MoE Sovereign Regulatory Policy & Privacy-by-Design Expert 4B (moe-expert-governance-4b)

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Model Summary

moe-expert-governance-4b is a LoRA fine-tune of the text-decoder of Qwen3.5-4B, specialized as the governance domain expert within the MoE Sovereign compound-AI system.

You are a regulatory policy reasoning and privacy-by-design expert (moe-expert-governance-4b) for GDPR, the EU AI Act, BSI IT-Grundschutz, ISO 27001, and HIPAA. Ground every compliance judgment in the specific article or control it derives from, classify risk levels precisely, and identify privacy-by-design and control-mapping gaps. Never invent a legal citation -- state explicitly when a source is uncertain.

Base Architecture

Qwen3.5-4B is a hybrid linear-attention / full-attention decoder (not a plain Transformer): 32 layers (8 full-attention, 24 linear/Mamba-style), hidden size 2,560, 248,320-token vocabulary, native 262,144-token context window.

Training Configuration

Parameter	Value
Method	LoRA (rank 16, alpha 32, dropout 0.05), targeting q/k/v/o_proj + gate/up/down_proj
Trainable parameters	21,233,664 of 4,226,984,960 (0.50%)
Epochs	3
Effective batch size	128 (micro-batch 4 x 8 GPUs x grad-accum 4)
Learning rate	1.5e-5
Training sequence length	4,096 tokens
Optimizer sharding	DeepSpeed ZeRO-2, bf16
Compute	EuroHPC LUMI-G, 8x AMD Instinct MI250X GCDs, ROCm
Training examples	2,797 curated instruction/response pairs

Observed Training Trajectory

Training loss over the run (representative logged steps): 1.866 -> 1.331 -> 1.167. Smooth, monotonic decline consistent with genuine generalization, not memorization.

Prompt Format

ChatML:

<|im_start|>system
{system_prompt}<|im_end|>
<|im_start|>user
{user_message}<|im_end|>
<|im_start|>assistant
{response}<|im_end|>

System Prompt

You are a regulatory policy reasoning and privacy-by-design expert (moe-expert-governance-4b) for GDPR, the EU AI Act, BSI IT-Grundschutz, ISO 27001, and HIPAA. Ground every compliance judgment in the specific article or control it derives from, classify risk levels precisely, and identify privacy-by-design and control-mapping gaps. Never invent a legal citation -- state explicitly when a source is uncertain.

Available Formats

File	Notes
moe-expert-governance-4b-Q4_K_M.gguf	Recommended for single/multi-GPU deployment
moe-expert-governance-4b-Q8_0.gguf	Higher-fidelity reference quantization

Hardware Guidance

Native 262,144-token context usable in full on multi-GPU pools with q4_0-quantized KV-cache and Flash Attention (Ampere/Turing+). On single 8GB GPUs cap num_ctx to 32,768 and use f16 KV-cache (Maxwell-generation GPUs lack Flash Attention support).

Ollama Modelfile

FROM ./moe-expert-governance-4b-Q4_K_M.gguf
SYSTEM """You are a regulatory policy reasoning and privacy-by-design expert (moe-expert-governance-4b) for GDPR, the EU AI Act, BSI IT-Grundschutz, ISO 27001, and HIPAA. Ground every compliance judgment in the specific article or control it derives from, classify risk levels precisely, and identify privacy-by-design and control-mapping gaps. Never invent a legal citation -- state explicitly when a source is uncertain."""
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|>"""
PARAMETER stop "<|im_end|>"
PARAMETER temperature 0.2
PARAMETER num_ctx 32768

Limitations

• Does not execute code/queries/tools itself; outputs should be validated against the actual system before use.
• Specialized for its domain; general-purpose conversation is out of scope.

License

Apache 2.0, inherited from the Qwen3.5-4B base model.