---
language:
- en
- de
license: apache-2.0
base_model: Qwen/Qwen3.8-27B
tags:
- compound-ai
- judge
- paraconsistent-consensus
- verification-oracle
- self-correction
- gguf
- lumi-g
- moe-sovereign
datasets:
- moe-sovereign/judge-verification-sft
pipeline_tag: text-generation
library_name: transformers
---

# ⚖️ MoE Sovereign Judge 27B (`sovereign-judge-27b`)
*Paraconsistent Consensus Oracle, Self-Correction Gatekeeper & Formal Output Verifier*

[![License: Apache 2.0](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
[![Base Model: Qwen 3.8 27B](https://img.shields.io/badge/Base_Model-Qwen3.8--27B-violet.svg)](https://huggingface.co/Qwen/Qwen3.8-27B)
[![Trained on: LUMI-G Supercomputer](https://img.shields.io/badge/Trained_on-LUMI--G_MI250X-green.svg)](https://www.lumi-supercomputer.eu/)

---

> ⚠️ **Note on the "Architecture: qwen35" GGUF sidebar tag:** Hugging Face reads this directly from the embedded `general.architecture` field of the uploaded GGUF files. `llama.cpp` assigns this identifier to the entire Qwen 3.5/3.6/3.8 hybrid linear-attention code family (Gated DeltaNet + Gated Attention) — it does **not** indicate a different or older base model. This checkpoint was converted from `Qwen/Qwen3.8-27B` (see `base_model` above and the Hyperparameters section below); the GGUF/llama.cpp architecture tag has simply not been split out per Qwen release yet.

---

## 📌 Executive Summary

**`sovereign-judge-27b`** is a high-capacity 27-billion parameter verification and evaluation model, 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 system, `sovereign-judge-27b` serves as the top-level **Quality Gatekeeper, Self-Correction Oracle, and Paraconsistent Consensus Arbitrator**. When 4B domain SLMs generate candidate solutions or when multi-agent debates produce conflicting propositions, `sovereign-judge-27b` evaluates formal consistency, detects logical contradictions, checks regulatory alignment, and decides whether an output passes the strict 66% consensus threshold or requires bounded self-correction.

---

## 🎯 Target Use Cases & Functional Scope

1. **Paraconsistent Consensus Arbitration:** Analyzes conflicting outputs from peer domain models, filtering out outliers and calculating calibrated consensus scores.
2. **Formal Self-Correction Triggering:** When an execution plan or code artifact fails validation gates, generates minimal, surgical correction directives for the Planner.
3. **Multi-Aspect Quality Scoring:** Evaluates candidate responses along 5 rigorous axes: Factual Grounding, Security Hardening, Syntactic Validity, Regulatory Compliance, and Efficiency.
4. **Correction Memory Ingestion:** Extracts detected failure patterns, abstracts the underlying anti-pattern, and formats new entries for persistent Correction Memory.

---

## 🔬 Behavioral Comparison: Stock Qwen 3.8 27B vs. Sovereign Judge

| Capability | Base Stock Qwen 3.8 27B | `sovereign-judge-27b` (Distilled) |
| :--- | :--- | :--- |
| **Evaluation Stance** | Lenient, sycophantic rating of AI outputs | **Strict, Adversarial Verification**; flags all logic flaws and subtle hallucinations |
| **Consensus Handling** | Simple majority vote or averaging | **Paraconsistent Logic Filter:** Detects contradictions without exploding the reasoning space |
| **Self-Correction** | Generates generic instructions to "try again" | **Surgical Failure Analysis:** Identifies the exact violated invariant and provides actionable remediation |
| **Memory Extraction** | No memory abstraction capabilities | **Automated Correction Memory Extraction:** Generalizes runtime errors into reusable patterns |

---

## 🏋️ Training Setup

```
+-----------------------------------------------------------------------------------+
|                          LUMI-G LORA FINE-TUNING PIPELINE                         |
|  [ Student: Qwen3.8-27B BF16 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:** Qwen3.8-27B in BF16
- **Epochs:** 3.0
- **Effective Batch Size:** 128 (Micro-batch 2 × 8 GPUs × Gradient Accumulation 8)
- **Learning Rate:** $1.0 \times 10^{-5}$ 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 Precision:** Pure BF16 with DeepSpeed ZeRO-2

---

## 💻 Quickstart Guide (Ollama & Llama.cpp)

### 1. Ollama `Modelfile`
```dockerfile
FROM ./sovereign-judge-27b-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/sovereign-judge-27b"

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 the following candidate DAG execution plan and determine whether it satisfies paraconsistent consensus and GDPR boundary rules.<|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_judge27b,
  author = {Horn, Philipp and MoE Sovereign Core AI Team},
  title = {MoE Sovereign Judge 27B: Paraconsistent Consensus & Self-Correction Oracle},
  year = {2026},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/h3rb3rn/sovereign-judge-27b}},
  note = {Trained on the EuroHPC LUMI-G Supercomputer}
}
```