professionals before implementation
• The model is intended for defensive security purposes only

Files

File	Description	Size
quantum-readiness-gui-f16.gguf	GGUF model (F16 quantization)	~6.2 GB
Modelfile	Ollama configuration	~2 KB

Citation

@misc{quantum-readiness-llm,
  title={Quantum Readiness LLM: A Fine-tuned Model for Post-Quantum Cryptography Analysis},
  author={ArcQubit},
  year={2024},
  publisher={Hugging Face},
  url={https://huggingface.co/tbowman/quantum-readiness-gui}
}

License

Apache 2.0

Acknowledgments

• Base model: Qwen/Qwen2.5-3B-Instruct
• NIST Post-Quantum Cryptography Standardization
• Hugging Face Transformers and PEFT libraries