Instructions to use QuantumindSSI/01-quantum-resistant-crypto-analyzer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantumindSSI/01-quantum-resistant-crypto-analyzer with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="QuantumindSSI/01-quantum-resistant-crypto-analyzer")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("QuantumindSSI/01-quantum-resistant-crypto-analyzer", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use QuantumindSSI/01-quantum-resistant-crypto-analyzer with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantumindSSI/01-quantum-resistant-crypto-analyzer"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantumindSSI/01-quantum-resistant-crypto-analyzer",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/QuantumindSSI/01-quantum-resistant-crypto-analyzer

SGLang

How to use QuantumindSSI/01-quantum-resistant-crypto-analyzer with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "QuantumindSSI/01-quantum-resistant-crypto-analyzer" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantumindSSI/01-quantum-resistant-crypto-analyzer",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "QuantumindSSI/01-quantum-resistant-crypto-analyzer" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantumindSSI/01-quantum-resistant-crypto-analyzer",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use QuantumindSSI/01-quantum-resistant-crypto-analyzer with Docker Model Runner:
```
docker model run hf.co/QuantumindSSI/01-quantum-resistant-crypto-analyzer
```

Quantum-Resistant Cryptographic Protocol Analyzer

A fine-tuned Small Language Model (SLM) that analyzes cryptographic protocol implementations and identifies quantum-vulnerable patterns, attack vectors, and NIST-aligned mitigations.

Model Details

Attribute	Value
Developer	QuantumIndSSI Ltd
Base Model	./base_model
Architecture	Transformer decoder (causal LM)
Fine-tuning Method	LoRA (Low-Rank Adaptation)
LoRA Rank	16
LoRA Alpha	32
License	apache-2.0

Intended Use

Automated quantum vulnerability scanning of protocol implementations (TLS, SSH, VPN, etc.)
Security audit assistance for classical-to-PQC migration planning
Developer education on quantum cryptanalysis risks
Edge deployment on Victron and other constrained hardware

Training Data

10,500+ synthetic cryptographic protocol analyses
Protocols: TLS/SSL, SSH, IPsec, WireGuard, WPA, S/MIME, OpenPGP, DNSSEC, Kerberos, Bitcoin, Ethereum, gRPC, MQTT, Bluetooth
Vulnerability types: Shor-vulnerable, Grover-amplified, HNDL, downgrade attacks, weak randomness, deprecated protocols, transition gaps
Attack vectors: Shor factoring, Shor DLP, Grover search, quantum collision finding, HNDL passive collection, quantum MITM

Evaluation Results

Metric	Target	Score
Perplexity	< 10.0	TBD
Vulnerability Detection Rate	> 85%	TBD
Attack Vector Recognition	> 80%	TBD
Edge Latency (CPU)	< 1000ms	TBD
Memory Footprint	< 4GB	TBD

Usage

model_id = "quantumindssi/01_quantum_resistant_crypto_analyzer"
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained(model_id)
tokenizer = AutoTokenizer.from_pretrained(model_id)

prompt = """Analyze the following TLS 1.2 implementation for quantum-vulnerable patterns:
```python
context = ssl.SSLContext(ssl.PROTOCOL_TLS_CLIENT)
context.set_ciphers('RSA-AES256-GCM-SHA384')

Identify the vulnerability type, quantum attack vector, and recommend mitigations."""

inputs = tokenizer(prompt, return_tensors="pt") outputs = model.generate(**inputs, max_new_tokens=512) print(tokenizer.decode(outputs[0], skip_special_tokens=True))


## Limitations

- Not a substitute for certified security consultants or formal verification
- Synthetic training data may not capture all real-world edge cases
- English only
- Analysis is heuristic; false positives/negatives are possible

## Hardware Requirements

| Target | RAM | Notes |
|--------|-----|-------|
| Cloud GPU | 4GB | FP16 inference |
| Workstation | 3GB | INT8 quantized |
| Victron Edge | 2-3GB | INT8/INT4 quantized, CPU |

## Citation

```bibtex
@misc{01_quantum_resistant_crypto_analyzer,
  title={Quantum-Resistant Cryptographic Protocol Analyzer},
  author={QuantumIndSSI Ltd},
  year={2026},
  publisher={Hugging Face},
  howpublished={\url{https://huggingface.co/quantumindssi/01_quantum_resistant_crypto_analyzer}}
}

Contact

GitHub: https://github.com/QuantumindSSI
HuggingFace: https://huggingface.co/quantumindssi
Email: contact@quantumindssi.com

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