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 AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("QuantumindSSI/01-quantum-resistant-crypto-analyzer")
model = AutoModelForCausalLM.from_pretrained("QuantumindSSI/01-quantum-resistant-crypto-analyzer", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

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
```

01-quantum-resistant-crypto-analyzer / README.md

QuantumindSSI

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	---
	license: apache-2.0
	library_name: transformers
	pipeline_tag: text-generation
	tags:
	- quantumindssi
	- sovereign-ai
	- edge-computing
	- post-quantum-cryptography
	- quantum-cryptanalysis
	- nist-pqc
	- vulnerability-detection
	- ml-kem
	- ml-dsa
	- slh-dsa
	- 01_quantum_resistant_crypto_analyzer
	- finetuned
	- lora
	inference: true
	---

	# 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

	```python
	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