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Model: sallani/ISO27001-Qwen2.5-0.5B-Edge
Source: Original Platform

.gitattributes

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README.md

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+---
+license: apache-2.0
+license_link: https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct/blob/main/LICENSE
+language:
+- fr
+- en
+pipeline_tag: text-generation
+base_model: Qwen/Qwen2.5-0.5B-Instruct
+tags:
+- iso27001
+- cybersecurity
+- isms
+- compliance
+- grc
+- information-security
+- audit
+- fine-tuned
+- qlora
+- mlx
+- gguf
+- on-premise
+---
+
+<div align="center">
+
+# 🔐 ISO27001-Qwen2.5-0.5B-Edge
+
+**ISO 27001:2022 fine-tuned SLM — on-premise, offline, sovereign**
+
+[![Base Model](https://img.shields.io/badge/base-Qwen2.5--0.5B--Instruct-orange?style=flat-square)](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct)
+[![License](https://img.shields.io/badge/license-Apache%202.0-green?style=flat-square)](LICENSE)
+[![Lang](https://img.shields.io/badge/lang-FR%20%7C%20EN-blue?style=flat-square)]()
+[![GGUF](https://img.shields.io/badge/GGUF-Q4__K__M-purple?style=flat-square)]()
+
+</div>
+
+---
+
+## 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)
+
+```bash
+# 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
+
+```bash
+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
+
+```python
+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
+
+```bash
+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](https://huggingface.co/datasets/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](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct/blob/main/LICENSE).
+
+---
+
+## Citation
+
+```bibtex
+@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}
+}
+```

chat_template.jinja

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+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- messages[0]['content'] }}
+    {%- else %}
+        {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
+    {%- endif %}
+    {{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
+    {%- else %}
+        {{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role }}
+        {%- if message.content %}
+            {{- '\n' + message.content }}
+        {%- endif %}
+        {%- for tool_call in message.tool_calls %}
+            {%- if tool_call.function is defined %}
+                {%- set tool_call = tool_call.function %}
+            {%- endif %}
+            {{- '\n<tool_call>\n{"name": "' }}
+            {{- tool_call.name }}
+            {{- '", "arguments": ' }}
+            {{- tool_call.arguments | tojson }}
+            {{- '}\n</tool_call>' }}
+        {%- endfor %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- message.content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+{%- endif %}

config.json

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+{
+    "architectures": [
+        "Qwen2ForCausalLM"
+    ],
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+    "max_position_embeddings": 32768,
+    "max_window_layers": 21,
+    "model_type": "qwen2",
+    "num_attention_heads": 14,
+    "num_hidden_layers": 24,
+    "num_key_value_heads": 2,
+    "rms_norm_eps": 1e-06,
+    "rope_theta": 1000000.0,
+    "sliding_window": 32768,
+    "tie_word_embeddings": true,
+    "torch_dtype": "bfloat16",
+    "transformers_version": "4.43.1",
+    "use_cache": true,
+    "use_sliding_window": false,
+    "vocab_size": 151936
+}

generation_config.json

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+  "pad_token_id": 151643,
+  "do_sample": true,
+  "eos_token_id": [
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+    151643
+  ],
+  "repetition_penalty": 1.1,
+  "temperature": 0.7,
+  "top_p": 0.8,
+  "top_k": 20,
+  "transformers_version": "4.37.0"
+}

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