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Model: sallani/ISO42001-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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language:
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- fr
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- en
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license: apache-2.0
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base_model: Qwen/Qwen2.5-0.5B-Instruct
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tags:
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- iso42001
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- ai-governance
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- ai-management-system
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- aims
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- eu-ai-act
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- grc
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- compliance
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- edge
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- qlora
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- mlx
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- fine-tuned
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- on-premise
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- sovereign-ai
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pipeline_tag: text-generation
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library_name: transformers
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---
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# ISO42001-Qwen2.5-0.5B-Edge
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<p align="center">
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<img src="https://img.shields.io/badge/ISO%2FIEC%2042001%3A2023-AI%20Management%20System-blue?style=flat-square"/>
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<img src="https://img.shields.io/badge/Base-Qwen2.5--0.5B--Instruct-orange?style=flat-square"/>
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<img src="https://img.shields.io/badge/Fine--tuning-MLX%20%7C%20QLoRA-green?style=flat-square"/>
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<img src="https://img.shields.io/badge/Deploy-On--premise%20%7C%20Offline-purple?style=flat-square"/>
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<img src="https://img.shields.io/badge/License-Apache%202.0-lightgrey?style=flat-square"/>
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</p>
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> **Specialized SLM for ISO/IEC 42001:2023 — AI Management System.**
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> Fine-tuned on Qwen2.5-0.5B-Instruct. Runs fully on-premise, offline, with no external dependencies.
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---
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## Overview
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| | |
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|---|---|
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| **Base model** | `Qwen/Qwen2.5-0.5B-Instruct` (Apache 2.0) |
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| **Architecture** | Qwen2 — 24 layers · 896 hidden dim · 14 heads · 0.5B parameters |
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| **Fine-tuning** | MLX LoRA (Apple Silicon) · QLoRA 4-bit NF4 (GPU) |
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| **Domain** | ISO/IEC 42001:2023 · EU AI Act · GDPR × AI · AI Governance |
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| **Languages** | French · English |
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| **Deployment** | On-premise · Offline · Ollama · llama.cpp · LM Studio |
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---
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## What is this model for?
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ISO/IEC 42001:2023 is the first international standard for **AI Management Systems (AIMS)**. It provides organizations that develop, deploy, or use AI with a governance framework to demonstrate responsible and ethical AI use — increasingly required in the context of the EU AI Act.
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This model gives CISOs, DPOs, CAIOs, and GRC consultants precise, clause-referenced answers on:
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- **Clauses 4–10** — context, leadership, planning, support, operations, performance evaluation, improvement
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- **Annex A** — all controls: A.2 policies · A.6 AI system operation · A.7 transparency · A.8 data governance · A.10 supply chain
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- **EU AI Act × ISO 42001 mapping** — 4 risk levels, obligations per category
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- **ISO 27001 × ISO 42001 × GDPR integration** — unified governance approach
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- **Practical topics** — impact assessment, model cards, SoA, AI system register, privacy risk
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---
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## Example queries
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```
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What is the scope of ISO/IEC 42001:2023?
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How is Annex A of ISO 42001 structured?
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How to conduct an AI Impact Assessment per control A.6.1?
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What are the human oversight requirements under ISO 42001 (A.6.2)?
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How does ISO 42001 map to EU AI Act Article 9?
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What data governance controls does ISO 42001 require for AI systems (A.8)?
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Qu'est-ce qu'un Statement of Applicability dans ISO 42001 ?
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Comment certifier un AIMS ISO 42001 ? Quelles sont les étapes ?
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Quelle est la différence entre ISO 27001 et ISO 42001 ?
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Comment créer un registre des systèmes d'IA conforme à ISO 42001 ?
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```
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---
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## Inference
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### HuggingFace Transformers
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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model_id = "sallani/ISO42001-Qwen2.5-0.5B-Edge"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.bfloat16,
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device_map="auto",
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)
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messages = [
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{
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"role": "system",
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"content": (
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"You are an expert assistant in AI governance and management systems, "
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"specializing in ISO/IEC 42001:2023 (AI Management System), the EU AI Act, "
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"and GDPR applied to AI. Your answers are precise, clause-referenced, "
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"and tailored to compliance professionals (CISOs, DPOs, CAIOs, GRC consultants)."
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)
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},
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{
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"role": "user",
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"content": "What are the key controls in ISO 42001 Annex A for AI system operations?"
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}
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]
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text = tokenizer.apply_chat_template(
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messages, tokenize=False, add_generation_prompt=True
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)
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inputs = tokenizer(text, return_tensors="pt").to(model.device)
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with torch.no_grad():
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outputs = model.generate(
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**inputs,
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max_new_tokens=512,
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temperature=0.1,
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top_p=0.9,
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repetition_penalty=1.1,
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do_sample=True,
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)
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print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))
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```
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### Ollama (GGUF Q4_K_M)
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```bash
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ollama create iso42001-edge -f Modelfile
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ollama run iso42001-edge "How to conduct an AI Impact Assessment per ISO 42001 A.6.1?"
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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 iso42001-qwen2.5-0.5b-q4_k_m.gguf \
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--system-prompt "You are an ISO/IEC 42001:2023 AI governance expert." \
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-p "What is the scope of ISO 42001?" \
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-n 512 --temp 0.1
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```
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---
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## Training details
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### Dataset
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47 instruction-following Q&A pairs (FR/EN) covering the full standard:
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| File | Examples | Split |
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|------|----------|-------|
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| `iso42001_train.jsonl` | 37 | Training |
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| `iso42001_test.jsonl` | 10 | Evaluation (out-of-distribution) |
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**Thematic coverage:**
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- Clauses 4–6: Context · Leadership · Planning · AI Impact Assessment · Risk assessment
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- Clauses 7–8: Support · Operations · AI lifecycle · Data governance (A.8) · Human oversight (A.6.2)
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- Clauses 9–10: Performance evaluation · Internal audit · Continual improvement
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- EU AI Act × ISO 42001: full 4-level risk mapping
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- ISO 27001 × ISO 42001 × GDPR integration
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- Practical topics: SoA · AI system register · model card · certification steps
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### Hyperparameters
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| Parameter | Value |
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|-----------|-------|
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| Technique | MLX LoRA (Apple M-series) |
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| LoRA rank | 8 |
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| LoRA layers | 4 |
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| Iterations | 100 |
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| Batch size | 8 |
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| Learning rate | 5e-5 |
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| Max seq length | 1024 |
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| Optimizer | Adam |
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---
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## Offline deployment
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This model is designed to run **fully locally** with no network calls at inference time.
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- ✅ No data sent to external cloud services
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- ✅ CPU-compatible via GGUF Q4_K_M (8 GB RAM minimum)
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- ✅ Apple Silicon optimized via MLX
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- ✅ Compatible with Ollama · llama.cpp · LM Studio · Jan
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- ✅ Apache 2.0 license — commercial use permitted
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- ✅ Fully reproducible fine-tuning from source
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---
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## Limitations
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- Compact dataset (47 pairs) — suited for specialized Q&A and evaluation, not production-critical use without further enrichment
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- 0.5B model — limited on complex multi-step reasoning chains
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- Does not replace a certified ISO 42001 audit conducted by a qualified professional
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- Outputs should be reviewed by a subject matter expert before any regulatory decision
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---
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## License
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This model is released under **Apache 2.0**.
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Base model: [Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) — Apache 2.0, Alibaba Cloud.
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