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h3rb3rn/
moe-expert-governance-4b

Name: moe-expert-governance-4b
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⚖️ MoE Sovereign Governance Expert 4B (`moe-expert-governance-4b`)

Policy Reasoning Engine over Authoritative Regulatory Evidence (EU-GDPR, AI Act, BSI IT-Grundschutz)

License: Apache 2.0

Base Model: Qwen 3.5 4B Hybrid Mamba

Trained on: LUMI-G Supercomputer

📌 Executive Summary & Architectural Role

moe-expert-governance-4b is a specialized 4-billion parameter Small Language Model (SLM), LoRA fine-tuned on the LUMI-G Supercomputer (8× AMD Instinct™ MI250X GCDs (4× physical modules, 64GB HBM2e per GCD)).

Within the MoE Sovereign compound AI architecture, this model serves as the Regulatory Policy Reasoning & Privacy-by-Design Expert. Rather than acting as an autonomous legal decider, it assists in evidence-grounded compliance analysis against versioned regulatory and policy sources (EU GDPR/DSGVO, EU AI Act, BSI IT-Grundschutz, ISO 27001, HIPAA). It evaluates data flows, assesses system boundaries, and synthesizes structured compliance audit trails based on verified statutory documents supplied by the knowledge infrastructure.

🎯 Functional Scope & Capabilities

EU-GDPR / DSGVO Technical Policy Auditing: Evaluates data minimization (Art. 5(1)(c)), purpose limitation, technical and organizational measures (TOMs, Art. 32), and DPIA risk factors.
EU AI Act Risk Classification: Categorizes AI workflows into risk tiers (Prohibited, High Risk, Specific Transparency, Minimal Risk) based on statutory definitions and annexes.
BSI IT-Grundschutz & ISO 27001 Control Mapping: Audits architecture components against standard security and confidentiality modules (e.g. INF.1, CON.2, OPS.1).
Structured Audit Trail Generation: Produces JSON/Markdown governance reports mapping data pipelines to statutory requirements.

🎯 Training Objectives & Intended Behavioral Specialization

Capability	Base Stock Qwen 3.5 4B	`moe-expert-governance-4b` (Distilled)
Legal Citation	Hallucinates fictitious GDPR sub-clauses or non-existent AI Act articles	Exact Statutory Alignment; cites authoritative articles, recitals, and annexes
Risk Categorization	Vague assertions ("This might be risky")	Rigorous Classification Trees (e.g. AI Act Annex III criteria with justification)
Technical Measures	Generic suggestions ("Use encryption")	Concrete TOMs (e.g. TLS 1.3, AES-256-GCM, pseudonymization pipelines, RBAC)
Auditing Clarity	Unstructured narrative essays	Auditable Matrix Formats (Statutory Requirement $\to$ Technical Control $\to$ Status)

📊 Empirical Evaluation (Held-Out Benchmark Suite)

ℹ️ Evaluation Status: Evaluated on held-out validation splits ($N=1,000$, zero training contamination). Full cross-architecture ablation suites across Compound AI vs. Monolithic LLMs are undergoing active execution in the Sovereign Scientific Benchmark Suite v1.

Evaluated on a held-out benchmark suite of 1,000 regulatory compliance and architectural audit scenarios with zero training contamination:

Evaluation Metric	Base Stock Qwen 3.5 4B	`moe-expert-governance-4b` (Distilled)	Delta ($\Delta$)
GDPR Article & Requirement Mapping Precision	66.2 %	96.3 %	+30.1 %
EU AI Act Risk Tier Classification Accuracy	58.7 %	94.8 %	+36.1 %
BSI IT-Grundschutz Control Coverage	51.4 %	92.5 %	+41.1 %
Privacy-by-Design Gap Detection	62.0 %	95.2 %	+33.2 %
Structured Compliance Matrix Formatting	71.8 %	98.4 %	+26.6 %
Hallucinated Legal Citation Rate	18.5 %	1.8 %	-16.7 %

Note: Evaluated at temperature=0.05 across 3 independent seeds. Audits were scored against gold-standard legal compliance matrices prepared by privacy and cybersecurity engineers.

🏋️ Training Setup

+-----------------------------------------------------------------------------------+
|                          LUMI-G LORA FINE-TUNING PIPELINE                         |
|  [ Student: Qwen3.5-4B Hybrid Linear Attention + Mamba Base ]                     |
|                       |                                                           |
|                       v  (LoRA r=16, alpha=32, target_modules: q/k/v/o/gate/up/down)|
|  [ Output: final_adapter -> CPU-BF16 Merge -> GGUF Q4_K_M & Q8_0 ]                |
+-----------------------------------------------------------------------------------+

Hyperparameters:

Compute Cluster: LUMI-G (8× AMD Instinct MI250X 128GB GPUs)
Base Architecture: Qwen3.5-4B (Hybrid Linear Attention + Mamba in BF16)
Epochs: 3.0
Effective Batch Size: 128 (Micro-batch 4 × 8 GPUs × Gradient Accumulation 4)
Learning Rate: $1.5 \times 10^{-5}$ with Cosine Decay and Warmup
LoRA Configuration: $r=16$, $\alpha=32$, Dropout $0.05$, Target Modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj

⚠️ Known Limitations & Failure Modes

Not a Substitute for Legal Counsel: The model provides technical architectural auditing and policy alignment; it does not furnish formal legal advice or substitute for licensed legal counsel.
Jurisdiction-Specific Precedents: Highly localized case law (e.g. specific regional court rulings in individual German Bundesländer) should be supplemented via GraphRAG retrieval.
Dynamic Legislative Changes: New statutory updates enacted after training cutoff must be supplied via the MoE Sovereign regulatory knowledge base.

💻 Quickstart Guide (Ollama & Llama.cpp)

1. Ollama `Modelfile`

dockerfile

FROM ./moe-expert-governance-4b-Q4_K_M.gguf
PARAMETER num_ctx 262144
PARAMETER temperature 0.05
TEMPLATE """{{ if .System }}<|im_start|>system
{{ .System }}<|im_end|>
{{ end }}{{ if .Prompt }}<|im_start|>user
{{ .Prompt }}<|im_end|>
{{ end }}<|im_start|>assistant
{{ .Response }}<|im_end|>"""

2. Python Inference

python

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "h3rb3rn/moe-expert-governance-4b"

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

prompt = "<|im_start|>user\nEvaluate a proposed biometric access control AI system under the EU AI Act risk categories and list required compliance mandates.<|im_end|>\n<|im_start|>assistant\n"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.05)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

📑 Citation

bibtex

@misc{moe_sovereign_2026_governance4b,
  author = {Horn, Philipp and MoE Sovereign Core AI Team},
  title = {MoE Sovereign Governance Expert 4B: Regulatory Policy Reasoning SLM},
  year = {2026},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/h3rb3rn/moe-expert-governance-4b}},
  note = {Trained on the EuroHPC LUMI-G Supercomputer}
}

h3rb3rn/moe-expert-governance-4b

⚖️ MoE Sovereign Governance Expert 4B (moe-expert-governance-4b)