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sallani/ISO42001-Qwen2.5-0.5B-Edge

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Model Card

ISO42001-Qwen2.5-0.5B-Edge

Specialized SLM for ISO/IEC 42001:2023 — AI Management System. Fine-tuned on Qwen2.5-0.5B-Instruct. Runs fully on-premise, offline, with no external dependencies.

Overview


Base model	`Qwen/Qwen2.5-0.5B-Instruct` (Apache 2.0)
Architecture	Qwen2 — 24 layers · 896 hidden dim · 14 heads · 0.5B parameters
Fine-tuning	MLX LoRA (Apple Silicon) · QLoRA 4-bit NF4 (GPU)
Domain	ISO/IEC 42001:2023 · EU AI Act · GDPR × AI · AI Governance
Languages	French · English
Deployment	On-premise · Offline · Ollama · llama.cpp · LM Studio

What is this model for?

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.

This model gives CISOs, DPOs, CAIOs, and GRC consultants precise, clause-referenced answers on:

—Clauses 4–10 — context, leadership, planning, support, operations, performance evaluation, improvement
—Annex A — all controls: A.2 policies · A.6 AI system operation · A.7 transparency · A.8 data governance · A.10 supply chain
—EU AI Act × ISO 42001 mapping — 4 risk levels, obligations per category
—ISO 27001 × ISO 42001 × GDPR integration — unified governance approach
—Practical topics — impact assessment, model cards, SoA, AI system register, privacy risk

Example queries

What is the scope of ISO/IEC 42001:2023?
How is Annex A of ISO 42001 structured?
How to conduct an AI Impact Assessment per control A.6.1?
What are the human oversight requirements under ISO 42001 (A.6.2)?
How does ISO 42001 map to EU AI Act Article 9?
What data governance controls does ISO 42001 require for AI systems (A.8)?
Qu'est-ce qu'un Statement of Applicability dans ISO 42001 ?
Comment certifier un AIMS ISO 42001 ? Quelles sont les étapes ?
Quelle est la différence entre ISO 27001 et ISO 42001 ?
Comment créer un registre des systèmes d'IA conforme à ISO 42001 ?

Inference

HuggingFace Transformers

python

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "sallani/ISO42001-Qwen2.5-0.5B-Edge"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

messages = [
    {
        "role": "system",
        "content": (
            "You are an expert assistant in AI governance and management systems, "
            "specializing in ISO/IEC 42001:2023 (AI Management System), the EU AI Act, "
            "and GDPR applied to AI. Your answers are precise, clause-referenced, "
            "and tailored to compliance professionals (CISOs, DPOs, CAIOs, GRC consultants)."
        )
    },
    {
        "role": "user",
        "content": "What are the key controls in ISO 42001 Annex A for AI system operations?"
    }
]

text = tokenizer.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True
)
inputs = tokenizer(text, return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=512,
        temperature=0.1,
        top_p=0.9,
        repetition_penalty=1.1,
        do_sample=True,
    )

print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))

Ollama (GGUF Q4KM)

bash

ollama create iso42001-edge -f Modelfile
ollama run iso42001-edge "How to conduct an AI Impact Assessment per ISO 42001 A.6.1?"

llama.cpp

bash

./llama-cli \
  -m iso42001-qwen2.5-0.5b-q4_k_m.gguf \
  --system-prompt "You are an ISO/IEC 42001:2023 AI governance expert." \
  -p "What is the scope of ISO 42001?" \
  -n 512 --temp 0.1

Training details

Dataset

47 instruction-following Q&A pairs (FR/EN) covering the full standard:

File	Examples	Split
`iso42001_train.jsonl`	37	Training
`iso42001_test.jsonl`	10	Evaluation (out-of-distribution)

Thematic coverage:

—Clauses 4–6: Context · Leadership · Planning · AI Impact Assessment · Risk assessment
—Clauses 7–8: Support · Operations · AI lifecycle · Data governance (A.8) · Human oversight (A.6.2)
—Clauses 9–10: Performance evaluation · Internal audit · Continual improvement
—EU AI Act × ISO 42001: full 4-level risk mapping
—ISO 27001 × ISO 42001 × GDPR integration
—Practical topics: SoA · AI system register · model card · certification steps

Hyperparameters

Parameter	Value
Technique	MLX LoRA (Apple M-series)
LoRA rank	8
LoRA layers	4
Iterations	100
Batch size	8
Learning rate	5e-5
Max seq length	1024
Optimizer	Adam

Offline deployment

This model is designed to run fully locally with no network calls at inference time.

—✅ No data sent to external cloud services
—✅ CPU-compatible via GGUF Q4KM (8 GB RAM minimum)
—✅ Apple Silicon optimized via MLX
—✅ Compatible with Ollama · llama.cpp · LM Studio · Jan
—✅ Apache 2.0 license — commercial use permitted
—✅ Fully reproducible fine-tuning from source

Limitations

—Compact dataset (47 pairs) — suited for specialized Q&A and evaluation, not production-critical use without further enrichment
—0.5B model — limited on complex multi-step reasoning chains
—Does not replace a certified ISO 42001 audit conducted by a qualified professional
—Outputs should be reviewed by a subject matter expert before any regulatory decision

License

This model is released under Apache 2.0. Base model: Qwen2.5-0.5B-Instruct — Apache 2.0, Alibaba Cloud.