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