Instructions to use QuantumindSSI/quantuml-pqc-0.5b-poc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantumindSSI/quantuml-pqc-0.5b-poc with Transformers:

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

pipe = pipeline("text-generation", model="QuantumindSSI/quantuml-pqc-0.5b-poc")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("QuantumindSSI/quantuml-pqc-0.5b-poc", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use QuantumindSSI/quantuml-pqc-0.5b-poc with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantumindSSI/quantuml-pqc-0.5b-poc"
# 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/quantuml-pqc-0.5b-poc",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/QuantumindSSI/quantuml-pqc-0.5b-poc

SGLang

How to use QuantumindSSI/quantuml-pqc-0.5b-poc 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/quantuml-pqc-0.5b-poc" \
    --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/quantuml-pqc-0.5b-poc",
		"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/quantuml-pqc-0.5b-poc" \
        --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/quantuml-pqc-0.5b-poc",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use QuantumindSSI/quantuml-pqc-0.5b-poc with Docker Model Runner:
```
docker model run hf.co/QuantumindSSI/quantuml-pqc-0.5b-poc
```

Post-Quantum Key Migration Advisor

A fine-tuned Small Language Model (SLM) providing step-by-step migration guidance for organizations transitioning from classical to quantum-safe cryptographic systems.

Model Details

Attribute	Value
Developer	QuantumIndSSI Ltd
Base Model	Qwen/Qwen2-0.5B-Instruct
Architecture	Transformer decoder (causal LM)
Fine-tuning Method	LoRA (Low-Rank Adaptation)
LoRA Rank	16
LoRA Alpha	32
License	apache-2.0

Evaluation Results

Metric	Score
Perplexity	1.59
Task Quality	87.5%
Train Loss	0.300
Eval Loss	0.073

Usage

model_id = "quantumindssi/02_post_quantum_key_migration_advisor"
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained(model_id)
tokenizer = AutoTokenizer.from_pretrained(model_id)

Limitations

Not a substitute for certified security consultants
Synthetic training data may not capture all real-world edge cases
English only

Citation

@misc{quantuml2026,
  title={Post-Quantum Key Migration Advisor},
  author={QuantumIndSSI},
  year={2026},
  howpublished={\url{https://huggingface.co/quantumindssi}}
}

Contact

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

Downloads last month: -; Downloads are not tracked for this model. How to track