🔐 ISO27001-Qwen2.5-0.5B-Edge
ISO 27001:2022 fine-tuned SLM — on-premise, offline, sovereign
About
A specialized AI agent for ISO/IEC 27001:2022 and ISO/IEC 27002:2022, designed to run entirely on-premise and offline — no data ever leaves your infrastructure.
Fine-tuned on 199 Q&A pairs covering the full ISO 27001:2022 requirements: clauses 4-10, all 93 Annex A controls, gap assessment, audit preparation, and regulatory alignment with NIS2, DORA, and GDPR.
Use Cases
- 🔍 ISMS Gap Assessment — maturity evaluation, non-conformity identification
- 📋 ISO 27001 Audit Support — clauses, Annex A controls, expected audit evidence
- 🛡️ CISO / DPO Advisory — risk management, risk treatment plan, Statement of Applicability
- 📄 Certification Preparation — auditor checklist, mandatory documentation
- 🔗 Regulatory Alignment — NIS2, DORA, GDPR, ISO 42001 mapped to ISO 27001
Quick Start
Ollama (recommended)
# Download the Modelfile
curl -O https://huggingface.co/sallani/ISO27001-Qwen2.5-0.5B-Edge/resolve/main/Modelfile
# Create and run
ollama create iso27001-agent -f Modelfile
ollama run iso27001-agent
llama.cpp
llama-cli \
-m iso27001-qwen2.5-0.5b-q4_k_m.gguf \
--chat-template qwen \
-p "What are the mandatory documents required by ISO 27001:2022?" \
-n 512
Python / transformers
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "sallani/ISO27001-Qwen2.5-0.5B-Edge"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
messages = [
{
"role": "system",
"content": "You are an ISO 27001:2022 Lead Auditor and ISMS expert. Your answers are precise, actionable, and referenced to specific clauses and controls."
},
{
"role": "user",
"content": "What are the mandatory documents required by ISO 27001:2022?"
}
]
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, temperature=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
MLX — Apple Silicon
pip install mlx-lm
python -m mlx_lm.generate \
--model sallani/ISO27001-Qwen2.5-0.5B-Edge \
--prompt "What is the Statement of Applicability in ISO 27001?" \
--max-tokens 512
Available Files
| File | Format | Size | Usage |
|---|---|---|---|
model.safetensors | SafeTensors FP16 | ~988 MB | transformers, MLX |
iso27001-qwen2.5-0.5b-q4_k_m.gguf | GGUF Q4_K_M | ~398 MB | Ollama, llama.cpp |
tokenizer.json | JSON | — | tokenization |
config.json | JSON | — | architecture |
Modelfile | Ollama | — | local deployment |
Model Details
| Parameter | Value |
|---|---|
| Architecture | Qwen2.5 Transformer decoder |
| Parameters | 0.5B |
| Base model | Qwen/Qwen2.5-0.5B-Instruct |
| Fine-tuning method | QLoRA / LoRA via MLX-LM |
| LoRA layers | 4 |
| Learning rate | 1e-4 |
| Iterations | 150 |
| Batch size | 8 |
| Max sequence length | 1,024 tokens |
| Max context | 32,768 tokens |
| GGUF quantization | Q4_K_M (~398 MB) |
| Languages | French 🇫🇷 + English 🇬🇧 |
Training Dataset
Fine-tuned on sallani/iso27001-isms-dataset — 199 unique Q&A pairs (159 train / 40 test).
Coverage:
- Clauses 4-10 (all mandatory ISMS requirements)
- All 93 Annex A controls across 4 themes: Organisational, People, Physical, Technological
- All 11 new ISO 27001:2022 controls (threat intelligence, cloud security, secure coding, DLP, data masking…)
- Gap assessment methodology and ISMS maturity levels
- Practical scenarios: ransomware response, NIS2/DORA alignment, Zero Trust, AI/ISO 42001
- Bilingual FR/EN with Lead Auditor system prompt
Limitations
- 0.5B parameter model — reasoning capabilities are limited compared to larger models (>7B)
- Does not replace a professional ISO 27001 audit or a certified Lead Auditor
- Answers should be validated by an expert before use in a real audit context
- Knowledge is limited to the fine-tuning date
License
Apache 2.0 — same license as the base model Qwen2.5-0.5B-Instruct.
Citation
@model{iso27001_qwen25_edge_2025,
title = {ISO27001-Qwen2.5-0.5B-Edge},
author = {Sabri Allani},
year = {2025},
url = {https://huggingface.co/sallani/ISO27001-Qwen2.5-0.5B-Edge}
}