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sallani
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Model Details
Model Provider
sallani
Model Tree
Qwen/Qwen2.5-0.5B-Instruct
Input Modalities
Output Modalities
Supported Functionality
| 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 |
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:
text
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 ?python
from transformers import AutoModelForCausalLM, AutoTokenizerimport 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))bash
ollama create iso42001-edge -f Modelfileollama run iso42001-edge "How to conduct an AI Impact Assessment per ISO 42001 A.6.1?"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 0.147 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:
| 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 |
This model is designed to run fully locally with no network calls at inference time.
This model is released under Apache 2.0.
Base model: Qwen2.5-0.5B-Instruct — Apache 2.0, Alibaba Cloud.
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 ?from transformers import AutoModelForCausalLM, AutoTokenizerimport 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 create iso42001-edge -f Modelfileollama run iso42001-edge "How to conduct an AI Impact Assessment per ISO 42001 A.6.1?"./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