🌐 MoE Sovereign Omni Expert 4B (moe-expert-omni-4b)

Cross-Domain Architectural Synthesis, Interface Harmonization & Multi-Expert Reconciliation

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📌 Executive Summary & Architectural Role

moe-expert-omni-4b is a specialized 4-billion parameter Small Language Model (SLM) distilled from DeepSeek-V3 and Qwen2.5-72B-Instruct on the LUMI-G Supercomputer (8× AMD Instinct™ MI250X 128GB GPUs).

Within the MoE Sovereign compound AI architecture, this model serves strictly as the Architectural Synthesizer & Cross-Domain Interface Harmonizer. It is not a generalist model designed to replace domain specialists. Instead, its explicit design role is to take structured outputs from multiple specialized experts (Coder, Security, DataInfra, Governance, Precision, GraphRAG), resolve cross-domain interface mismatches, flag semantic tensions, and compile them into a unified, coherent system architecture.

  +------------------+    +-------------------+    +-------------------+
  | Coder Expert 4B  |    | Security Expert 4B|    |DataInfra Expert 4B|
  +--------+---------+    +---------+---------+    +---------+---------+
           |                        |                        |
           +------------------------+------------------------+
                                    |
                                    v
                 +--------------------------------------+
                 |      moe-expert-omni-4b (SLM)        |
                 |  - Reconciles Interface Contracts    |
                 |  - Flags Cross-Domain Contradictions |
                 |  - Compiles Unified System Arch      |
                 |  * NEVER invents missing domain facts|
                 +--------------------------------------+
                                    |
                                    v
                       [ Sovereign Judge 35B / 27B ]

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🎯 Functional Scope & Capabilities

1. Cross-Domain Interface Harmonization: Aligns REST/gRPC contracts, database schemas, security contexts, and regulatory tags into unified specification documents.
2. Structural Conflict Flagging: Detects semantic contradictions between expert proposals (e.g. Coder proposing in-memory caching while Governance mandates zero-persistence for PII).
3. End-to-End System Blueprinting: Produces clean architecture diagrams (Mermaid), API specifications (OpenAPI 3.1), and sequence flows combining multi-expert inputs.
4. Strict Epistemic Discipline: Prohibited from generating ungrounded domain claims; defers missing technical facts back to domain specialists.

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🎯 Training Objectives & Intended Behavioral Specialization

Capability	Base Stock Qwen 3.5 4B	moe-expert-omni-4b (Distilled)
Multi-Agent Merging	Concatenates or overwrites domain expert outputs arbitrarily	Harmonizes Contracts & Interfaces while preserving domain guarantees
Contradiction Handling	Ignores security/governance constraints to satisfy code prompt	Explicitly Flags Conflicts between security/legal policies and code
Domain Boundary	Tries to hallucinate complex SQL or Z3 proofs itself	Integrates Expert Solutions without inventing unverified domain facts
Architecture Output	High-level vague prose	Executable Blueprints (OpenAPI, Mermaid, Docker Compose topologies)

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📊 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 multi-expert synthesis and system integration tasks with zero training overlap:

Evaluation Metric	Base Stock Qwen 3.5 4B	moe-expert-omni-4b (Distilled)	Delta ($\Delta$)
API / Interface Contract Harmonization Rate	64.1 %	96.2 %	+32.1 %
Cross-Domain Conflict Detection (Recall)	52.8 %	94.5 %	+41.7 %
Architectural Coherence & Consistency	67.5 %	95.8 %	+28.3 %
Synthesized Mermaid Diagram Validity	71.0 %	98.7 %	+27.7 %
Epistemic Discipline (No Fact Hallucination)	60.3 %	96.1 %	+35.8 %
OpenAPI 3.1 Syntax & Schema Validity	68.2 %	97.4 %	+29.2 %

Note: Evaluated at temperature=0.1 across 3 independent seeds. Evaluated on multi-agent merge tasks combining 2 to 6 disparate domain expert outputs.

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🏋️ Training Setup & Distillation Methodology

+-----------------------------------------------------------------------------------+
|                            LUMI-G DISTILLATION PIPELINE                           |
|                                                                                   |
|  [ Teachers: DeepSeek-V3 + Qwen2.5-72B-Instruct ]                                 |
|                       |                                                           |
|                       v  (Multi-Domain Consistency & Schema Validation Filtering) |
|  [ SFT Dataset: 35,000 Multi-Expert Harmonization Trajectories ]                  |
|                       |                                                           |
|                       v  (DeepSpeed ZeRO-2, ROCm 7.0, PyTorch 2.6, 8x MI250X)     |
|  [ 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, Slurm Job #21189565)
• Base Architecture: Qwen3.5-4B (Hybrid Linear Attention + Mamba in BF16)
• Dataset Size: 35,000 multi-expert synthesis trajectories
• 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
• Training Loss (Final): 0.0075
• Token Accuracy (Final): 99.82 %

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⚠️ Known Limitations & Failure Modes

1. Not a Standalone Domain Specialist: Omni should not be called in isolation for deep mathematical proofs, raw kernel driver writing, or standalone regulatory audits without expert inputs.
2. Upstream Contradiction Resolution: When two expert outputs present an irreconcilable factual stalemate, Omni surfaces the dispute for the Sovereign Judge (Belnap-Dunn consensus) rather than making an arbitrary decision.
3. Context Length Handling: Trained for multi-turn context maintenance at context lengths up to 256k; for architectures involving more than 8 simultaneous expert tracks, two-pass hierarchical synthesis in the orchestrator is recommended.

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💻 Quickstart Guide (Ollama & Llama.cpp)

1. Ollama Modelfile

FROM ./moe-expert-omni-4b-Q4_K_M.gguf
PARAMETER num_ctx 262144
PARAMETER temperature 0.1
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

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "h3rb3rn/moe-expert-omni-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\nHarmonize the provided Coder REST API endpoints and Security RBAC policies into a unified OpenAPI 3.1 specification.<|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=0.1)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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📑 Citation

@misc{moe_sovereign_2026_omni4b,
  author = {Horn, Philipp and MoE Sovereign Core AI Team},
  title = {MoE Sovereign Omni Expert 4B: Cross-Domain Architectural Synthesis SLM},
  year = {2026},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/h3rb3rn/moe-expert-omni-4b}},
  note = {Trained on the EuroHPC LUMI-G Supercomputer}
}